Vegetation solar-induced chlorophyll fluorescence (SIF) is linked to photosynthetic activities and has shown great potential for studying the global carbon cycle. As the most widely used leaf chlorophyll fluorescence radiative transfer model, Fluspect has been extensively used in the exploration and application of chlorophyll fluorescence. The computational efficiency of Fluspect greatly influences its practicability because the leaf-level Fluspect model is the core of fluorescence simulations. However, a numerical algorithm with iterations required was adopted in Fluspect to calculate mesophyll fluorescence matrices. This study revealed that the numerical algorithm is the most time-consuming part (~98%) of Fluspect and proposed an analytical algorithm to replace it. The two algorithms are equivalent, but the analytical algorithm uses 90% less time. Similarly, in a test over a newly collected leaf fluorescence dataset, the inversion of Fluspect achieves an 86% decrease in computational time after incorporating the analytical algorithm. With this updated algorithm, the computational efficiency and practicability of Fluspect are significantly improved and will better serve SIF-related studies.

Current global land-surface parameter products are generally derived from surface reflectance data acquired by a single satellite sensor, which requires atmospheric corrections for top-of-atmosphere (TOA) reflectance data. And the retrieval methods for different parameters are commonly based on different assumptions, as a result of which the estimated parameters are inconsistent in terms of physical detail. Furthermore, the currently available land-surface parameter products only have a limited number of spatial resolutions. These problems existing in the current land-surface parameter products greatly limit their applications in earth sciences. This paper proposes a novel approach to simultaneously estimate leaf area index (LAI), photosynthetically active radiation (PAR), fraction of absorbed photosynthetically active radiation (FAPAR) and surface albedo at multiple spatial scales from satellite TOA reflectance data with different spatial resolutions. Based on the average of multi-year Global Land Surface Satellite (GLASS) LAI product, an Ensemble Multiscale Tree (EnMsT) was constructed to describe the initial conversion relationships among LAI values at different spatial scales. The EnMsT was coupled with a land surface-atmosphere radiative transfer model to simulate TOA reflectance data with different spatial resolutions, which were compared with the corresponding satellite TOA reflectance data to update the LAI values of each node in the EnMsT. Finally, the estimated LAI values were input into the land surface-atmosphere radiative transfer model to calculate PAR, FAPAR and surface albedo values at different spatial resolutions. In this paper, the method was applied to estimate LAI, PAR, FAPAR and surface albedo at 28.9, 57.8, 115.6, 231.2, 462.4, and 924.8 m from Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) TOA reflectance data at four sites with cropland and grassland biomes. The retrieved parameter values were compared with the corresponding MODIS, GLASS, the second version of the Geoland2 (GEOV2) products, and available LAI and FAPAR ground measurements at these sites. The results demonstrate that the retrieved parameter values agree with the corresponding parameter products and the retrieved LAI and FAPAR values at different resolutions are in good agreement with the aggregated LAI and FAPAR values from the high-resolution reference maps.

Vegetation productivity is an important parameter for estimating carbon stocks in terrestrial ecosystems and is important for monitoring regional and global ecological changes. In this study, gross primary productivity (GPP) and net primary productivity (NPP) products with a spatial resolution of 500 m and a temporal resolution of 8 days from 2000 to 2019 were produced based on Global land surface satellite (GLASS) leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR) products, and an improved light use efficiency (LUE) model that introduced clearness index (CI) to represent the effect of radiation on LUE. Validated by FLUXNET GPP data, Bigfoot NPP and EMID NPP data, the GPP and NPP products have high accuracy. The dataset has the potential to monitor global and regional ecology and vegetation growth conditions.

Solar-induced chlorophyll fluorescence (SIF) is found well correlated with gross primary productivity (GPP) and a good indicator of vegetation status. However, the influence of topography on SIF has not been studied, and SIF models with topographic consideration are needed to analyze this influence. Unfortunately, apart from computationally expensive 3-D models, current SIF models cannot work with sloping terrain. An efficient 1-D SIF model with topographic consideration (SIFT) is proposed in this study based on the well-known Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The evaluation of SIFT, by comparing with the 3-D Discrete Anisotropic Radiative Transfer (DART) model, demonstrates that it has high accuracy. This study also demonstrates that ignoring topography induces significant errors (exceeding 125% for a 60° slope) in canopy SIF simulations. The conclusion that the topography is an important factor for SIF and the proposed SIFT model will benefit those who are interested in SIF simulations and applications.

