Utilizing hyperspectral remote sensing technology enables the extraction of fine-grained land cover classes. Typically, satellite or airborne images used for training and testing are acquired from different regions or times, where the same class has significant spectral shifts in different scenes. In this paper, we propose a Bi-directional Domain Adaptation (BiDA) framework for cross-domain hyperspectral image (HSI) classification, which focuses on extracting both domain-invariant features and domain-specific information in the independent adaptive space, thereby enhancing the adaptability and separability to the target scene. In the proposed BiDA, a triple-branch transformer architecture (the source branch, target branch, and coupled branch) with semantic tokenizer is designed as the backbone. Specifically, the source branch and target branch independently learn the adaptive space of source and target domains, a Coupled Multi-head Cross-attention (CMCA) mechanism is developed in coupled branch for feature interaction and inter-domain correlation mining. Furthermore, a bi-directional distillation loss is designed to guide adaptive space learning using inter-domain correlation. Finally, we propose an Adaptive Reinforcement Strategy (ARS) to encourage the model to focus on specific generalized feature extraction within both source and target scenes in noise condition. Experimental results on cross-temporal/scene airborne and satellite datasets demonstrate that the proposed BiDA performs significantly better than some state-of-the-art domain adaptation approaches. In the cross-temporal tree species classification task, the proposed BiDA is more than 3%∼5% higher than the most advanced method. The codes will be available from the website: https://github.com/YuxiangZhang-BIT/IEEE TCSVT BiDA.

It is well known that complex topography can affect satellite observations, leading to substantial uncertainties in surface parameter estimation when topographic effects are ignored. However, most existing studies have focused on high-resolution satellite data (e.g., < 100 m resolution), while the impacts of topography on the moderate-resolution satellite data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) observation, product generation, and further applications have not been well explored. In this context, we investigated how topography-induced deviations propagate through moderate-resolution satellite observations, product generation, and downstream applications. We examined proxies such as top-of-atmosphere (TOA) reflectance, surface reflectance, land surface temperature (LST), leaf area index (LAI), and gross primary production (GPP), systematically analyzing their topographic effects across representative mountainous regions using multiple satellite datasets and radiative transfer models. Specifically, we conducted the following three tasks: (i) we utilized simultaneous observations from Geostationary Operational Environmental Satellite–16 (GOES-16) and GOES-17, which have differing viewing angles, to evaluate the topographic effects on geostationary satellite data; (ii) we analyzed MODIS-Terra and MODIS-Aqua data, with varying sun and viewing angles, to assess the impact of topography on polar-orbiting satellite products; and (iii) we employed radiative transfer models to gain theoretical insights into how topography influences satellite data across different terrain conditions. Our findings showed that topography induced an average deviation of 7.4% in near-infrared (NIR) band TOA reflectance in concurrent GOES-16 and GOES-17 observations. The surface reflectance and LST had similar deviation patterns as TOA reflectance. For NIR band surface reflectance, topographic effects lead to a maximum error of 0.37 in simulated data and an average of 16.7% deviation in MODIS-based evaluations. Furthermore, topographic impacts on LAI and GPP were found to average 36.0% and 10.4%, respectively, across four 1° × 1° mountainous regions globally. Long-term GPP trend analyses revealed uncertainties of 5.2% in the Alps and 3.8% in the Qinghai-Xizang Plateau, attributable to topographic effects. Our study demonstrates that topographic influences not only affect satellite observations but also propagate through to downstream applications for moderate-resolution data. By quantifying these effects, we underscore the importance of integrating topographic considerations into high-level satellite products over mountainous regions.

Downward shortwave radiation (DSR) is greatly affected by rugged terrains, which account for about 24% of the world’s surface. Yet, existing DSR products do not take into account topographical effects. Some topographic correction algorithms have been developed for estimating the clear-sky instantaneous DSR over rugged terrains (DSRins-rugged), but no specific algorithms are available to get the daily average DSR over rugged terrains (DSRdaily-rugged). The objective of this study is to develop an efficient and robust model to retrieve the clear-sky DSRdaily-rugged based on DSR satellite products. After examining ground measurements collected from several mountainous sites over the Chengde Experimental Area in China, we found that the clear-sky DSRins-rugged over a day follows a pseudo-sine curve, depending on aspect, slope, and other terrain factors, which form the foundation of our terrain correction sinusoidal model (TCSM). TCSM also includes a new simple shadow correction method. Validation against ground measurements showed that shadow-corrected clear sky TCSM DSRdaily-rugged estimated from in situ measurements is highly accurate with a root-mean-square error (RMSE) of 9.69 Wm−2, bias of 0.93 Wm−2, and$R^{2}$of 0.99. After applying TCSM to correct the topographic effects of both the Clouds and Earth’s Radiant Energy Systems synoptic Edition4 (CERES-SYN1deg_Ed4A) and MCD18A1 C6 (MCD18) DSR products, the accuracies significantly improved, with the validated RMSE reduced from 63.60 and 64.51 to 14.03 and 12.60 Wm−2, the bias from −38.58 and −36.93 to 5.53 and −7.17 Wm−2, and$R^{2}$from 0.46 and 0.44 to 0.97 and 0.98, respectively. Additionally, the TCSM can be easily applied to other DSR products that do not consider the topographic effects.

Downward shortwave radiation (DSR) is critical to many surface processes, and many satellite-derived DSR products have been released. Few studies have validated DSR over mountains where it is highly heterogeneous and so the shortwave flux measured at ground stations does not match kilometer-scale DSR products. To tackle this challenge, we used a high spatial resolution (30 m) daily DSR over Sierra Nevada, Spain for 2008–2015, and a mountainous radiative transfer model to explore how topographic effects impacted the performances of DSR products. Four widely-used satellite products were selected as proxies for our evaluation: (i) MCD18A1 V6.1 (with a spatial resolution of 1 km); (ii) MSG DSR (~ 3.3 km); (iii) GLASS DSR V42 (0.05°); and (iv) BESS DSR (0.05°). There are three main findings under clear skies. Firstly, the product accuracies were slope-dependent, decreasing by 59.8–134.6% with slope ≥ 25° compared to areas with slope < 10°. Secondly, the product accuracies were aspect-dependent, exhibiting a higher degree of overestimation (i.e., average of 27.6 W/m²) on the north side and underestimation (i.e., average of -1.3 W/m²) on the south side. Thirdly, and finally, the product accuracies were time-dependent, exhibiting seasonal variations and pronounced overestimation in summer (i.e., 8.8 to 18.2 W/m²). Moreover, the impact of topography decreased with increasing cloud cover. Our findings can be applied to various mountainous areas due to the same mechanism of how topography influences the DSR estimation. This study corroborates the substantial uncertainties of the current DSR products in mountains and the necessity of incorporating topographic information into DSR estimations.

Estimation of net shortwave radiation at the top-of-the atmosphere (Rns,TOA) at high spatial and temporal resolutions is essential for studying the Earth’s energy budget and its associated radiative forcing of natural or anthropogenic events on global or regional scales. Existing products typically use broadband sensors with coarse spatial resolution for the estimation. While narrowband sensors offer higher spatial resolution, they have a limited number of daily observations. Traditional estimation methods often necessitate atmospheric products as inputs, while inaccurate cloud and aerosol information can result in substantial estimation errors. Furthermore, geostationary satellites-based products are often developed for specific regions, with limited spatial coverage and varying accuracy due to the diverse range of satellites and algorithms used. To overcome these challenges and obtain globalRns,TOAwith improved spatiotemporal resolution and accuracy, a universal approach was proposed in this study to derive hourly 3-km globalRns,TOA, which takes the advantages from radiative transfer model, machine learning algorithm, and dense observations from five geostationary satellites. OurRns,TOAestimation shows reasonably good agreement with the Earth’s Radiant Energy System (CERES) product, with root mean square errors (RMSE) ranging from 53.43 W/m2to 75.67 W/m2and bias ranging from -12.78 W/m2to -2.01 W/m2on instantaneous scales, and the RMSE on daily scale improved by up to 6.7 W/m2compared to those of the sinusoidal-integrated values. Our generated 3-km dailyRns,TOAexhibits highly consistency of spatial pattern with 1° CERES product at multiple temporal conditions, while providing much more spatial details. Furthermore, we find the diurnal patterns at 3km resolution differ significantly from the sinusoidal patterns at 1°, exhibiting greater variability. The difference in daytimeRns,TOAestimation between the two reaches up to 86W/m2. This significant difference reflects the unreliability of relying solely on sinusoidal pattern and the necessity of high frequency observations to estimate daily values at high spatial resolution. However, increasing the observation frequency beyond a certain point (120-min) yields only limited improvements in the accuracy of daily radiation estimates. Overall, the algorithms proposed in this study are reliable, and can be easily applied to any satellite equipped with MSG (Meteosat Second Generation)-like or more bands. This study demonstrates the feasibility of jointly using multiple geostationary satellites for,Rns,TOAestimation with high spatial and temporal resolutions.

Land surface temperature (LST) data acquired from satellites are used extensively in studying climate variability. Many researchers have used Moderate Resolution Imaging Spectroradiometer (MODIS) LST to detect the temperature trend, however, its reliability has not been fully investigated. Using in-situ data acquired from 67 stations worldwide, this study examined the reliability of the detected temperature trends and investigated the associated influencing factors. The high-quality MODIS data have an RMSE of 2.44 K and 3.70 K at nighttime and daytime, respectively. However, its trend detection had an RMSE of 0.81 K/decade and 0.98 K/decade at nighttime and daytime, respectively. Clear-sky bias, quality control, LST estimation uncertainties, trend magnitude, and length of time were factors that influenced the detected trends. Filling cloud-covered areas in MODIS data may effectively reduce biases in trend detection.

Data assimilation has been demonstrated as the potential crop yield estimation approach. Accurate quantification of model and observation errors is the key to determining the success of a data assimilation system. However, the crop growth model error is not fully taken into account in most of the previous studies. The objective of this study is to better quantify the model uncertainty in the data assimilation system. Firstly, we calibrated a crop growth model and inferred its posterior uncertainty based on the Global LAnd Surface Satellite (GLASS) 250-m LAI product, regional statistical data, station observations, and field measurements with a Markov chain Monte Carlo (MCMC) method. Secondly, the model posterior uncertainty was used in the Ensemble Kalman Filter (EnKF) algorithm to better characterize the ensemble distribution of model errors. Our results indicated the proposed Bayesian posterior-based EnKF can improve the accuracy of winter wheat yield estimation at both the point scale (the coefficient of determination R2value increasing from 0.06 to 0.41, the mean absolute percentage error MAPE value decreasing from 12.65% to 7.82%, and the root mean square error RMSE value decreasing from 987 to 688 kg∙ha-1) and the regional scale (R2value from 0.30 to 0.57, MAPE value from 19.67% to 10.13%, and RMSE value from 1275 to 695 kg∙ha-1) compared with the open-loop estimation. Our analysis also indicated that the Bayesian posterior-based EnKF can perform better compared to the standard Gaussian perturbation-based EnKF. The proposed framework provides an important reference for crop yield estimation at the regional scale in similar agricultural landscapes worldwide.

Chlorophyll is of great physiological and ecological significance. Leaf and canopy chlorophyll contents can be retrieved from remotely sensed data based on vegetation indices (VIs). However, the impacts of canopy structure and soil remain open problems. VIs are typically calculated from spectral reflectance. In this study, we also constructed and examined VIs based on canopy scattering coefficients (Wλ) from spectral invariants theory. Based on extensive leaf and canopy radiative transfer simulations, linear regression and artificial neural network models were built with reflectance-based andWλ-based VIs to retrieve leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). The results showed that the canopy structure and soil significantly affected the retrievals.Wλcan effectively suppress the impacts of the leaf angle distribution (LAD) but not the leaf area index (LAI). TheWλ, estimated as the ratio reflectance/directional area scattering factor (DASF), contained a large error when the soil effect was strong. TheWλ-based VIs did not yield very accurate results in LCC estimation but exhibited higher accuracy for CCC estimation compared to reflectance-based VIs. Of all the VIs investigated, the best VI was D99 ((R850-R710)/(R850-R680)) for LCC and Wmul (W749×W956) for CCC. Compared to D99 for LCC, Wmul for CCC was less accurate, and the accuracy varied more among canopies with different LADs. The main reason was that CCC equals LCC multiplied by LAI, but LCC and LAI impact VIs in a similar manner.

Top-of-atmosphere (TOA) albedo is a significant factor for Earth energy budget, climate change and environmental change. As tremendous regional and global changes happened over ocean, more details for the ocean monitoring need to be provided. However, there were still no high-spatial resolution TOA albedo products over ocean. In this study, all-sky TOA albedo estimation over ocean was proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Instead of building angular dependent models, direct retrieval models between TOA reflectance and TOA albedo was developed based on extensive radiative transfer simulations under clear sky and cloudy sky, covering thousands of ocean and atmosphere types. Three-Component Ocean Water Albedo model was involved to take account for the ocean surface anisotropy at different wind speed, wind direction and chlorophyll conditions while Modtran was utilized to simulate different atmospheric conditions. Our results showed good agreement with the Clouds and the Earth's Radiant Energy System based on a global comparison on August, 4, 2011, with RMSE = 0.055 under cloudy sky and RMSE = 0.015 under clear sky. MODIS-based products provide more spatial details due to higher spatial resolution, which will be a good data source for regional environmental and climatic research.

