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.

The bidirectional reflectance factor (BRF) is a key parameter for understanding the radiative transfer process within cities, influenced by the sun-target-sensor geometry. Simulating urban BRF poses significant challenges due to the complex three-dimension (3-D) structures of urban landscapes. Previous attempts have been facing significant hurdles, either inadequately addressing the complexities of real-world urban structures or consuming too many computing resources. To overcome these challenges, this study introduces an analytical model—urban bidirectional reflectance analytical model (UBRAM), which takes advantage of landscape-level geometric and radiometric parameters incorporating 3-D urban structures to simulate the BRF of neighborhood-scale urban landscapes ($100\times 100- 1000\times 1000$m). UBRAM underwent verification using simulations from the well-known radiative transfer model (discrete anisotropic radiative transfer, DART) under various solar-illumination geometries and diverse urban morphologies. Results demonstrated a good agreement between UBRAM and DART, with an overall${R} ^{2}$ranging from 0.829 to 0.983 and the shape similarity index exceeding 0.99 in most scenes, underscoring the effectiveness of UBRAM. Notably, leveraging a modular design, UBRAM enables independent processing of 3-D urban scenes and radiative transfer simulation, ensuring efficiency and scale applicability. This study establishes UBRAM as a viable model, offering a novel approach for quantifying BRF of neighborhood-scale urban surfaces. Moreover, UBRAM requires a limited number of easily obtainable parameters, facilitating its straightforward adaptation for application in other urban areas. UBRAM facilitates the simulation of radiative transfer processes based on real-world 3-D urban models, thereby enhancing the accuracy of scene rendering and downstream applications such as urban heat islands and environment monitoring.

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.

Surface albedo plays a significant role in Earth’s energy budget and global climate change. The spaceborne remote sensing technique is efficient for deriving and monitoring long-term surface albedo over large regions. Numerous satellite surface albedo products have been established and used for climate change research. However, the surface daily mean albedo is not regularly produced from satellite observations, even though it is more critical than instantaneous albedo for calculating daily shortwave radiation budget. Compared to polar-orbiting satellites, the geostationary satellites offer greater potential for mapping daily albedo with more diurnal observations. This study proposes an innovative multisensor combined direct estimation algorithm, which takes advantage of multiple clear-sky observations from new-generation geostationary satellite sensors FengYun-4 Advanced Geostationary Radiation Imager (AGRI) and Himawari-8 Advanced Himawari Imager (AHI) taken during the same day to improve the surface albedo estimation. Compared to albedo estimates derived from a single sensor, the root mean squared error (RMSE) decreases from 0.030 to 0.022 at OzFlux sites and from 0.040 to 0.031 at Heihe sites. Moreover, an information index of top-of-atmosphere (II_TOA) reflectance is proposed to quantify the amount of information that multiangular TOA observations carry in the surface albedo estimation. As a result, combining observations from two sensors increases such information by 20%, as compared to observations from a single sensor. This study demonstrates the combined multisensor direct estimation method has significant potential for improving surface albedo estimation.

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.

This article introduces the Chinese Terrestrial Ecosystem Carbon Inventory Satellite(TECIS), the first mission with the integration of active and passive sensors for forest carbon mapping. TECIS utilizes time-synchronized multiple-beam LiDAR and multi-angle optical imagery for forest carbon monitoring. We first provide an overview of the satellite's features and discuss the observational capabilities of the LiDAR and multi-angle payload. The preliminary results for forest height estimation analysis were shown using the payloads. The Bidirectional Reflectance Distribution Function (BRDF) features such as hot/dark spot information, were calculated based on the multi-angle images. A deep learning approach for forest parameter estimation through the fusion of LiDAR and BRDF data.

Cloud detection of satellite images is an important preprocessing step for remote sensing applications due to the adverse effect of clouds on data analysis. With the development of machine learning methods, an increasing number of studies have applied the state-of-art technology to detect clouds in satellite imagery. However, machine learning-based cloud detection methods are often limited by a small number of training samples with insufficient representativeness. To improve the robustness of cloud detection, developing a method to automatically construct a reliable training sample database is a meaningful issue. This paper explores a hybrid Erosion morphology processing and Disagreement-based Semi-supervised Learning model (EDSL) to refine cloud samples generated by the widely-used Fmask algorithm. Experimental results show that the sample accuracy after refinement could be improved from the original Fmask cloud masks. Further, the random forest model trained by the sample database after EDSL refinement achieves comparable accuracy but higher efficiency with Fmask algorithm, thus proving the effectiveness of the above method.