Topography is an inevitable factor when processing remote sensing data. Slope and aspect are sufficient for describing topographic conditions within a fine-scale pixel (e.g., 30 m); the resulting schematic is referred to as a sloping terrain and is modeled as a solo slope. A composite slope, which contains many solo slopes that are collectively referred to as rugged terrain, is needed for coarse-scale pixels (e.g., 1 km). However, many parameter estimation algorithms use topographic approximation methods, such as the assumption of a flat surface, assumption of a solo slope, omission of contributions from adjacent slopes, and usage of the terrain view factor (TVF) to approximate adjacent contributions. These topographic approximations can induce significant errors over mountain areas; however, errors caused by various approximation methods have not been comprehensively analyzed. This study summarizes radiative transfer (RT) processes over rugged terrains, proposes composite-slope models for surface parameters, and analyzes the influences of different topographic approximation methods on surface reflectance (${\rho }$), directional brightness temperature (${T_{b}}$), surface net radiation (${E_{n}}$), slope downward radiation (${E_{d}}$), absorbed photosynthetically active radiation (APAR), total emitted solar-induced chlorophyll fluorescence (SIF) by all leaves (${F_{e}}$), SIF observed at the top of the canopy (${F_{o}}$), broadband albedo (${\alpha }$), and broadband hemispherical emissivity (${\varepsilon }$) at a series of spatial resolutions (30, 90, 270, 540, 1080, and 5400 m). Three surface types are tested: vegetation, soil, and snow. The results demonstrate that: 1) assumptions of a flat surface or a solo slope and the use of the TVF method induce significant errors (1%–58%) in all aforementioned parameters; 2) adjacent contributions can be neglected when simulating${\varepsilon }$, APAR,${F_{o}}$,${F_{e}}$, and low-reflective${\rho }$; and 3) adjacent contributions should be considered for${E_{n}}$,${E_{d}}$, and high-reflective${\rho }$, and they are also significant when simulating${\alpha }$using fine-resolution data or over snow surfaces. These findings and the composite-slope models developed in this study benefit those who intend to conduct forward modeling and parameter estimation studies over rugged terrains.

Adaxial and abaxial leaf surfaces have different anatomical structures and varied chemical compositions. This asymmetry between leaf sides dictates that the spectral properties (reflectance, transmittance, and emissivity) of adaxial and abaxial surfaces are not identical. Laboratory measurements have demonstrated that this difference is significant over certain wavelengths. However, the influences of leaf dorsoventrality on such parameters (e.g., reflectance and brightness temperature) at the canopy scale have received little attention from the remote sensing community. One of the reasons is the lack of canopy radiative transfer models that can handle leaf dorsiventral properties. Although 3-D ray-tracing- or Monte Carlo-based models can achieve this, they are too complex to be implemented for massive tasks due to their low computational efficiency. Instead, they usually serve as benchmarks to evaluate other models. This study develops a unified optical–thermal canopy radiative transfer model considering leaf dorsiventral properties. It is based on the 1-D scattering by arbitrary inclined leaves (SAIL) model and, thus, has excellent efficiency and is easy to use. Evaluation of the proposed model by comparing it with the 3-D ray-tracing discrete anisotropic radiative transfer (DART) model shows that it is consistent with DART, with normalized root mean square errors (NRMSEs) of 0.013 and 0.005 within a reflective (for reflectance) and an emissive [for directional brightness temperature (DBT)] bands, respectively. Preliminary analyses ignoring leaf dorsoventrality within the optical and thermal spectral ranges by using the measured leaf spectra demonstrated that it induced significant errors in canopy reflectance (up to 40%) and DBT (up to 0.2 K) estimates under certain wavelengths. However, it should be noted that the influences of leaf dorsoventrality are determined by leaf adaxial/abaxial spectra, which still need to be explored due to limited measurements, especially under the thermal infrared bands.

Most surface–atmosphere radiative transfer models (RTMs) work only for flat surfaces, with the exception being time-consuming 3-D scene-based models. The deficiency of flat-surface RTMs that do not consider topographic effects is that their applications in earth observation and simulation studies are impaired because rugged terrains make up approximately 24% of the global land surface. Another deficiency of most surface–atmosphere RTMs is that they model reflected and emitted (i.e., solar and thermal) radiative transfer processes separately, which limits RTMs in applications, such as fire detection. This study proposes a unified optical–thermal RTM coupling framework (RTM-CF) that considers topographic effects based on the four-stream approximation theory. The framework couples surface–atmosphere RTMs and can simultaneously simulate a set of parameters at the top-of-atmosphere (TOA) and bottom-of-atmosphere (BOA) levels from optical and thermal spectral ranges. These parameters include the TOA directional radiance/reflectance, TOA exitance/albedo, TOA net radiation, surface radiance/reflectance/albedo, surface downward/upward/net radiation, and FAPAR/APAR. The RTM-CF with topographic effects is compared with the well-known 3-D discrete anisotropic radiative transfer (DART) ray-tracing model and validated by field measurements from three steep sites. The evaluation results show that the simulated reflectance, radiance, and radiation fluxes are consistent with the DART results and the field data, with$R^{2}>0.93$and scatter points close to the 1:1 line for all parameters. In this RTM-CF, atmospheric and topographic effects are simultaneously incorporated, and the surface anisotropy is also effectively considered. This framework is highly modularized, which enables it to be easily adapted to different submodels.