As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ($R_{n}$) drives many physical and biological processes. Remote estimation of$R_{n}$using satellite data is an effective approach to monitor the spatial and temporal dynamics of$R_{n}$. Accurate daily$R_{n}$estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily$R_{n}$products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtain$R_{n}$data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km$R_{n}$, which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation ($R_{ns}$). Then, another RF model was developed to estimate the daily$R_{n}$from$R_{ns}$, incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the process of$R_{ns}$estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky$R_{ns}$. Benefiting from high spatio-temporal resolutions, our daily$R_{ns}$estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth’s Radiant Energy System (CERES) product. Using accurate daily$R_{ns}$estimates and LRD as inputs, the EHM model shows reasonably good results for estimating$R_{n}$($R^{2}$, RMSE, and bias of 0.91, 20.95 W/m2, and −0.05 W/m2, respectively). Maps of 1-km$R_{ns}$and$R_{n}$exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed$R_{n}$retrieval scheme can accurately estimate all-sky 1-km$R_{ns}$and$R_{n}$at mid- to low-latitudes and can be easily adapted and applied to other GOES- 16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimating$R_{n}$using geostationary satellites with improved accuracy and resolutions.

The asymptotic radiative transfer (ART) model has been widely used in snow remote sensing. However, the anisotropic effects of snow reflectance challenge this model because of its underestimation in the forward-scattering direction. To exhibit these strong scattering properties of the snow surface, a microfacet specular kernel has been supplemented with the ART model (hereinafter named the ARTS model). In this study, we propose a method of multiplying by a correction term for improving the ART model (hereinafter named the ARTF model). We validate the performance of the ARTF model using various data sources. Our results demonstrate that: 1) the ARTF model has higher accuracy in characterizing snow bidirectional signatures, with$R^{2}$and root mean square error (RMSE) values in the ranges from 0.722 to 0.990 and 0.007 to 0.041, respectively, than the ART ($R^{2} =0.507$–0.802 and RMSE = 0.038–0.088) and ARTS ($R^{2} =0.686$–0.962 and RMSE = 0.021–0.044) models, especially in the long-wave near-infrared region and 2) the ARTF model can effectively represent snow hyperspectral reflectance, while the ART and ARTS models significantly underestimate snow reflectance in the visible and shortwave near-infrared region. The$R^{2}$values of these three models reach ~0.99, and the RMSE values of the ARTF model range from 0.012 to 0.024, which are smaller than those of the ART (RMSE = 0.021–0.061) and ARTS (RMSE = 0.021–0.049) models. These results demonstrate that the ARTF model is better than the ART and ARTS models for characterizing snow hyperspectral bidirectional reflectance.

Radiometric calibration of the medium-resolution satellite data is critical for monitoring and quantifying changes in the Earth’s environment and resources. Many medium-resolution satellite sensors have irregular revisits and, sometimes, have a large difference in illumination viewing geometry compared with a reference sensor, posing a great challenge for routine cross-calibration practices. To overcome these issues, this study proposed a cross-calibration method to calibrate medium-resolution multispectral data. The Chinese Gaofen-4 (GF-4) panchromatic and multispectral sensor (PMS) data with large viewing angles were used as the test data, and Landsat-8 operational land imager (OLI) data were used as the reference data. A bidirectional reflectance distribution function (BRDF) correction method was proposed to eliminate the effects of differences in illumination viewing geometry between GF-4 and Landsat-8. The validation using concurrent image shows that the mean relative error (MRE) of cross calibration is less than 6.65%. Validation using ground measurements shows that our calibration results have an improvement of around 14.8% compared with the official released calibration coefficients. The time series cross calibration reveals that, without the requirements of simultaneous nadir observations (SNOs), our calibration activities can be carried out more often in practice. Gradual and continuous radiometric sensor degradation is identified with the monthly updated calibration coefficients, demonstrating the reliability and importance of the timely cross calibration. Besides, the cross-calibration approach does not rely on any specific calibration site, and the difference in illumination viewing geometry can be well considered. Thus, it can be easily adapted and applied to other optical satellite data.

Surface albedo plays a key role in global climate modeling as a factor controlling the energy budget. Satellite observations were utilized to estimate surface albedo at global and regional scales with good precision over flat areas. However, because topography greatly complicates radiative transfer (RT) processes, estimating the albedo of rugged terrain with satellite data remains a challenge. In addition, albedo definitions over sloping terrain differ from that for flat areas. They include horizontal/horizontal sloped surface albedo (HHSA) and inclined/inclined sloped surface albedo (IISA). Methods for retrieving HHSA and IISA in mountains have not been well-explored. Here, we retrieved HHSA and IISA on sloping terrain from Landsat 8 using a direct estimation algorithm. We simulated a dataset of Landsat top-of-atmosphere (TOA) reflectance and surface albedo with discrete anisotropic radiative transfer (DART) model, for variable atmospheric, vegetation, soil, and topography properties. Then, we used artificial neural networks (ANNs) to derive an empirical relationship between TOA reflectance and surface albedo. The accuracy of our method was verified within situmeasurements: root mean squared error (RMSE) and bias equal to 0.029 and −0.010 for HHSA, and 0.023 and −0.001 for IISA, respectively. Several albedo results (HHSA, IISA, values without topographic consideration) were evaluated and compared. HHSA was found similar to albedo without topographic consideration, but IISA, considered as the “true albedo” for sloping terrain, showed large difference from them. This study demonstrated the feasibility of surface albedo estimation from Landsat TOA reflectance directly in rugged terrains and advanced our understanding of energy budget in mountains.

Generating spatially and temporally consistent biophysical products at the global scale from Landsat data for monitoring and assessing surface change dynamics remains a challenge. This article presents an inversion framework called Land continuous Variable Estimator (LoVE)–Landsat for estimating a group of spatiotemporal continuous land surface variables with daily temporal resolution from Landsat 5, 7, and 8 top-of-atmosphere (TOA) data. LoVE–Landsat adopts a data assimilation approach originally developed for coarse-resolution satellite data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Major improvements to the approach include constructing empirical dynamic equations based on MODIS retrievals and other ancillary information, developing an artificial neural networks (ANN)-based emulator of the coupled radiative transfer (RT) model of atmosphere and land surface (vegetation, soil, and snow) as the observation operator, and designing a hybrid four-dimensional variational (4DVar) and ensemble Kalman filter (EnKF) data assimilation algorithm. The approach starts with generating the initial cloud-free regularly distributed (every 16 days) time series of Landsat data. The 4DVar is then used to assimilate clear-sky snow-free Landsat TOA observations over one year into the empirical dynamic evolution models of the land surface variables (e.g., leaf area index—LAI). The EnKF is then used to further adjust the state vector at the actual Landsat acquisition times. After determining a core set of variables (e.g., LAI), other variables, such as broadband albedo, emissivity, and fraction of absorbed photosynthetically active radiation (FAPAR), are calculated by the coupled RT model. Several experimental cases are presented to demonstrate that the proposed LoVE–Landsat framework is effective to estimate daily land surface variables.

Top-of-atmosphere (TOA) albedo is a significant factor of earth energy budget, climate change, and environmental change. As tremendous regional and global changes are happening over ocean, more details are needed to monitor the ocean environment. However, there were still no high-spatial resolution TOA albedo products over ocean. In this study, a new algorithm for clear-sky TOA albedo estimation over ocean was proposed, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Instead of building angular distribution models, direct retrieval models between TOA reflectance and TOA albedo were developed based on extensive radiative transfer (RT) simulations, covering thousands of ocean and atmosphere types. Three-component ocean water albedo model was involved to take account for the ocean surface anisotropy at different wind speed, wind direction, and chlorophyll concentration, while Modtran 5 was utilized to simulate different atmospheric conditions. Our results showed good agreement with the Clouds and the Earth’s Radiant Energy System (CERES) based on a global comparison on August 4, 2011, with RMSE = 0.015 and bias = 0.002. And our MODIS-based products provide more spatial details due to higher spatial resolution (1 km), which will be a good data source for regional environmental and climatic research and will also enhance the understanding of Earth’s radiation budget.

Optical sensors are subject to cloud obscuration and sunlight dependence, resulting in large proportions of missing snow cover information. Microwave sensors are a good alternative to snow cover monitoring in all weather conditions. Thus far, few studies in the literature have directly derived the fractional snow cover (FSC) from passive microwave data, and none have considered the relationship between FSC and brightness temperature (TB). This study first explores the FSC–TB relationship with a radiation transfer model, exhibiting that no generic function can properly describe the nonlinear and complex FSC–TB relationship. Therefore, a new algorithm based on machine learning method was designed to improve FSC retrieval from TB data, considering other auxiliary information, including soil property, land surface, and geography information. Benchmarked against the Moderate Resolution Imaging Spectroradiometer (MODIS) reference FSC, our FSC retrieval model performed well with an average correlation coefficient of 0.70, the mean absolute error ranging from 0.15 to 0.17, and the root-mean-square error ranging from 0.19 to 0.21. The generated FSC maps reasonably characterized the seasonal dynamics and spatial distribution patterns of snow cover; time series analysis with three AmeriFlux stations observation indicated effective capture of snowpack evolution process by the generated FSC. In addition, the verification of snow mapping capability using snow depth measurements from 13 521 stations indicates that it was relatively stable with overall accuracy greater than 0.88. For precise monitoring of snow cover extent in all weather conditions, particularly for subpixel snow cover areas, the development of the FSC estimation scheme with TB data should be extensively encouraged and implemented.

Surface downward shortwave radiation (DSR) is a key parameter in Earth’s surface radiation budget. Many satellite products have been developed, but their accuracies need further improvements. This study proposed an innovative deep learning method that combines radiative-transfer (RT) modeling with convolutional neural network (CNN) learning for estimating instantaneous DSR from VIIRS observations. Unlike traditional CNN methods that rely on spatial contextual information and are not optimal for medium to coarse resolution satellite data, the proposed algorithm takes advantage of both spectral information as well as vertical information. The algorithm firstly estimates the atmospheric effective optical depth from TOA and surface reflectance by using the look-up table created by radiative transfer simulations. We then constructed a spectral-wised virtual matrix to train the CNN using surface DSR measurements at 34 Baseline Surface Radiation Network sites globally during 2013. The developed CNN was also compared with four traditional machine learning algorithms. The validation results showed that the root mean square error (RMSE) and the bias were 91.42 W/m2and -0.94 W/m2respectively. This research is the first spectral-wised CNN application to estimate surface biophysical parameters from satellite remote sensing data quantitively. The comparison with previous look-up table and optimization-based algorithms shows that the proposed algorithm outperforms by around 10~20 W/m2We also explored how transfer learning can further improve the DSR estimation. Our results indicate that the universal model with local data transfer learning outperforms either the CNN with local data or the universal CNN by around 10~20 W/m2.

Surface incident shortwave radiation (ISR) is an important component of the surface radiation budget. We refined the optimization method developed for polar-orbiting satellite data[1]and applied it to estimate ISR from the new generation geostationary Advanced Himawari Imager (AHI) onboard the Himawari-8/9 satellite and Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R Series. Validation of the AHI ISR estimation at 2-km resolution showed an$R^{2}$of 0.93, bias of 0.52 W/m2, and RMSE of 106.52 W/m2for instantaneous estimates; an$R^{2}$of 0.95, bias of −0.12 W/m2, and RMSE of 22.49 W/m2for daily mean ISR; and a bias of −0.18 W/m2and RMSE of 7.72 W/m2for monthly mean ISR. Validation of the ABI ISR at 2-km spatial resolution showed an$R^{2}$value of 0.93, bias of 8.71 W/m2, and RMSE of 102.30 W/m2for instantaneous estimates; an$R^{2}$of 0.95, bias of −2.38 W/m2, and RMSE of 27.17 W/m2for daily mean ISR; and a bias of 1.40 W/m2and RMSE of 14.75 W/m2for monthly mean ISR. Our study also demonstrated that AHI and ABI observations have realized much better estimations for hourly and diurnal ISR than previous polar-orbiting satellite data because of their higher frequency of sampling on the atmospheric conditions.

The medium-resolution spectral imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth’s environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear-and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination ($R^{2}$) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m2, 0.092, 0.084, and 115.305 W/m2, respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations.

Since a new generation of geostationary satellite data has incredibly high temporal, spatial, and spectral resolutions, new methodologies are now needed to take advantage of both the temporal and spectral signatures of them for accurate estimation of Earth's environmental variables. This article describes a novel optimization method to estimate a suite of 11 physically consistent land surface and atmospheric variables under all-sky conditions from the geostationary advanced Himawari imager (AHI) top-of-atmosphere (TOA) observations. This method is based on a coupled soil, snow, vegetation, and atmospheric radiative transfer (RT) model from 0.28 to 14 μm. The inversion algorithm consists of three major steps. First, the “clearest” observations at each moment during a temporal window were determined and then the essential variables that characterize surface RT models, such as leaf area index (LAI), leaf chlorophyll concentration, and soil parameters were estimated. Second, the atmospheric variables, including aerosol optical depth (AOD) under clear-sky conditions, and cloud optical thickness (COT) and cloud effective particle radius (CER) under cloudy-sky conditions, were inverted given surface reflectance calculated by the surface RT models. Finally, the inverted atmospheric and land surface variables were fed into the coupled RT model to calculate the remaining set of variables, including spectral directional reflectance, surface broadband albedo, thermal emissivity, incident shortwave radiation (ISR), photosynthetically active radiation (PAR), fraction of absorbed PAR by green vegetation (FAPAR), and TOA shortwave albedo. The retrieved variables were validated using in-situ measurements from Ozflux network sites and compared with the other existing satellite products. Intercomparisons demonstrate that the AHI-retrieved atmospheric variables (AOD, CER, and COT) and surface variables (surface reflectance, LAI, FAPAR, PAR, and surface emissivity) are well correlated with the corresponding JAXA released AHI, NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES), and the Global LAnd Surface Satellite (GLASS) products. Direct validation using in-situ measurements indicates that the retrieved ISR achieves higher accuracy than the CERES ISR product (with R2values of 0.95 and 0.89, and root-mean-square error (RMSE) of 20.3 and 29.6 W/ m2for the AHI-retrieved and CERES daily ISR, respectively). Validation also shows that the estimated daily surface albedo has an accuracy comparable to the MODIS daily albedo product (RMSE = 0.03). Both the direct validation and product comparisons have demonstrated that this proposed inversion framework works very well for the AHI data. Unlike other algorithms that are usually used for estimating an individual parameter and rely heavily on a separate atmospheric correction, this inversion framework can effectively estimate a group of atmospheric and land surface variables and be easily applied to other similar multispectral geostationary satellite data. A comprehensive sensitivity and validation study is still needed to quantify the uncertainties of the retrieval variables.

Dynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.

The ocean surface net radiation ( Rn) characterizing ocean surface radiation budget is a key variable in ocean climate modeling and analysis. In this study, a downscaling scheme was developed to generate a high-resolution (0.05°) time-series (2002-2013) daily ocean surface Rnfrom the third-generation Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO3) at 0.25° based on the Advanced Very-High-Resolution Radiometer (AVHRR) top-of-atmosphere (TOA) observations (AVH021C) and other ancillary information (Clearness Index and cloud mask). This downscaling scheme includes the statistical downscaling models and the residual correction post-processing. A series of angle-dependent downscaling statistical models were established between the daily ocean surface Rnin J-OFURO3 and the AVHRR TOA data, and then, the residual correction was conducted to the model estimates Rn_AVHRR_est to obtain the final downscaled data set Rn_AVHRR. Validation against the measurements from 57 moored buoy sites in six ocean observing networks shows the high accuracy of the downscaled estimates Rn_AVHRR_est with a R2of 0.88, RMSE of 23.44 W·m-2, and bias of -0.14 W·m-2under all-sky condition. The results of the spatio-temporal analysis in Rn_AVHRR and intercomparison with Cloud and the Earth's Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) products also indicated that the superior of the Rn_AVHRR with more detailed information especially in the hot spot regions, such as central tropical Pacific (warming pool), Atlantic and Equatorial Eastern Indian Ocean (EIO).

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ($R^{2}$) of 0.77–0.84 ($p < 0.01$) and root-mean-square error (RMSE) values of 17.9–21.5 W/m2among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for$R^{2}$and decreased by 4.2–8.3 W/m2for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

Parameterization schemes for estimating clear-sky surface downward longwave radiation (SDLR) are well recognized for their simplicity and acceptable accuracy, especially at the local scale. The near-surface temperature and/or water vapor are usually used to predict the clear-sky SDLR in a parameterization scheme. Air temperature inversion (ATI) alters the atmospheric state at the near-surface boundary layer and affects the accuracy of the clear-sky SDLR estimation. However, few studies have investigated the impact of ATI on the estimate of the clear-sky SDLR. This article investigated the impact of ATI on the estimate of the clear-sky SDLR using six widely used parameterization schemes. According to the evaluation results using ATI profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database and the Surface Radiation Budget Network (SURFRAD) sites, all the parameterization schemes are sensitive to ATI, and their accuracy is degraded greatly as a whole. The SDLR is underestimated for the ATI profile both in the TIGR database and SURFRAD sites. The best three schemes can achieve the accuracy with bias values of approximately -10 W/m2and root-mean-square errors (RMSEs) less than 20 W/m2for the ATI profiles in the TIGR database. The reason the SDLR is underestimated for the ATI profiles is provided by a simulation study. An empirical method is proposed to correct the impact of ATI. The accuracy of all the parameterization schemes is remarkably improved at SURFRAD sites after correcting the impact of ATI, with the absolute values of bias and RMSEs less than 10 and 20 W/m2at SURFRAD sites.

New generation geostationary satellite data exhibit high temporal, spectral, and spatial resolutions. This fact calls for new methodologies for accurate estimation of multiple land surface and atmospheric parameters. In this study, geostationary Advanced Himawari Imager (AHI) top-of-atmosphere (TOA) observations are used to simultaneously estimate a suit of physically consistent land surface and atmospheric parameters using the optimization method with a coupled soil, snow, canopy, and atmosphere radiation transfer model from 0.28 μm to 14 μm under all-sky conditions. Preliminary results of the algorithm by comparisons with the current remote sensing product and the field measurements were presented.

Top-of-atmosphere (TOA) albedo is important for studying Earth's energy budget and climate dynamics. As no direct validation can be conducted to determine their accuracies, comprehensive intercomparisons are of significance for robust applications. In this study, five TOA albedo products over land surfaces have been intercompared, including three global TOA albedo products from Advanced Very High Resolution Radiometer (AVHRR) (TAL-AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) (TAL-MODIS) and Clouds and the Earth's Radiant Energy System (CERES), the regional product from the Climate Monitoring Satellite Application Facility (CM SAF), and the harmonized product called Diagnosing Earth's Energy Pathways in the Climate system (DEEP-C). Results show that there is good consistence among the five products overall, especially after the year 2000. The differences among these products in the high-latitude regions are relatively larger (~0.2). The differences among TAL-AVHRR, TAL-MODIS and CERES are mostly less than 0.05 while the differences between TAL-AVHRR and DEEP-C before 2000 can be up to 0.1.

The daytime surface net radiation (Rn) product from version 4.0 Global LAnd Surface Satellite (GLASS) product suite was recently generated from Moderate Resolution Imaging Spectroradiometer data. It is the daytime average product of Rnderived from 2000 to 2015 at a spatial resolution of 0.05°. This letter describes the results of validation of this new Rn product using ground measurements collected from 142 sites distributed worldwide. The overall accuracy of the GLASS daytime Rnproduct was satisfactory, with an R2of 0.80, root-mean-square error of 51.35 Wm-2, and mean bias error of 0.11 Wm-2. Its accuracy and quality were highly consistent for different land cover classes and elevation zones.

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth's surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.

Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth's radiation budget and the fundamental quantities of energy available at the earth's surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003-2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m2and a root mean square error of 28.83 (32.84) W/m2at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m2with a standard deviation of 0.8 W/m2over a 13-year (2003-2015) period.

Surface albedo is one of the essential climate variables. There is an increasing need for albedo data to be available for use in applications that require a medium to fine spatial resolution. In our earlier study, the direct estimation approach, previously used with coarser resolution data, was refined and applied to the Landsat data archive, including MSS, TM, ETM+, and OLI. Extensive validations made against ground measurements showed that the albedo estimation algorithm could achieve low root-mean-squared-errors (RMSEs) not more than 0.031 over both snow-free and snow-covered surfaces. In this study, the algorithm was used with Landsat data to map the surface albedo changes in the ablation zone over west Greenland since 1980s, where massive melting events occurred during the past few decades. As a case study, an analysis of surface albedo change over Greenland combining four satellite albedo datasets, namely MODIS, GLASS, CLARA, and Landsat was conducted to better understand the magnitude and timing of albedo changes in the ablation zone.

Land surface albedo (LSA) is an important parameter charactering the land surface energy balance. The prevailing LSA products have supplied a well understanding of the global weather change but for regional use, it is difficult to capture the patch-size change induced by human activities. In this paper, we estimate the high resolution LSA from GF-1 WFV data based on a direct estimation algorithm. Results compared with field observation indicate that the estimation accuracy is high with the coefficient of determination of 0.705 and Bias of 0.008. When compared with Landsat LSA data, a high consistency is performed, the coefficients of determination for black and white sky albedo are 0.943 and 0.941 respectively.

Traditional methods for estimating land-surface parameters from remotely sensed data generally focus on a single parameter with a specific spectral region, resulting in physical and spatiotemporal inconsistencies in current satellite products. We recently proposed a unified inversion scheme to estimate a suite of parameters simultaneously from both visible and near-infrared and thermal-infrared MODIS data. In this letter, we implemented this scheme to estimate six time-series parameters [leaf area index, fraction of absorbed photosynthetically active radiation, surface albedo, land-surface emissivity, land-surface temperature (LST), and upwelling longwave radiation (LWUP)] from the Visible Infrared Imaging Radiometer Suite (VIIRS) data. Several components of these schemes are refined, including the incorporation of a snow bidirectional reflectance distribution function model, determination of the best band combination, and better estimation of the snow-covered surface emissivity by accounting for the snow-cover fraction. Validation using the measurements at 12 sites of SURFRAD, CarboEuropeIP, and FLUXNET, and intercomparisons with MODIS and Global Land-Surface Satellite products, are carried out: the retrieved albedo, LST, and LWUP achieved accuracies (R€) of 0.77, 0.96, and 0.95, root mean square errors of 0.06, 2.9 K, and 18.3 W/m2, and biases of 0.01, 0.09 K, and -0.08 W/m2, respectively. The retrieved parameters can achieve comparable or higher accuracy than existing products, which indicates that the unified algorithm can be applied effectively to the VIIRS data with high physical and temporal consistency and accuracy.

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in many climate and ecological models. The accuracy of satellite FAPAR products directly influences estimates of ecosystem productivity and carbon stocks. The targeted accuracy of FAPAR products is 10% or 0.05 for many applications. However, most current FAPAR products do not meet such requirements, and further improvements are still needed. In this paper, a data fusion scheme based on the multiple resolution tree (MRT) approach is developed to integrate multiple satellite FAPAR estimates at site and regional scales. MRT was chosen because of the superior computational efficiency compared with other fusion methods. The fusion scheme removed the bias in FAPAR estimates and resulted in a 15% increase in the R2and 3% reduction in the root-mean-square error compared with the average of individual FAPAR estimates. The regional-scale fusion filled in the missing values, and provided spatially consistent FAPAR distributions at different resolutions. Overall, MRT can be used to efficiently and accurately generate spatially and temporally continuous FAPAR data across both site and regional scales.

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input parameter in many climate and ecological models. Long time series of global FAPAR products are required for many applications, such as vegetation productivity, carbon budget calculations, and global change studies. Three long time series of global FAPAR products have been existing since the 1980s: Global LAnd Surface Satellite (GLASS) Advanced Very High Resolution Radiometer (AVHRR), National Centers for Environmental Information (NCEI) AVHRR, and third-generation Global Inventory Monitoring and Modeling System (GIMMS3g). Currently, no intercomparison studies exist that have evaluated these FAPAR products to understand their differences for effective applications. In this paper, these three long time series of global FAPAR products are first intercompared to evaluate their spatial and temporal consistencies, and then compared with FAPAR values derived from high-resolution reference maps of VAlidation of Land European Remote sensing Instruments sites. Our results demonstrate that the GLASS AVHRR FAPAR product is spatially complete, whereas the NCEI AVHRR and GIMMS3g FAPAR products contain many missing pixels, especially in rainforest regions and in middle- and high-latitude zones of the Northern Hemisphere. The GLASS AVHRR, NCEI AVHRR, and GIMMS3g FAPAR products are generally consistent in their spatial patterns. However, a relatively large discrepancy among these FAPAR products is observed in tropical forest regions and around 55°N-65°N. In latitudes between 15°N and 25°N, the mean GIMMS3g FAPAR values are clearly larger than the mean GLASS AVHRR and NCEI AVHRR FAPAR values during July-October each year. The GLASS AVHRR FAPAR product provides smooth FAPAR temporal profiles, whereas the NCEI AVHRR and GIMMS3g FAPAR products showed fluctuating trajectories, especially during the growing seasons. All three FAPAR products show high agreement coefficients (ACs) in vegetation regions with obvious seasonal variations and low ACs in tropical forest regions and sparsely vegetated areas. A comparison of these FAPAR products with the FAPAR values derived from high-resolution reference maps demonstrates that the GLASS AVHRR FAPAR product has the best performance [root mean square deviation (RMSD) = 0.0819 and bias = 0.0043], followed by the NCEI AVHRR FAPAR product (RMSD = 0.1061 and bias = 0.0371), and then finally, the GIMMS3g FAPAR product (RMSD = 0.1152 and bias = 0.0248).

Land surface albedo is a critical variable used in many climate and environmental applications. The multispectral Advanced Baseline Imager (ABI) onboard the next generation geostationary satellites (GOES-R series, first launched in Nov. 2016) offers high temporal and medium spatial resolution observations, which can be used for monitoring diurnal variation of surface albedo. In this paper, we applied an optimization method to derive hourly surface albedo from geostationary satellite observations on a daily-basis. Data from the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 satellite was used as a proxy for algorithm development, which has spectral bands and spatial resolutions similar to ABI. Validations against ground measurements and other satellite albedo products showed promising results for the surface albedo estimates, which can satisfy the accuracy requirements for downstream applications, particularly for the Environmental Monitoring Center at NOAA.