The high spatial resolution land surface phenology (LSP) monitoring is often limited by the temporal discontinuity of high spatial resolution observations. Many gap-filling methods have been proposed for LSP monitoring, however, a thorough intercomparison and evaluation is still lacking. Four widely-used methods, including the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), Multi-year based model, and Spatiotemporal Shape Matching Model (SSMM) were selected to extract green-up date (GUD) based on the Harmonized Landsat and Sentinel-2 (HLS) dataset. The results of the four methods show consistency with those of PhenoCam sites (R2>0.64) and there is a lag phenomenon. Compared within 3×3 VIIRS pixel window, the difference between mean VIIRS GUDs and aggregated 30m GUDs is small and the mean absolute difference is less than 7 days. Comprehensively, SSMM has high consistency (R2=0.75) and smaller bias (Bias=7.3days), which shows more potential in LSP monitoring.

Three-dimensional (3D) urban data opens up new possibilities and challenges for urban research. In this paper, we present an analytical model for urban bidirectional reflectance distribution function based on geometric parameters extracted from real-world 3D data. The analytical model relies on the simplified urban scenes, which have the same geometric parameters as real urban 3D scenes. The bidirectional reflectance factor of the 4 urban scenes in the two major cities of Beijing and Shanghai in China is simulated with the urban analytical model. The innovations of this paper are as follows: 1) a simple and efficient analytical model is developed, considering the complexity of urban structure and the optical properties of ground objects, and 2) the analytical model is backed up by real 3D data and can be applied in various forms of urban scenes.

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.

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.

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.

Photosynthetically active radiation (PAR) incident at the surface is crucial for understanding and modeling the Earth's climate and ecosystems. In this article, a general parameterization scheme suitable for PAR estimation under cloudy skies is proposed based on an elaboration on the PAR radiative transfer (RT) processes above, in, and beneath cloud layers. Its most important novel property is that all RT processes in cloudy atmospheres are explicitly explained, including ozone absorption, Rayleigh scattering, cloud single scattering, cloud multiple scattering, aerosol scattering, and cloud reflection. Theoretical accuracy evaluations show over 95% of errors (against rigorous RT calculations) lie within ±20 W/m2, and an operational application with multisource satellite products as inputs shows the root-mean-square error (RMSE) of ≤42 W/m2on the hourly timescale. Therefore, the parameterization scheme is accurate both in theory and actual applications.

Surface shortwave albedo plays a crucial role in studying urban heat island, local and global radiation budget balance, and climate change. Through modifying the land cover and consequently surface reflectivity, urbanization may lead to substantial changes in surface albedo. China has undergone rapid and massive urbanization during the past 30 years. In order to explore the change of surface shortwave radiation budget caused by urbanization, an accurate and fine resolution surface albedo dataset is needed. Our previous efforts have been devoted to estimating 30m surface albedo from Landsat data, which has been validated with ground measurements distributed globally. In this study, we first examined the estimation accuracy of surface albedo at sites in China, from which the albedo estimation had a bias of 0.005, R2of 0.717, and root-mean-square-error (RMSE) of 0.053. Then, our albedo algorithm was applied to Landsat data over a few cities in China at different stages of economic development, i.e. Shanghai, Wuhan, and Tai’an. The temporal and spatial change of albedo from 1986 to 2015 and its relationships with land cover change in those study regions were studied. The result shows that urban expansion has brought an increase in albedo in most of these cities, especially in the central district. The rate of albedo increase of Shanghai was the highest among these cities, which is 0.007 per decade, resulting in about 4.65 W/ m2increase of shortwave upward radiation per decade. Compared to previous research, this study provides more detailed albedo changes of cities at the community scale. The results can also serve as a benchmark for decisionmakings to counteract climate change from future land cover changes and to improve urban microclimate research.

Driven by complicated socioeconomic factors, China experienced drastic forest cover changes since the late 1950s, which need to be quantified by satellite images. Also, the size and frequency of change events are important for prescribing disturbance scenarios in carbon and ecosystem models and for evaluating the natural and anthropogenic drivers of forest change. The distribution of size and frequency of forest change is usually modeled by a power-law function, which, however, poorly describes the non-constant variance of patch size at different frequency levels. Based on a forest-cover and -change dataset derived from remote sensing data, we proposed a hierarchical method to model the size and frequency distribution and examined the spatial pattern of forest loss from 2000 to 2005 in China. We found that each province had a unique relationship between size and frequency and dominant disturbance patches in the southern provinces were typically smaller than the north.

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.

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.

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.

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.

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 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.

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 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 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.

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.

Surface albedo is one of the key radiation parameters required for modeling of the Earth's energy budget, and many ecological and climate models usually require high quality consistent surface albedo as inputs and validation sources. The Moderate Resolution Imaging Spectroradiometer (MODIS) has been providing surface albedo products periodically. However, due to influence of weather, sensors and algorithms, MODIS albedo products often have many gaps and low quality pixels. This paper proposed an improved spatio-temporal smoothing algorithm utilizing multilevel procedure that combines spatial interpolation and tempora smoothing to get better albedo products based on multiyear observations and high quality neighboring pixels. Compared with field measurements, the improved albedo products in China based on MOD43B3 show better correlation with the measured surface albedo with the overall root mean squared errors around 0.0767. The generated albedo products may be applied in land surface models.