After over two decade of efforts, many land products are now being produced systematically from a variety of satellite data, and these products have been widely used. However, estimating a set of atmospheric and surface variables from one sensor data is often an ill-posed inversion problem, because the number of unknowns is often larger than the available bands[1]. Thus, one has to make assumptions while trying to obtain realistic solutions, and as a result, most products still need significant improvements of quality and accuracy. Although the average accuracy may be acceptable, the error of each product can be very large under certain conditions. Furthermore, different products of land variables from different inversion algorithms are physically inconsistent for most cases. Many products in the current form are not suitable for climate study because the products are not continuous both spatially and temporally due to factors such as clouds. There is an urgent need to develop more advanced new inversion methods and produce more accurate products. We have recently proposed a data assimilation approach to estimate an improved suite of products from one or multiple satellite data. The general idea is to use the surface and atmospheric radiation models with parameters that are adjusted to optimally reproduce the spectral radiance received by the EOS sensors. Such adjustments are usually made by identifying reasonably close “first guesses” for the model parameters and determining statistically optimum estimates of the parameters by giving appropriate weights to the first guesses versus addition to the error increments needed to get agreement with the observations. The first guesses are the multiple years MODIS/MISR land product climatologies. The best estimate at present time is a climatological value corrected by some combinations of previous time's departure from climatology weighted using temporal autocorrelation and what it takes to fit present observations. The presentation will review this approach and also introduce three case studies[2-4]. Case one [3] estimated only leaf area index (LAI) by integrating temporal, spectral, and angular information from Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT/VEGETATION, and Multi-angle Imaging Spectroradiometer (MISR) data based on an ensemble Kalman filter (EnKF) technique. Validation results at six sites demonstrate that the combination of temporal information from multiple sensors, spectral information provided by red and near-infrared (NIR) bands, and angular information from MISR bidirectional reflectance factor (BRF) data can provide a more accurate estimate of LAI than previously available. Case two [2] estimated temporally complete land-surface parameter profiles from MODIS time-series reflectance data also based on the EnKF technique. The products include LAI, the fraction of absorbed photosynthetically active radiation (FAPAR) and surface broadband albedo. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112. spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively. Case three [4] further estimated multiple land surface parameters and aerosol optical depth (AOD) from MODIS top-of-atmosphere (TOA) reflectance data without relying on atmospheric correction. Soil, vegetation canopy, and atmospheric radiative transfer models were coupled. LAI and AOD were estimated first and the coupled model then calculated land surface reflectance, incident photosynthetically active radiation (PAR), land surface albedo, and the FAPAR. The flowchart is shown in Fig. 1. The retrieved land surface parameters and AOD were compared with the corresponding MODIS, Global Land Surface Satellite (GLASS), GEOV1, and MISR products and validated by ground measurements from seven sites with different vegetation types. The results demonstrated that the new inversion method can effectively produce multiple physically consistent parameters with accuracy comparable to that of existing satellite products over the select sites (Figure 2).

Land surface emissivity, Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) are critical parameters for studying the energy and water balance between the atmosphere and land surfaces. Due to the lack of reliable observations, a constant emissivity value or very simple parameterizations are adopted in land surface modeling. Retrieval of time-series dynamic surface emissivity is a solution to split window technique which is used to estimate LST. By exploiting both the reflective and emissive bands observations from VIIRS sensor, this study estimated the time-series LST, emissivity and longwave radiation based on a unified radiative transfer model, and presented the preliminary results of the algorithm by comparisons with the current VIIRS LST product and the field measurements.

Visible Infrared Imaging Radiometer Suite (VIIRS) land surface albedo (LSA) product is one of the routinely generated VIIRS environmental data records (EDR). A direct estimation approach based on radiative transfer simulation was developed to directly estimate instantaneous LSA value from VIIRS clear-sky top-of-atmosphere (TOA) reflectance. Comprehensive validations and evaluations were performed by comparing VIIRS LSA retrieval with ground measurements, MODIS 16-day mean albedo product, and Landsat high-resolution albedo estimation. Recently, an algorithm framework was also developed to produce a new gridded and temporal-filtered VIIRS LSA product, which is expected to provide spatially and temporally continuous LSA map with improved accuracy, and facilitate the analysis and application of global LSA data.

The spring snow-albedo feedback (SAF) has been found to be positively correlated with summer drying in the United States in climate change simulations. However, whether this relationship exists in real climate is unclear. In this letter, we explored the relationship between spring SAF and summer drying with the help of satellite observations. It was found that a positive correlation between spring SAF and summer drying existed from 1982 to 2013. There was a negative interannual correlation between spring SAF strength and summer soil moisture (SM) ($r < -0.35$) and a positive interannual correlation between spring SAF and land surface temperature ($T_{s}$) in summer ($r > 0.35$) throughout dry regions in western North America, Europe, and central Asia. Furthermore, the strength of the snow-cover component ($- 0.67 \,\pm \, 0.06{\%}\,\cdot \,\text {K}^{-1}$, effect of$T_{s}$on land surface albedo ($a_{s}$) over surfaces transitioning from snow-covered to snow-free conditions) was about twice the magnitude of the metamorphosis component ($- 0.31 \pm 0.07{\%}\cdot \text {K}^{-1}$, effect of$T_{s}$on$a_{s}$over snow-covered surfaces) during the spring, which explained the majority of spring SAF strength over the Northern Hemisphere (NH) snow-covered landmass during 1982–2013. Meanwhile, the sensitivity of summer SM and$T_{s}$to changes in the snow-cover component rather than the metamorphosis component dominated the relationship between spring SAF and summer drying over the NH. This was the first attempt to provide observational evidence for the sensitivity of summer drying to spring SAF over the NH.

The availability of multiangular information from the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument provides an excellent opportunity for the characterization of surface anisotropy, which can be used for improving surface albedo estimation. However, the MISR data have been reported with large uncertainties and data gaps due to inaccurate aerosol estimation and/or cloud masking limiting its otherwise broader applications. To mitigate these issues, two approaches were proposed to estimate land surface albedo directly from surface reflectance (LSA_sfc) and Top-of-Atmosphere reflectance (LSA_toa), respectively. As a further development of the traditional albedo algorithms, this is the first attempt to simultaneously utilize multispectral and multiangular information in surface albedo estimation without any prior constraining information. Validations at AmeriFlux sites show that the proposed algorithms can achieve accuracies similar to that of the MISR product with respective bias and RMSE of 0.004 and 0.032 for LSA_sfc and 0.005 and 0.032 for LSA_toa algorithms. We found that the LSA_toa algorithm can significantly reduce data gaps and provide accurate surface albedo retrievals with two to three times more valid data than the current MISR product. In addition, these approaches can be easily applied to other optical sensors to produce accurate and gap-free clear-sky surface albedo estimations. The results of this paper also highlight the importance of having two to three simultaneous observations with sufficient angular sampling, which can improve albedo accuracy and reduce data gaps.

Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface broadband albedo are three routinely generated land-surface parameters from satellite observations, which have been widely used in land-surface modeling and environmental monitoring. Currently, most global land products are retrieved separately from individual satellite data. Many issues, such as data gaps, spatial and temporal inconsistencies, and insufficient accuracy under certain conditions resulting from the inadequacies of single-sensor observations, have made the incorporation of multiple sensors a reasonable solution. In this paper, an approach to simultaneous estimation of LAI, broadband albedo, and FAPAR from multiple-satellite sensors is further refined. The method, improved from that proposed in an earlier study using Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of several steps. First, a coupled dynamic and radiative-transfer model based on MODIS, SPOT/VEGETATION, and Multiangle Imaging SpectroRadiometer data was developed to retrieve LAI values and use them to construct a time-evolving dynamic model. Second, an iteration process with predefined exit criteria was developed to obtain consistent gap-filled LAI estimates. Third, a spectral albedo based on the retrieved LAI values was simulated using a radiative-transfer model and then converted to a broadband albedo using empirical methods. Snow-covered pixels identified by normalized difference snow index thresholds were adjusted to the weighted average of the underlying albedo and the maximum snow albedo. Finally, the FAPAR of green vegetation was calculated as a combination of the albedo at the top of the canopy, the soil albedo, and the transmittance of the PAR down to the background. Validation of retrieved LAI, albedo, and FAPAR values obtained from multiple-satellite data over ten study sites has demonstrated that the proposed method can produce more accurate products than presently distributed global products.

Most methods for generating global land surface products from satellite data are parameter specific and do not use multiple temporal observations, which often results in spatial and temporal discontinuity and physical inconsistency among different products. This paper proposes a data assimilation (DA) scheme to simultaneously estimate five land surface parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) time series reflectance data under clear and cloudy conditions. A coupled land surface-atmosphere radiative transfer model is developed to simulate TOA reflectance, and an ensemble Kalman filter technique is used to retrieve the most influential surface parameters of the coupled model, such as leaf area index, by combining predictions from dynamic models and the MODIS TOA reflectance data whether under clear or cloudy conditions. Then, the retrieved surface parameters are input to the coupled model to calculate four other parameters: 1) land surface reflectance; 2) incident photosynthetically active radiation (PAR); 3) land surface albedo; and 4) the fraction of absorbed PAR (FAPAR). The estimated parameters are compared with those of the corresponding MODIS, the Global LAnd Surface Satellite, and the Geoland2/BioPar version 1 (GEOV1) products. Validation of the estimated parameters against ground measurements from several sites with different vegetation types demonstrates that this method can estimate temporally complete land surface parameter profiles from MODIS TOA time series reflectance data, with accuracy comparable to that of existing satellite products over the selected sites. The retrieved leaf area index profiles are smoother than the existing satellite products, and unlike the MOD09GA product, the retrieved surface reflectance values do not have the high peak values influenced by clouds. The use of the coupled land surface-atmosphere model and the DA technique ensures physical connections between the land surface parameters and makes it possible to calculate radiation-related parameters for clear and cloudy atmospheric conditions, which is an improvement for FAPAR retrieval compared with the MODIS and GEOV1 products. The retrieved FAPAR and PAR values can reveal the significant differences in them under clear and cloudy atmospheric conditions.

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.

High-spatial resolution data of the top-ofatmosphere (TOA) reflected shortwave radiation flux are needed to understand the effects of local-scale anthropogenic and natural processes on earth's radiation budget. A previous study developed an algorithm to estimate the TOA instantaneous shortwave component from polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) data. This paper presents a temporal scaling approach to predict daily values of TOA reflected shortwave radiation flux from temporally sparse MODIS observations. Radiative transfer simulation and statistical regression are used to establish the relationship between daily shortwave flux and MODIS spectral reflectance. A comparison between the Terra and Aqua combined MODIS computed data and the Clouds and the Earth's Radiant Energy System SYN1deg product of 1° regional daily shortwave fluxes have a bias of 3.8 W/m2with a root-mean-square error (RMSE) of 13.3 W/m2using data from 2009 over eight subsets across various latitudes. Comparing the regional monthly shortwave fluxes reduces the RMSE to 6.9 W/m2. The longer the averaging period the lower the uncertainty is self-explanatory.

Unlike instantaneous albedo, daily albedo of land surfaces is currently not routinely generated from satellite data, although it is a key input parameter for calculating daily shortwave radiation budget. This paper presents a novel approach to directly retrieve daily mean values of land surface broadband blue-sky albedo from Visible Infrared Imaging Radiometer Suite clear-sky data of apparent reflectance, with the assumption that the atmospheric conditions of the satellite overpass time can represent their daily values. Training data were simulated by atmospheric radiative transfer models, with surface spectra and bidirectional reflectance distribution function data as inputs for four aerosol types and a range of aerosol loadings. Sensitivity analysis was conducted to study the effects of cloud coverage, aerosol, and surface types on retrieval accuracy. Two years of measurements at six Surface Radiation Budget Network and eight Greenland Climate Network stations were used for algorithm validation. Daily albedo of snow-free surfaces can be retrieved with very high accuracy. By excluding far off-nadir observations of snow surfaces, the overall accuracy of retrieving daily albedo has a bias of 0.003 and a root-mean-square error of 0.055.

Surface albedo has been widely used in studying energy budgets and climate dynamics in the Arctic region. Previous efforts have focused on using reanalysis albedo data, but their uncertainties remain unknown. In this letter, we evaluated four popularly used reanalysis surface albedo products, namely, the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR), and the Japanese 55-Year Reanalysis (JRA-55), over the Arctic Ocean using satellite-retrieved product (CLARA-SAL) from 1982 to 2009. Owing to the flawed parameterization scheme or problematic model inputs, reanalysis products are unable to capture both the interannual variation and long-term reduction of surface albedo in the Arctic. This results in a large bias in the decline of shortwave radiative forcing at both surface (from -11.74 to -38.25 W m-2) and top of atmosphere (from -5.35 to -20.19 W m-2). The most significant underestimation occurred in the melt season and after sea-ice melting acceleration started since 1996, in the central Arctic Basin north of 80° N, which is likely due to the failure in simulating the influence of thinning ice and decreasing snow depth. The JRA-55 albedo product outperformed the other three products, which is likely due to the employment of observed sea-ice concentration on the parameterization scheme. On the other hand, the other three reanalysis products, namely, ERA-Interim, MERRA, and CFSR, are unable to effectively track the interannual variation of surface albedo and significantly underestimate (from -0.016 to -0.021 relative to -0.048, by one-third to half) the decreasing surface albedo.

Surface upwelling longwave radiation (LWUP) is a vital component in calculating the Earth's surface radiation budget. Under the general framework of the hybrid method, we developed linear and dynamic learning neural network (DLNN) models for estimating the global 1-km instantaneous clear-sky LWUP from the top-of-atmosphere radiance of Moderate Resolution Imaging Spectroradiometer thermal infrared channels 29, 31, and 32. Extensive radiative transfer simulations were conducted to produce a large number of representative samples, from which the linear model and DLNN model were derived. These two hybrid models were evaluated using ground measurements collected at 19 sites from three networks (SURFRAD, ASRCOP, and GAME-AAN). According to the validation results, the linear model was more accurate than the DLNN model, with a bias and root-mean-square error (RMSE) of -0.31 W/m2and 19.92 W/m2obtained by averaging the mean bias and RMSE for the three networks. Additionally, the computational efficiency of the linear model was much higher than that of the DLNN model. We also compared our linear model to a hybrid method developed by a previous study and found ours to perform better.

Current satellite broadband emissivity (BBE) products do not correctly characterize the seasonal variation of vegetation abundance. This paper proposes a new method to estimate the BBE of vegetated surfaces to better describe the seasonal variation of vegetation abundance. The method takes advantage of the radiative transfer models' ability to calculate multiple scattering with a physical basis and uses the 4SAIL model to construct a lookup table (LUT) of BBE for vegetated surfaces. The BBE of the vegetated surface was derived from the LUT using three inputs: leaf BBE, soil BBE, and leaf area index (LAI). The validation results show that the accuracy of the new method exceeds 0.005 over fully vegetated surfaces. As a case study, this method was applied to data from 2003 to generate global vegetated surface BBE products for that year. An analysis of the results indicated that the derived BBE can correctly reflect seasonal variations in vegetation abundance that the data converted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS spectral emissivity products have been unable to reveal. The new method was also compared to the vegetation cover method (VCM). The VCM can correctly characterize seasonal variations in vegetation abundance. However, the classification of bare soil and vegetation in the VCM may produce step discontinuity in the calculated BBE. The new method is being implemented to produce a new version of the Global LAnd Surface Satellite (GLASS) BBE product over vegetated surfaces.

Ocean water albedo (OWA) plays an important role in the global climate variation. Compared with the achievements in land surface albedo studies, the global distributions of ocean water and sea ice albedo are seldom addressed. This study designed an operational global OWA algorithm based on the three-component reflectance model of the ocean water: sun glint, whitecaps, and water-leaving reflectance. The related achievements in these three areas are reviewed and integrated into the operational algorithm. After the sensitive analysis, the algorithm is compared with previous studies and validated with ground observations at COVE site located 25 km east of Virginia Beach (36.91° N, 75.71° W), and the results indicate that the proposed algorithm is generally consistent with previous parameterization scheme. As an example, the global OWAs in summer and winter 2011 are generated using the remote sensing reflectance data sets via the Moderate Resolution Imaging Spectroradiometer and Modern-Era Retrospective analysis for Research and Applications meteorological reanalysis data set. The generated product includes instantaneous (e.g., local noon) and daily mean OWAs under both clear-sky and white-sky conditions. Upon the examples, the local noon clear-sky OWA shows a significant latitude variation due to the dominance of the solar angle, whereas the white-sky OWA is sensitive to wind speeds and optical constituents. The global distribution of the daily mean OWA exhibits a similar trend to the local noon OWA. However, the daily mean clear-sky OWA is significantly larger than the local noon OWA; this finding should be noted when using OWA products for energy balance research. Additionally, all forms of OWA products exhibit increase in coastal areas with high input of terrestrial matters.

Fractional vegetation cover (FVC) is an important parameter for describing the conditions of land surface vegetation and is widely used for Earth surface process simulations and global change studies. Regional FVC is primarily derived from remotely sensed data. However, current FVC estimation methods are mainly employed on remotely sensed data at a single time point, which can only reflect the instantaneous physical state of the land surface and ignore the important information from the vegetation growing characteristics. The vegetation growing characteristics have great potential to capture the temporal variations of FVC and, thus, can provide complementary information to improve the FVC estimation accuracy. In this paper, a dynamic Bayesian network method was proposed to estimate FVC from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data through combining a radiative transfer model and a statistical crop growth model, which could synthetically use information from both remote sensing data and crop growing characteristics. The performance of the proposed method was investigated in a cropland area of the Heihe River Basin, covering the whole growing season of maize in 2012. The time series field FVC data were quantitatively measured using digital photography and then used to generate high-spatial-resolution FVC maps using the Advanced Spaceborne Thermal Emission and Reflection Radiometer and Compact Airborne Imaging Spectrometer data for evaluating the accuracy of FVC estimates from MODIS data. The validation results showed a satisfactory performance with a coefficient of determination R2of 0.956 and a root-mean-square error (RMSE) of 0.057, as compared with the performance ( R2= 0.817, RMSE = 0.106 ) of the FVC estimates using the lookup table method, which utilized the information from remote sensing data. These results indicated that the proposed method could effectively utilize the vegetation growth information and achieve reliable FVC estimates in the cropland area.

Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981-1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000-2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps (RMSE = 0.7848 and R2= 0.8095) than the GEOV1 LAI values (RMSE = 0.9084 and R2= 0.7939) and the MOD15 LAI values (RMSE = 1.1173 and R2= 0.6705).

Incident solar radiation (Rs) over the Earth's surface plays an important role in determining the Earth's climate and environment. Generally, Rscan be obtained from direct measurements, remotely sensed data, or reanalysis and general circulation model (GCM) data. Each type of product has advantages and limitations: the surface direct measurements provide accurate but sparse spatial coverage, whereas other global products may have large uncertainties. Ground measurements have been normally used for validation and occasionally calibration, but transforming their “true values” spatially to improve the satellite products is still a new and challenging topic. In this paper, an improved thin-plate smoothing spline approach is presented to locally “calibrate” the Global LAnd Surface Satellite (GLASS) Rsproduct using the reconstructed Rsdata from surface meteorological measurements. The influence of surface elevation on Rsestimation was also considered in the proposed method. The point-based surface reconstructed Rswas used as the response variable, and the GLASS Rsproduct and the surface elevation data at the corresponding locations as explanatory variables to train the thin-plate spline model. We evaluated the performance of the approach using the cross-validation method at both daily and monthly time scales over China. We also validated the estimated Rsbased on the thin-plate spline method using independent ground measurements and independent satellite estimates of Rs. These validation results indicated that the thin-plate smoothing spline method can be effectively used for calibrating satellite-derived Rsproducts using ground measurements to achieve better accuracy.

A weak-constraint variational data assimilation (WC-VDA) scheme was developed to estimate turbulent heat fluxes by assimilating sequences of land surface temperature measurements. In contrast to the commonly used strong-constraint VDA system, the WC-VDA approach accounts for the effects of structural and model errors and generates better results. This is achieved by adding a model error term ($\omega$) to the surface energy balance equation. The WC-VDA model was tested at two sites with very distinct hydrological and vegetated conditions: the Daman site (a wet site located in an oasis area and covered by seeded corn) and the Huazhaizi site (a dry site located in a desert area and covered by sparse grass). The two sites represent typical desert–oasis landscapes in the middle reaches of the Heihe River Basin, northwestern China. The results proved that the WC-VDA method performed well over very dry and wet conditions, and the estimated sensible and latent heat fluxes agree well with eddy covariance measurements.

Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method (R2=0.809, RMSE =0.157) was comparable with that of the GEOV1 FVC product (R2=0.775, RMSE =0.166), which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Surface shortwave net radiation (SSNR) is a key component of the surface radiation budget. In this paper, we refined a direct estimation approach to retrieve daily SSNR estimates from combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. The retrieved MODIS SSNR estimates were validated against measurements at seven stations of the Surface Radiation Budget Network. We also compared the MODIS retrievals with three existing SSNR products: the Clouds and the Earth's Radiant Energy System (CERES) products, the North American Regional Reanalysis (NARR) data, and the ERA-Interim reanalysis data from the European Centre for Medium-Range Weather Forecasts. MODIS data at 1 km were upscaled to mitigate the mismatch between site measurements and satellite retrievals. Among the four data sets, the aggregated MODIS retrievals agreed best with in situ measurements, with a root-mean-square error (rmse) of 23.1 W/m2and a negative bias of 6.7 W/m2. The CERES products have a slightly larger rmse of 24.2 W/m2and a positive bias of 7.6 W/m2. Both reanalysis data (NARR and ERA-Interim) overestimate daily SSNR and have much larger uncertainties. Monthly satellite SSNR data are more accurate than daily values, and the scaling issue in validating monthly MODIS SSNR retrievals is also less prominent. Averaged with a window size of 23 km, the two MODIS sensors can estimate monthly SSNR with an rmse error of 11.6 W/m2, representing an improvement of 2.4 W/m2over the CERES products.

Currently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.

Land surface albedo is a key input parameter required in the current general circulation models (GCMs). The accurate validation of albedo products is very important for use in various applications by the scientific community. The most common validation methods compare motely sensed albedo products directly with ground-based observed broadband albedo values without considering the band width inconsistency and the narrow-to-broad band transformation error of these products. Unlike previous validation approaches, we propose a spectral albedo direct validation method. By applying the novel instrument of multi-band albedometer, it is possible to obtain MODIS visible and near-infrared bands albedo measurements. The in-field measured data combined with a 16-day interval is compared directly with MODIS narrow band albedo products. The results indicate that the multi-band albedometer measurement and the MODIS narrow band white and black sky albedo has a good consistency. It is an efficient way to get rid of the errors induced by the band conversion and is a labor-saving technique of making possible time series measurements.

Directionality is ignored in the satellite retrieval of surface thermal-infrared emissivity, which will unavoidably affect the estimates of surface broadband emissivity and surface longwave net radiation. The purpose of this work is to quantify the effects of emissivity directionality. First, three types of emissivity data are used to calculate hemispherical emissivity and the difference between directional broadband emissivity and hemispherical broadband emissivity. The emissivity directionality is highly significant, and the directional emissivity decreases with increasing view angles. A view angle within 45° -60° can be found whose directional emissivity is highly close to the hemispherical emissivity, and the difference between the calculated directional and hemispherical broadband emissivity is zero. The difference between the atmospheric downward radiation and blackbody radiation at surface temperature is then determined by extensive simulations. Finally, the error ranges of surface longwave net radiation are presented. If the sensor scan angle is within ±55°, the error can reach as high as 17.48 and 14.05 W/m2for water and bare ice, respectively; the error is less than 2.74 W/m2for snow with different radii; the error can reach 4.11 W/m2for sun crust; the error is less than 5.14 W/m2for minerals, sand, slime and gravel; and clay has the smallest error at 1.02 W/m2.

Radiometric sensor calibration is critical for quantitative use of the data obtained from the FeungYun-3A MEdium-Resolution Spectral Imager (MERSI) sensor. To meet the required calibration criteria, several vicarious calibration (VC) techniques are being employed in the current operational calibration of MERSI. This letter presents the independent results of sensor degradation assessment which were obtained directly from sequences of top-of-atmosphere (TOA) reflectance from calibration sites. The absolute calibration approaches and the desert tracking approach used in the current operational process involve a radiative transfer model (RTM) when simulating the reference TOA reflectance. However, collecting synchronous in situ measurements for an RTM is usually expensive and time consuming, thus greatly limiting the frequency of such calibration processes. The direct approach developed in this study is an efficient and complementary VC technique that can increase the reliability of VC results in concert with other techniques. TOA reflectance measurements from three pseudoinvariant calibration sites are used to estimate sensor degradation rates. The results are compared with those obtained by other operational methods such as the multisite and the deep convective cloud tracking methods. The direct approach is consistent across the three calibration sites, producing degradation estimates comparable to those of the two other operational methods, suggesting the effectiveness of the direct approach for the MERSI radiometric sensor calibration.

Maps of high-resolution surface shortwave net radiation (SSNR) are important for resolving differences in the surface energy budget at the ecosystem level. The maps can also bridge the gap between existing coarse-resolution SSNR products and point-based field measurements. This study presents a modified hybrid method to estimate both instantaneous and daily SSNR from Landsat data. SSNR values are directly linked to Landsat top-of-atmosphere reflectance by extensive radiative transfer simulation. Regression coefficients are pre-calculated and stored in a look-up table (LUT). Atmospheric water vapor is a key parameter affecting SSNR, and three methods of treating water vapor are evaluated in this study. Comparison between Landsat retrievals and field measurements at six AmeriFlux sites shows that the hybrid method with water vapor as a dimension of LUT can estimate SSNR with a root mean square error of 77.5 W/m2(instantaneous) and 36.1 W/m2(daily). The method of water vapor correction produces similar results. However, a generic LUT that covers all levels of water vapor results in much larger errors.

Land surface albedo is a key factor in climate change and land surface modeling studies, which affects the surface radiation budget. Many satellite albedo products have been generated during the last several decades. However, due to the problems resulting from the sensor characteristics (spectral bands, spatial and temporal resolutions, etc.) and/or the retrieving procedures, surface albedo estimations from different satellite sensors are inconsistent and often contain gaps, which limit their applications. Many approaches have been developed to generate the complete albedo data set; however, most of them suffer from either the persistent systematic bias of relying on only one data set or the problem of subpixel heterogeneity. In this paper, a data fusion method is prototyped using multiresolution tree (MRT) models to develop spatially and temporally continuous albedo maps from different satellite albedo/reflectance data sets. Data from the Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus are used as examples, at a study area in the north central United States mostly covered by crop, grass, and forest, from June to September 2005. Results show that the MRT data fusion method is capable of integrating the three satellite data sets at different spatial resolutions to fill the gaps and to reduce the inconsistencies between different products. The validation results indicate that the uncertainties of the three satellite products have been reduced significantly through the data fusion procedure. Further efforts are needed to evaluate and improve the current algorithm over other locations, time periods, and land cover types.

The monitoring of top-of-atmosphere (TOA) reflectance time series provides useful information regarding the long-term degradation of satellite sensors. For a precise assessment of sensor degradation, the TOA reflectance time series is usually corrected for surface and atmospheric anisotropy by using bidirectional reflectance models so that the angular effects do not compromise the trend estimates. However, the models sometimes fail to correct the angular effects, particularly for spectral bands that exhibit a large seasonal oscillation due to atmospheric variability. This paper investigates the use of time series algorithms to identify both the angular effects and the atmospheric variability simultaneously in the time domain using their periodical patterns within the time series. Two nonstationary time series algorithms were tested with the Landsat 5 Thematic Mapper time series data acquired over two pseudoinvariant desert sites, the Sonoran and Libyan Deserts, to compute a precise long-term trend of the time series by removing the seasonal variability. The trending results of the time series algorithms were compared to those of the original TOA reflectance time series and those normalized by a widely used bidirectional-reflectance-distribution-function model. The time series results showed an effective removal of seasonal oscillation, caused by angular and atmospheric effects, producing trending results that have a higher statistical significance than other approaches.

Land surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. The polarization and directionality of the Earth's reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is${\sim}{0.009}$for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly.

Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China.

Land surface albedo is one of the key parameters in land surface modeling and climate change studies. In the past several decades, global surface albedo datasets have been developed from multiple satellite sensors. However, existing albedo products suffer from several problems, such as cloud contamination, algorithm limitation, and sensor failure, which may bring gaps or reduced accuracy for climate modeling applications. A novel approach was proposed in this paper by fusing multiple satellite albedo products across different spatial scales to reduce gaps and improve consistency. To implement the prototype algorithm, three satellite albedo products from the Multi-angle Imaging Spectro-Radiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat were used to generate consistent albedo datasets at different spatial resolutions simultaneously.

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a critical input parameter in many climate and ecological models. An accuracy of ±0.1 in FPAR is considered acceptable in the applications. However, most of current FPAR products, such as Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR), do not fulfill the accuracy requirement yet. The objective is to develop a new radiative transfer model for FPAR estimation, with broadened surface reflectance database from the time series of twelve years' reflectance data. The model proposed here could successfully identify growing season and the time series curve of estimated FPAR was smooth over years. The R2between estimated FPAR and in situ measurements was improved compared to existing FPAR products.

The surface downward shortwave radiation (250~3000 nm), also known as insolation, is referred to as total solar irradiance incident at Earth surface, which is an essential parameter in land surface radiation budget and many land surface process models. Currently, the downward shortwave radiation is obtained either from satellite observations based on empirical and physical-based retrieval methods or geostatistical methods using ground-based measurements. Both data type of remote sensing product convey substantial information: the ground-based measurements provide hard (accurate) but scare data, whereas, the radiation images (estimated from remotely sensed data or provided in reanalysis and GCMs data) provide exhaustive but soft (vague) information. In this paper we present a novel approach which takes advantages of both hard and soft data to estimate the surface downward shortwave radiation. This method, which based on a realistic representation of the spatiotemporal domain, can combine rigorously and efficiently various forms of physical knowledge and sources of uncertainty. Cross-validation results using ground measurements indicate that surface downward shortwave radiation estimates from BME are slightly improved.

In recent years, time series remote sensing data products have been assimilated into the coupled crop growth model and the radiative transfer model to improve the time series LAI estimation. However, due to the large number of input parameters to the crop growth model, the applications of the crop growth models for regional use is restricted. This paper proposed a data-based mechanistic assimilation method for estimation of the time series LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) data. By coupling a revised universal data-based mechanistic model (LAI_UDBM) with a vegetation canopy radiative transfer model (PROSAIL), The proposed method applies the Ensemble Kalman Filter (ENKF) method to improve the estimation accuracy. Results indicate that the time series LAI estimated by this approach is superior to the MODIS LAI. Furthermore, because the model does not require the historical observation of every pixel, it is applicable over a wider range of uses.

Surface broadband emissivity (BBE) in the thermal infrared spectrum is essential for calculating the surface total longwave net radiation in land surface models. However, almost all narrowband emissivities estimated from satellite observations are in the 3-14-μm spectral region. Previous studies converted these narrowband emissivities to BBE over different spectral ranges, such as 3-14, 8-12, 8-13.5, and 8-14 μm . Errors in the calculated total longwave net radiation must be quantified systematically using these BBEs. Moreover, the best spectral range for longwave net radiation must be determined. The key to addressing these issues is the use of the realistic emissivity spectra. By applying modern radiative transfer tools, we derived the emissivity spectra of water, snow, and minerals at 1-200 μm . Using these emissivity spectra, we first investigated the accuracy of replacing all-wavelength surface longwave net radiation with the surface longwave net radiation in the 3-100-, 4-100-, 2.5-100-, 2.5-200-, and 1-200-μm spectral domains. Surface longwave net radiation at 2.5-200 μm was found to be optimal, with a bias and root mean square (rms) of less than 0.928 and 0.993 W/m2, respectively. We calculated the errors when estimating surface longwave net radiation at 2.5-200 μm with BBE in different spectral ranges. The results show that BBE at 8-13.5 μm had the lowest error and the corresponding bias and rms were less than 0.002 and 1.453 W/m2, respectively. When the 2.5-200-μm surface longwave net radiation calculated by the 8-13.5-μm BBE was used to replace the all-wavelength surface longwave net radiation, the average bias and rms were 1.473 and 2.746 W/m2, respectively. Using the most representative emissivity spectra, we derived the conversion formulas for calculating BBE at 8-13.5 μm from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectrometer (MODIS) narrowband emissivity products.

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5-$\mu\hbox{m}$spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future.

The quantification of uncertainty in satellite-derived global surface albedo products is a critical aspect in producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A challenge in validating albedo measurements acquired from space is the ability to overcome the spatial scaling errors that can produce disagreements between satellite and field-measured values. Here, we present the results from an accuracy assessment of MODIS and Landsat-TM albedo retrievals, based on collocated comparisons with tower and airborne Cloud Absorption Radiometer (CAR) measurements collected during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC). The initial focus was on evaluating inter-sensor consistency through comparisons of intrinsic bidirectional reflectance estimates. Local and regional assessments were then performed to obtain estimates of the resulting scaling uncertainties, and to establish the accuracy of albedo reconstructions during extended periods of precipitation. In general, the satellite-derived estimates met the accuracy requirements established for the high-quality MODIS operational albedos at 500 m (the greater of 0.02 units or ±10% of surface measured values). However, results reveal a high degree of variability in the root-mean-square error (RMSE) and bias of MODIS visible (0.3-0.7 μm) and Landsat-TM shortwave (0.3-5.0 μm) albedos; where, in some cases, retrieval uncertainties were found to be in excess of 15 %. Results suggest that an overall improvement in MODIS shortwave albedo retrieval accuracy of 7.8%, based on comparisons between MODIS and CAR albedos, resulted from the removal of sub-grid scale mismatch errors when directly scaling-up the tower measurements to the MODIS satellite footprint.

Sonoran Desert is a potential pseudo-invariant site that can be used for vicarious calibration of satellite sensors. However, the surface and atmospheric anisotropy of the site first needs to be characterized for the precise evaluation of the long-term stability of the sensors. In this study, the anisotropy of the desert site is investigated by applying widely used bidirectional reflectance distribution function (BRDF) models to the top-of-atmosphere (TOA) reflectance of Landsat 5 TM data. Time series of the original and BRDF-normalized TOA reflectance are first presented, and the radiometric stability of the time series is computed by using the BRDF-normalized TOA reflectance of the Landsat data.

Bidirectional reflectance factors (BRFs) play a key role in land surface studies. Snow has a significant influence on vegetative surface BRF. To evaluate the surface reflectance behaviors of snow-covered regions, a surface BRF database has been constructed from Multi-angle Imaging SpectroRadiometer BRF products for five biomes in the mid-high latitude regions of the U.S. (evergreen needleleaf forests, shrublands, grasslands, croplands, and urban areas). Using corresponding surface snow depth data from 26 meteorological stations, BRF signatures with snow cover are derived from the database to show the effect of snow on the BRF of vegetation. Five bidirectional reflectance distribution function models' abilities of capturing vegetation-snow mixed BRF shape are evaluated by fitting all the BRF data with snow. The results show that the Rahman model, Ross-Li model, and Walthall model perform well in fitting forest, grassland, and cropland BRFs when the surface is covered by snow. The Rahman model, Ross-Li model, and Roujean model fit visible reflectance well for mixed surfaces. The Rahman model best captures the BRF shapes, followed by the Ross-Li model.

Surface albedo is one of the key radiation parameters required for modeling of the Earth's energy budget. The future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager (ABI) will provide the observations in several shortwave spectral bands together with both high spatial and temporal resolutions which will carry much angular information for estimating instantaneous surface albedo and bi-directional reflectance. According to these advanced sensor characteristics, we propose an improved algorithm that retrieves surface albedo and aerosol optical depth (AOD) simultaneously. To prototype this algorithm, satellite observations with the similar spectral bands and spatial resolution acquired by MODIS are used. Results show a good agreement between retrieved albedo values and ground measurements from SURFRAD.

This paper explores snow bidirectional reflectance distribution function (BRDF) properties over some snow covered regions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) surface reflectance products. In the visible and near infrared (NIR) region, MODIS and MISR surface bidirectional reflectance factors (BRFs) over snow are accumulated to extract snow BRDF properties. Five surface BRDF models are concerned to simulate snow surface reflectance shape. All the models capture the distribution of snow BRFs with limit of accuracy. The simulated BRFs from several models have similar distribution trend and different details. The BRDF properties discussed can be used as background in the snow BRDF retrieval from spaceborne measurements.

Daily evapotranspiration (ET) was mapped and validated at the regional extent over a vineyard landscape. The mapping was performed using the simplified surface energy balance index (S-SEBI) model, along with an Advanced Spaceborne Thermal Emission and Reflection Radiometer imagery, over two growth cycles. The validation exercise was conducted within a Mediterranean vineyard watershed, over seven sites that differed in canopy, soil, and water conditions. Despite the use of a very simple model over a complex row-structured landscape, the obtained accuracy (0.8 mm/day) was similar to those reported over simpler canopies with full covers, and corresponded to requirements for further applications in agronomy and hydrology, where daily ET can be assimilated into land surface models for calibration and control purposes. An analysis of the validation results suggested that, among the possible factors that could affect S-SEBI performances (spatial variability, vine water status, soil type and color, and row orientation), the first-order influence was row orientation.

Temperature and emissivity separation (TES) algorithms designed to work with mid-infrared (MIR) hyperspectral data are extremely limited. Two TES algorithms originally designed for long-wave infrared hyperspectral data, specifically, the iterative spectrally smooth (ISS) algorithm and the stepwise refining algorithm, are extended into MIR and renamed the extended iterative spectrally smooth (EISS) and extended stepwise refining algorithms (ESR), respectively. Numerical experiments are first conducted to evaluate their feasibility. The results of the numerical experiments indicate that the accuracy of the ESR algorithm is higher than that of the EISS algorithm. Moreover, the ESR algorithm is more robust than the EISS algorithm under sunlit conditions. Their accuracy is then validated with in situ measurements. Finally, the emissivity root mean square errors (RMSEs) of the EISS and ESR algorithms are compared with the data derived with the ISS algorithm using in situ measurements. Results show that the average emissivity RMSEs of 0.03 in 2000-2200 cm-1 and of 0.03-0.30 in 2400-3000 cm-1 for nighttime, and 0.02 in 2000-2200 cm-1 and 0.03 in 2500-3000 cm-1 for daytime, can be obtained from ground-based MIR hyperspectral data using the ESR algorithm.

The leaf area index (LAI) is a critical variable used to characterize the terrestrial ecosystem and model land surface processes. Remote sensing is an ideal tool for mapping the LAI. However, the quality of current satellite LAI products does not meet the requirements of the user community in terms of estimation accuracy and data consistency. One way to address these issues is to develop LAI integration algorithms that incorporate existing multiple LAI products and prior knowledge. This paper presents a new data integration method based on empirical orthogonal function (EOF) analysis. The proposed EOF integration algorithm can be operated on both fine and coarse spatial resolution to accommodate the problems arising from a large volume of data. Two runs of multivariate EOF analysis are proposed to address the issue of incompatible temporal resolutions among different data sets. Comparisons with high-spatial-resolution LAI reference maps at 12 sites over North America show that the proposed method can improve LAI product accuracy. After data integration, R2increases from 0.75 to 0.81 and the root mean square error (rmse) decreases from 1.04 to 0.71 over moderate-resolution imaging spectroradiometer (MODIS) products. The improvement of R2and rmse over Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products is not as significant as that over MODIS products. However, the use of a combination of multiple data sources reduces the bias of the LAI estimate from MODIS's 0.3 and CYCLOPES's -0.2 to -0.1.

Although long-term consistent observations of LAI play a significant role in understanding the Earth system and its change, there is no reliable, consistent and accurate LAI product available due to estimation errors, data gaps and inconsistency among data. This paper presents a method, based on the multiresolution tree (MRT), to integrate satellite LAI data with different resolutions. The results of integrating MODIS and MISR LAI show MRT has the capability of filling data gaps, reducing measurement errors and can also generate consistent results across scales. When compared with optimal interpolation (OI), MRT produces similar results but with significantly improved efficiency.

This letter assesses three satellite-estimated surface downwelling shortwave irradiance data sets: 1) GEWEX-SRB; 2) ISCCP-FD; and 3) CERES-FSW, using ground measurements collected at 36 globally distributed sites from 2000–2002. SRB and FD solar irradiance are available at three hourly intervals during daytime and FSW hourly solar irradiance is available at late morning. The data are compared to ground measurements at the temporal resolutions of the satellite measurements. Results indicate that the downwelling solar irradiances of the three products show good overall agreement with ground measurements. FSW has an overall coefficient of determination of$R^{2} = \hbox{0.69}$, a bias of 29.7$\hbox{Wm}^{-2}$(6.0% in relative value), and a standard deviation (STD) of 123.2$\hbox{Wm}^{-2}$(25.1% in relative value). The values are 0.83,$-5.5\ \hbox{Wm}^{-2}$($-$1.9%), 101.3$ \hbox{Wm}^{-2}$(35.0%) for SRB, and 0.83, 2.8$\hbox{Wm}^{-2}$(0.3%), 101.7$\hbox{W m}^{-2}$(35.0%) for FD. However, there are substantial uncertainties in these products in some regions. For example, large biases ranging from$-90.2\ \hbox{Wm}^{-2}$to 45.8$\hbox{Wm}^{-2}$are found in SRB in Southeast Asia. FD has large biases in Southeast Asia (58.9$\hbox{Wm}^{-2}$) and Greenland$\hbox{(-33.2}\ \hbox{Wm}^{-2}\hbox{)}$. FSW has substantial biases in Southeast Asia (72.6$\hbox{Wm}^{-2}$) and Japan (66.5$\hbox{Wm}^{-2}$); while$R^{2}$is 0.35 in Tibetan Plateau and 0.47 in Southeast Asia.

This letter presents new models for estimating clear-sky instantaneous longwave radiation over land surfaces using the Geostationary Operational Environmental Satellites (GOES) Sounders and GOES-R Advanced Baseline Imager (ABI) thermal infrared top-of-atmosphere (TOA) radiances. The method used in this study shares the same hybrid method framework designed for Moderate Resolution Imaging Spectroradiometer. We propose separate surface downward longwave radiation (LWDN) and upwelling longwave radiation (LWUP) models because the two components are dominated by different surface/atmospheric properties. A nonlinear model was developed to estimate LWDN, and a linear model was developed to estimate LWUP. The GOES-12 Sounder-derived LWDN, LWUP, and surface net longwave radiation (LWNT = LWUP-LWDN) were evaluated using one full-year of ground data from the Surface Radiation Budget Network. The root-mean-squared errors (rmses) are less than 22.03 W/m2at all four sites. Our study indicates that the hybrid method can also be applied to estimate LWUP using the future GOES-R ABI TOA radiances. The lack of a channel beyond 13.3 m in the proposed ABI design may cause larger rmses when estimating LWDN.

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in numerous environmental studies. In this paper, a stepwise refining temperature and emissivity separation (SRTES) algorithm is proposed based on the analysis of the relationship between surface self-emission and atmospheric downward spectral radiance in a narrow spectral region. The SRTES algorithm utilizes the residue of atmospheric downward spectral radiance in the calculated surface self-emission as a criterion and adopts a stepwise refining method to determine both the emissivity at the location of an atmospheric emission line in a narrow spectral region and the surface temperature. Three methods have been used to evaluate the SRTES algorithm. First, numerical experiments are conducted to evaluate if the SRTES algorithm can accurately retrieve the “true” LST and LSE from the simulated data. When a noise equivalent spectral error of$2.5\ e^{-9}\ \hbox{W/cm}^{2}/\hbox{sr}/\hbox{cm}^{-1}$is added into the simulated data, the retrieved temperature bias$(T_{\rm bias})$is 0.04$\pm$0.04 K, and the root-mean-square error (rmse) of the retrieved emissivity is below 0.002 except in the extremities of the 714–1250$\hbox{cm}^{-1}$spectral region. Second,in situmeasurements are used to validate the SRTES algorithm. The average rmse of the retrieved emissivity of ten samples is about 0.01 in the 750–1050$\hbox{cm}^{-1}$spectral region and is 0.02 in the 1051–1250$\hbox{cm}^{-1}$spectral region, but the rmse is larger when the sample emissivity is relatively low. Third, our new algorithm is compared with the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm using both a simulated data set andin situmeasurements. The comparison demonstrates that the SRTES algorithm performs better than the ISSTES algorithms, and it can overcome some of the common drawbacks in the existing hyperspectral TES algorithms for the accurate retrieval of both temperature and emissivity.

It is an urgent need for natural disaster monitoring to generate biophysical variables data with high accuracy timely from remotely sensed data. A real-time inversion method to estimate leaf area index (LAI) using MODIS time series reflectance data (MOD09A1) is developed in this paper. A seasonal autoregressive integrated moving average (SARIMA) model is used to derive LAI climatology. A dynamic model is then constructed based on the climatology from the SARIMA model to evolve LAI in time, and used to provide the short-range forecast of LAI. Predictions from the model are used with the ensemble Kalman filter (EnKF) techniques to recursively update biophysical variables as new observations arrive. The validation results show that the real-time inversion method is able to produce a relatively smooth LAI product efficiently, and the accuracy is significantly improved over the MODIS LAI product.

Surface upwelling longwave radiation (LWUP) is an important component in the surface radiation budget. Existing satellite-derived LWUP data sets are too coarse to support high-resolution numerical models, and their accuracy needs to be improved. In this paper, we evaluate three methods for estimating clear-sky land LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1-km spatial resolution. The three methods are as follows: (1) the temperature-emissivity method; (2) the linear model method; and (3) the artificial neural network (ANN) model method. Methods 2 and 3 are new methods based on extensive radiative transfer simulations and statistical analysis. We explicitly considered surface emissivity effects by incorporating the University of California Santa Barbara emissivity library in the radiative transfer simulation. The three methods were evaluated using ground-measured LWUP from six SURFRAD sites. Although methods 2 and 3 were developed using MODIS Terra atmospheric profiles, they were applied to both Terra and Aqua data because the designs of the two sensors are similar. The root mean squared errors (rmses) of the ANN model method are smaller than that of the other two methods at all sites. The averaged rmses of the ANN model method are 15.89 W/m2(Terra) and 14.57 W/m>2(Aqua); the averaged biases are -8.67 W/m2(Terra) and -7.21 W/m2(Aqua). The biases and rmses for Aqua are ~1.3 W/m2smaller than that of Terra. The biases and rmses of the ANN model method are ~5 W/m2smaller than that of the temperature-emissivity method and ~2.5 W/m2smaller than that of the linear model method.

Multiple leaf area index (LAI) products have been generated from remote-sensing data. Among them, the Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI product (MOD15A2) is now routinely derived from data acquired by MODIS sensors onboard Terra and Aqua satellite platforms. However, the MODIS LAI product is not spatially and temporally continuous and is inaccurate in many areas for some vegetation types. In this paper, a new algorithm is developed to estimate LAI from time-series MODIS reflectance data (MOD09A1). A radiative-transfer model is coupled with a double-logistic LAI temporal-profile model, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the parameters of the coupled model from the temporal signature in a given time window. Preliminary analysis using MODIS surface-reflectance data at flux sites was performed to validate this method. The results show that the new algorithm is able to construct a temporally continuous LAI product efficiently, and the accuracy has been significantly improved over the MODIS LAI product as compared to field-measured LAI data.

The bidirectional reflectance distribution function (BRDF) of land surfaces specifies the behavior of surface directional reflectance as a function of illumination and viewing angles. The Moderate resolution Imaging Spectroradiometer (MODIS) and The Multiangle Imaging Spectroradiometer (MISR) provide their BRDF model parameters products respectively. Since the BRDF model parameters products are inverted from the observations with limited viewing directions for a given pixel, it is necessary to evaluate whether they can effectively characterize the directional reflectance in other viewing directions. In this study, we choose BRDF products data on four land cover types to learn their bidirectional reflectance signatures and to analyze the representation of MISR and MODIS BRDF model parameters products. The results show that: the MISR BRDF product shows hot spot more clearly than MODIS product, and both BRDF models' representative ability for extrapolating the reflectance at those directions without viewing dada tends to weaken when the viewing zenith angle increases.

This study develops a method to estimate surface daytime net radiation (Rn) from solar shortwave radiation measurements with help of the conventional meteorological observations and vegetation indices from satellite. Data collected at twenty-two sites in the U.S. and two sites on the Tibetan Plateau (China) from 2000 to 2006 are used to develop and validate the method. The land cover types of the sites vary among desert, semi-desert, croplands, grasslands and forest. The elevations of the sites range from 98 m to 4700 m. The results show that our method estimates Rnaccurately. The bias varies from -7.8 W m-2to 9.7 W m-2(plusmn3% in relative value) for different sites, while the root mean square error ranges from 12.8 W m-2to 21 W m-2(from +5% to +9% in relative value) for different sites with an average of 16.9 W m-2(+6% relative) for all sites. For all sites the correlation coefficient is about 0.99. The correlation coefficient between the measured and predicted annual abnormal (year-average subtract multi-year average) in daytime Rnis as high as 0.91, indicating that the method can be used to accurately estimate long-term variation in Rn.

Gaps and uncertainties within current Moderate Resolution Imaging Spectroradiometer (MODIS) land products limit their application in understanding land surface dynamics. This paper employs a revised iterative strategy based on multi-channel singular spectrum analysis (MSSA) to fill the gaps in MODIS land products. Our results on MODIS leaf area index (LAI) products show MSSA has the ability to use both spatial and temporal information to fill gaps within MODIS LAI products to produce spatially and temporally smoother datasets.

We proposed a method in an earlier study to estimate latent heat of evapotranspiration (ET). However, the influence of soil moisture (SM) on ET was not well considered and is addressed in this paper by incorporating the Diurnal land surface temperature (Ts) Range (DTsR). ET, measured at twelve sites in the U. S. during 2001-2006, is used to validate the improved method. Site land cover varies from grassland, native prairie, cropland, deciduous forest, to evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-average ET is about 0.92 for all the sites, the bias is -1.9 W m-2and the Root Mean Square Error (RMSE) is 28.6 W m-2. We calculated global monthly ET from 1986 to 1995 at a spatial resolution of 1degtimes1deg from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and compared it with the fifteen land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months global daily ET show that the bias is 4.5 W m-2, the RMSE is 19.8 W m-2and the correlation coefficient is 0.82.

A previous algorithm developed to estimate photosynthetically active radiation (PAR) from satellite observations is improved and extended to retrieve surface albedo as well. The revised algorithm was validated with GOES-12 data over the North America continent. We used measurements from six SURFRAD and six FLUXNET sites in 2003 to validate the GOES PAR and albedo retrievals. The results show that GOES instantaneous PAR retrievals have a mean deviation of 175.0 mu mol s-1m-2(20.4% in relative value), a bias of 15.4 mu mol s-1m-2(2.0% in relative value), and a correlation coefficient of 0.881 for the sites under all-sky conditions. GOES daytime averaged PAR has a mean deviation of 91.3 NASA mol s-1m-2(11.8% in relative value), a bias of 14.1 mu mol s-1m-2(2.0% in relative value), and a correlation coefficient of 0.931 for all the sites under all sky conditions. Compared with the albedo measurements at the six FLUXNET sites, GOES albedo retrievals in 0.4-0.7 mum have a mean deviation of 0.017, a bias of 0.002 and a correlation coefficient of 0.773, while the corresponding MODIS visible albedo product has a mean deviation of 0.021, a bias of -0.028 and a correlation coefficient of 0.600.

This study analyzes the accuracy of the Particle Filter (PF) assimilation algorithm to retrieve Leaf Area Index (LAI) from remotely sensed observations using the crop growth model CERES_Maize as a dynamic system, the radiative transfer model SAIL as the observation equation, and MOD09 for external observations. Nonlinearity of the crop growth and radiative models makes the posterior probability of retrieved LAI non-Gaussian. The advantage of PF is its ability to estimate accurately the non-Gaussian posterior probability of retrieved LAI by the particles system. We retrieve LAI by the bootstrap particle filter algorithm whenever a remotely sensed observation was available. By comparing our filtered results to measured LAI at the Yushu area of Jilin province, China, we found that this algorithm greatly improved LAI retrieval. The crop growth model's constraint information and accurate estimation of posterior probability contributed to the improvement in retrieved LAI. We validated the accuracy of maize yield estimation by field measurements.

A new algorithm is developed to estimate LAI from time-series MODIS reflectance data (MOD09A1) based on coupled radiative transfer model and process model. The radiative transfer model is coupled with an empirical LAI dynamic model to simulate the time series reflectances. An optimization method is used to adjust the values of the parameters of the coupled model to seek a model trajectory that best fits a set of observations in a given time window. The preliminary analysis using MODIS surface reflectance data at some fluxnet sites was performed to validate this method. The results show that the algorithm is able to produce spatially and temporally continuous LAI product efficiently, and the accuracy of the retrieved LAI has been significantly improved over the MODIS LAI product compared to the field measured LAI data.

Many canopy reflectance models have been developed in the last decades and used for estimating land surface biogeophysical variables, such as leaf area index (LAI), from satellite observations through optimization procedures. In most studies, the derivative information of the canopy reflectance model has not been used effectively, which limits this approach for regional and global applications. The final solutions are often converged to the local minima. To address these issues, the adjoint model of a canopy radiative transfer model is developed in this study through the automatic differentiation technique. The developed adjoint model is used for sensitivity study, and a combination of the adjoint model with the trust region global optimization method is performed to retrieve LAI from the Enhanced Thematic Mapper Plus (ETM+). This study demonstrates that this method can be reliably used for inverting LAI efficiently and is suitable for global applications.

Much attention has been focused on the assimilation of satellite data and products into land surface processes. In this letter, a variational data assimilation scheme is developed based on the weak-constraint concept. It assimilates surface skin temperature into a simple land surface model for the estimation of turbulent fluxes. An automatic differentiation technique is used to derive the adjoint codes to evaluate the gradient of the cost function. After the construction of this assimilation system, numerical experiments are conducted to test its performance with different model errors, and the comparison is also made with the strong constraint scheme. The results show that the land surface turbulent fluxes can be retrieved with highly satisfactory accuracy.

In this paper, a methodology for improving the Leaf Area Index (LAI) product of the vegetation canopy and the preliminary retrieval results by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) data is presented. We attempt to improve the estimation of LAI through a physical inversion algorithm with a canopy reflectance model. Taking Konza Prairie experiment as an example, the results suggest that this method can utilize effectively the MISR and MODIS observing information and the prior knowledge which can be obtained from the ground measuring and the sensor products.

We developed a new algorithm to retrieve incident PAR at 1 km resolution using Geostationary Operation Environmental Satellite (GOES) visible band data. This algorithm draws on multi-temporal observations to simultaneously retrieve surface reflectance and atmospheric parameters. Topography correction is also included in the algorithm to account for the impact of rugged terrains. Validation against ground measurement indicated the algorithm's effectiveness.

This letter reports a statistical method to estimate detector-dependent systematic error in Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) Bands 20-25 and 27-36. There exist scan-to-scan overlapped pixels in MODIS data. By analyzing a sufficiently large amount of those most overlapped pixels, the systematic error of each detector in the TIR bands can be estimated. The results show that the Aqua MODIS data are generally better than the Terra MODIS data in 160 MODIS TIR detectors. There are no detector-dependent systematic errors in Bands 31 and 32 for both Terra and Aqua MODIS data. The maximum detector errors are 3.00 K in Band 21 of Terra and -8.15 K in that of Aqua for brightness temperatures of more than 250 K

Overview of the Special Issue on Global Land Product Validation: In parallel with the recent bloom of sensors providing frequent medium-resolution observations (Fig. 1), global land products have been increasingly developed and released within the community. The raw data acquired by these sensors are transformed into higher level products that can be more easily exploited by the user community. In many cases, multiple products are developed from each sensor and similar products derived from different sensors. With this, users need access to quantitative information on product uncertainties to help them assess the most suitable product, or combination of products for their specific needs. As remote sensing observations are generally merged with other sources of information or assimilated within process models, evaluation of product accuracy is required. Making quantified accuracy information available to the user can ultimately provide developers the necessary feedback for improving the products, and can possibly provide methods for their fusion to construct a consistent long-term series of surface status.

The minimum band reflectance based compositing method is a main compositing method for retrieval of land surface reflectance from multi-temporal observations. But the shadow effect is difficult to be removed. In this paper, a new method that can identify and remove the shadow pixels from multi-temporal data is proposed. It compares the least two pixels to determine the possibility of the first pixel as shadow. If the first pixel is shadow, it is discarded. The shadow pixels can he removed from the multi-temporal observations after iteration of this procedure two or three times. It has been tested with MODIS data and the results showed it could operate effectively over different land cover types.

Semiarid rangelands are very sensitive to global climatic change; studies of their biophysical attributes are crucial to understanding the dynamics of rangeland ecosystems under human disturbance. In the Santa Rita Experimental Range, AZ, the vegetation has changed considerably, and there have been many management activities applied. This study calculates seven surface variables: the enhanced vegetation index, the normalized difference vegetation index (NDVI), surface albedos (total shortwave, visible, and near-infrared), leaf area index (LAI), and the fraction of photosynthetically active radiation (FPAR) absorbed by green vegetation from the Enhanced Thematic Mapper (ETM+) data. Comparison with the Moderate Resolution Imaging Spectroradiometer vegetation index and albedo products indicates they agree well with our estimates from ETM+, while their LAI and FPAR are larger than from ETM+. Human disturbance has significantly changed the cover types and biophysical conditions. Statistical tests indicate that surface albedos increased and FPAR decreased following tree-cutting disturbances. The recovery will require more than 67 years and is about 50% complete within 40 years at the higher elevation. Grass cover, vegetation indexes, albedos, and LAI recovered from cutting faster at the higher elevation. Woody plants, vegetation indexes, and LAI have recovered to their original characteristics after 65 years at the lower elevation. More studies are needed to examine the spectral characteristics of different ground components.

We apply a hybrid inversion algorithm to estimate land surface biophysical variables (e.g., leaf area index) from the CHRIS (Compact High Resolution Imaging Spectrometer), and ETM+. Field campaigns were conducted over Tucson, Arizona to validate the algorithms and the products. The derived products were compared for different human management activities. These products are then available for input to a plant growth model for calculating the potential for carbon sequestration.

This study presents some preliminary results on estimating crop yield at the regional scale from MODIS (Medium resolution imaging spectroradiometer) data using the data assimilation method. MODIS data products include leaf area index (LAI) and enhanced vegetation index (EVI). The crop growth models of DSSAT were used in this study, which are driven by weather, soil and crop management data. Some of the variables of the models were adjusted through data assimilation algorithms for accurate prediction of crop yields.

There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.

The talk is mainly based on my book (Liang, (2003)). It summarizes various recent algorithms used in quantitative remote sensing of land surfaces in the optical spectrum. Different examples from our recent research projects will be illustrated.

Albedo is a key parameter in monitoring the energy exchanges between the solar radiations and the land surfaces. The MODIS team generates the albedo products every 16 days. The products need to be validated by ground truths under different environmental conditions. In this study, we developed a 3-step validation procedure. The Ambrals (Algorithm for Modeling Bidirectional Reflectance Anisotropies of the Land Surface) model inversion was used to retrieve the albedo from the measured BRDF data over the winter wheat fields at the point/plot scale. And then, as our second step, the albedo values from the Airborne Multiangular Thermal-infrared Imaging System (AMTIS) over the same target area were estimated and validated using the ground point measurements. Finally, the retrieved albedo from airborne data were aggregated and compared with the MODIS albedo products. Our validation procedure has demonstrated a practical method to validate that albedo from spacebrone remotely sensed data (e.g., MODIS). The validation results show that the MODIS albedo products are reasonably good. Albedo is a key parameter in monitoring the energy exchanges of land surfaces. The hemispherical albedo is traditionally observed by albedometer at local meteorological stations, where the observing targets are usually grassland in a specific environment. Because some applications require albedo over a large area, retrieving regional and global albedo products from remote sensing observations can be more productive. The MODIS albedo products are from the multi-angular remote sensing (MARS) observations of every 16-days accumulation. The production needs to be validated by ground truths. One of the main problems in the validation is that the field-measured albedo is different in scale from the albedo retrieval using remote sensing data. The albedometer field measurement is over a small area, less than 1m 2 , while the spatial resolution of the MODIS albedo product is about 1 km. Another problem is associated with the different wavebands between the albedometer and the MODIS sensors. As a possible solution, we created a 3-steps validation procedure. As the first step, we used the BRDF data measured in the field to retrieve the albedo by Ambrals model inversion. The observing target was winter wheat. The retrieved albedo is comparable with that one measured by albedometer since both measurements are in the same observing scale. The effect of the wavebands difference was also corrected at this step. In the second step, we retrieved the albedo from the airborne MARS observation data of the same target. The spatial resolution is 1.36m at nadir. The retrieved albedo from airborne AMTIS BRDF data can be validated by using our field measurement. Finally, the retrieved albedo from airborne data was compared with the MODIS albedo product. Scaling-up needs to be considered in the comparison. In this work, the field measurements and airborne data came from the large satellite-airborne-ground synchronous experiment in the April of 2001. The experimental region is in the Shunyi county, 50km northeast of the Beijing City, China.

Leaf area index (LAI) is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimate LAI from Landsat-7 Enhanced Thematic Mapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, Maryland were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.

Land surface albedo is a critical variable needed in land surface modeling. The conventional methods for estimating broadband albedos rely on a series of steps in the processing chain, including atmospheric correction, surface angular modeling, and narrowband-to-broadband albedo conversions. Unfortunately, errors associated with each procedure may be accumulated and significantly impact the accuracy of the final albedo products. In an earlier study, we developed a new direct procedure that links the top-of-atmosphere spectral albedos with land surface broadband albedos without performing atmospheric correction and other processes. In this paper, this method is further improved in several aspects and implemented using actual Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Several case studies indicated that this new method can predict land surface broadband albedos very accurately and eliminate aerosol effects effectively. It is very promising for global applications and is particularly suitable for nonvegetated land surfaces. Note that a Lambertian surface has been assumed in the radiative transfer simulation in this paper as a first-order approximation; this assumption can be easily removed as long as a global bidirectional reflectance distribution function climatology is available,

The Advanced Land Imager (ALI) is a multispectral sensor onboard the National Aeronautics and Space Administration Earth Observing 1 (EO-1) satellite. It has similar spatial resolution to Landsat-7 Enhanced Thematic Mapper Plus (ETM+), with three additional spectral bands. We developed new algorithms for estimating both land surface broadband albedo and leaf area index (LAI) from ALI data. A recently developed atmospheric correction algorithm for ETM+ imagery was extended to retrieve surface spectral reflectance from ALI top-of-atmosphere observations. A feature common to these algorithms is the use of new multispectral information from ALI. The additional blue band of ALI is very useful in our atmospheric correction algorithm, and two additional ALI near-infrared bands are valuable for estimating both broadband albedo and LAI. Ground measurements at Beltsville, MD, and Coleambally, Australia, were used to validate the products generated by these algorithms.

The Coleambally Irrigation Area (CIA) in Australia has been used to validate Hyperion data. A time series of Hyperion imagery has been acquired over the CIA during the entire 2001/2002 southern hemisphere summer growing season. Extensive spectral ground sampling was performed to validate the spectral signatures derived by Hyperion. In this paper, we describe the basic processing steps that have been applied to geometrically and atmospherically correct the data and reduce the effects of noise prior to investigating the time series of specific measures of crop productivity through its biochemistry.

Annual sequences of the first reprocessed albedo, bidirectional reflectance distribution function (BRDF), and nadir BRDF-adjusted surface reflectance (NBAR) products are being evaluated. BRDF model parameters (or weights) are used to compute black sky albedo at local solar noon and white sky albedo and to compute surface reflectance at a common nadir geometry. In addition to these standard resolution albedo, BRDF, and NBAR products, which are provided in the integerized sinusoidal grid projection, the BRDF parameters, black sky albedos (at local solar noon), and white sky albedos are now also being operationally produced in a global geographic projection known as the Climate Modeling Grid (CMG). These are currently available at a 0.25 degree spatial resolution, although there is community interest in a 0.05 degree resolution. In addition to the operational CMGs, coarser 0.5 degree and 1 degree resolution versions of the reprocessed albedo data have also been produced for the ISLSCP-II initiative. The global distribution of these albedos (as they vary throughout the year) are presented, as well as discussions of the most recent evaluations of the quality of the standard products.

For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on atmospheric correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.

Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with N bands presents N equations in N+1 unknowns (N spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER) data. The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way, The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5/spl deg/, 70.2% within 1/spl deg/ and 84% within 1.5/spl deg/), although improvements are still needed.

To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. After reviewing historical development of atmospheric correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of atmospheric and surface conditions.

Remotely sensed imagery has been used for developing and validating various studies regarding land cover dynamics. However, the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles. The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. We introduce a number of computational techniques that lead to a substantial speedup of an atmospheric correction algorithm based on using look-up tables. Excluding I/O time, the previous known implementation processes one pixel at a time and requires about 2.63 seconds per pixel on a SPARC-10 machine, while our implementation is based on processing the whole image and takes about 4-20 microseconds per pixel on the same machine. We also develop a parallel version of our algorithm that is scalable in terms of both computation and I/O. Experimental results obtained show that a Thematic Mapper (TM) image (36 MB per band, 5 bands need to be corrected) can be handled in less than 4.3 minutes on a 32-node CM-5 machine, including I/O time.

The radiative transfer equations for a coupled atmosphere and canopy are solved numerically be an improved Gauss-Seidel iteration algorithm. The radiation field is decomposed into three components: unscattered sunlight, single scattering, and multiple scattering radiance for which the corresponding equations and boundary conditions are set up and their analytical or iterational solutions are explicitly derived. This algorithm enables one to obtain the internal radiation field as well as radiances at boundaries. Any form of bidirectional reflectance distribution function (BRDF) as a boundary conditon can be easily incorporated into the iteration procedure. The upwelling radiances have been evaluated for different atmospheric conditions, leaf area index (LAI), leaf angle distribution (LAD), leaf size and so on. The formulation presented is also well suited for analyzing the relative magnitude of multiple scattering radiance and single scattering radiance in both the visible and near-infrared regions.>

Radiative transfer modeling of the bidirectional reflectance distribution function (BRDF) of leaf canopies is a powerful tool to relate multiangle remotely sensed data to biophysical parameters of the leaf canopy and to retrieve such parameters from multiangle imagery. However, the approximate approaches for multiple scattering that are used in the inversion of existing models are quite limited, and the sky radiance frequently is simply treated as isotropic. This paper presents an analytical model based on a rigorous canopy radiative transfer equation in which the multiple-scattering component is approximated by asymptotic theory and the single-scattering calculation, which requires numerical integration to properly accommodate the hotspot effect, is also simplified. Because the model is sensitive to angular variation in sky radiance, the authors provide an accompanying new formulation for directional radiance in which the unscattered solar radiance and single-scattering radiance are calculated exactly, and multiple-scattering is approximated by the well-known delta two-stream approach. A series of validations against exact calculations indicates that both models are quite accurate, especially when the viewing angle is smaller than 55 degrees . The Powell algorithm is then used to retrieve biophysical parameters from multiangle observations based on both the canopy and the sky radiance distribution models.>