The scattering by arbitrary inclined leaves (SAIL)-series models are some of the most well-known and widely used canopy radiative transfer models in the remote-sensing community. The latest version of 4SAIL simulates directional radiance from the optical to thermal spectrum range, but it is not suitable for sloping terrain. This limits its use in the currently ever-expanding development of applications for high-spatial-resolution observations. This study extends the 4SAIL model to 4SAILT, which considers the topographical effects on direct solar radiation and the obstruction of the surrounding topography for hemispherical radiation and the gravitropic influences on leaf angle distribution (LAD). The proposed 4SAILT model was evaluated by the 3-D discrete anisotropic radiative transfer (DART) ray-tracing model for various sky radiation conditions, soil and leaf temperatures, observational geometries, leaf area index values, and six typical LAD functions. The simulated results of directional radiance demonstrated that 4SAILT was consistent with DART, having RMSE values less than 2.0 and 0.1 W/ m2/μm/sr over the 0.35-2.5 and 2.5- 15 μm spectra, respectively. As an accurate, efficient, and ready-to-use model, 4SAILT benefits those who intend to use SAIL for modeling terrain areas. The 4SAILT model simulates canopy directional radiance, reflectance, emissivity, and brightness temperature over terrain surfaces, through the optical to thermal ranges. It can also be used as a surface model when estimating shortwave, longwave, and net radiation.

The applications of high-spatial-resolution satellite data have been increasing in recent years owing to improvements in sensor techniques, and the errors in estimated parameters induced by ignoring topographic effects are increasingly stressed because their effects are important for parameter retrieval from high-spatial-resolution satellite observations. A coupled surface-atmosphere model is employed to develop a two-step multiparameter estimation scheme to simultaneously estimate multiple parameters (leaf area index, LAI; aerosol optical depth, AOD; photosynthetically active radiation, PAR; incident shortwave radiation, ISR; surface albedo, and fraction of absorbed photosynthetically active radiation, FAPAR) from long-term Landsat 4-8 top-of-atmosphere (TOA) observations. First, the influential parameters of the coupled model are retrieved through optimization retrieval strategies. Then, these estimated parameters are entered into the coupled model to compute the PAR, ISR, surface reflectance, surface albedo, and FAPAR. Validation of this scheme with in situ measurements from 57 sites demonstrates that it can successfully estimate multiple parameters from Landsat TOA data, with root mean square errors (RMSEs) of LAI, AOD, FAPAR, visible albedo, shortwave albedo, PAR, and ISR of 0.69, 0.16, 0.13, 0.034, 0.047, 26.80, and 64.28 W/m2, respectively. In the two-step multiparameter estimation scheme, atmospheric and topographic corrections of satellite observations are avoided because the atmospheric and topographic effects are incorporated, and the surface anisotropy is also effectively considered. In addition, by using the two-step multiparameter estimation scheme, physical connections among the multiple parameters are ensured since they are estimated from the same physical model.

The long time series vegetation productivity products are of great significance to the research of increasing CO2and global changes. In this paper, global net primary productivity (NPP) in 1981-2018 was firstly estimated with Global LAnd Surface Satellite (GLASS) data, ERA-Interim meteorological data and the other variables by using the improved Multi-source data Synergized Quantitative (MuSyQ) NPP algorithm. The average global NPP is 61.0 PgC/yr in 1981-2018, which is in great agreement with the other similar products. The global NPP has shown a significant increase trend, with an annual growth rate of 0.10 PgC/yr over the past 38 years. NPP in the northern hemisphere and southern hemisphere account for 62.0% and 38.0% of the global respectively, both show an increasing trend. The overall increasing trends in NPP are also consistent among most of the biomes.

Leaf area index (LAI) is a key parameter in characterizing vegetation canopy. In this paper, we proposed an efficient method to estimate LAI from Landsat surface reflectance data. The three-dimensional (3-D) discrete anisotropic radiative transfer (DART) model was used to simulate canopy reflectance by constructing real vegetation scenes. However, it took too much time to simulate reflectance because of its computational complexity. Thus, we employed Gaussian process (GP) to emulate the DART model using simulation data. LAI was retrieved by iteratively minimizing a cost function with the shuffled complex evolution (SCE-UA) global optimization method. The final results demonstrated that the retrieved LAI values are in good agreement with the ground reference map.

This study rewrites the widely used 6S radiative transfer model (RTM) in Fortran 90, to make which easily studied and used. Three surface reflectance models are also incorporated, including the PROSAIL-D canopy reflectance model, the ACRM canopy reflectance model, and the asymptotic radiative transfer (ART) snow reflectance model. The 6S+PROSAIL model is then further analyzed: (1) Sensitivity analysis (SA) of parameters of the 6S+PROSAIL model is conducted at 400-2500 nm, and SA of parameters of PROSAIL is also given. (2) Bidirectional reflectance factors of the PROSAIL and 6S+PROSAIL models are simulated over 2π space, to show their anisotropy characteristics. The analysis results provide reference information for those who intend to use these models in their applications, such as parameter inversion and observation simulation; and the updated code is helpful to users of 6S, and those who plan to put 6S and PROSAIL together.

NPP is calculated to characterize vegetation activity as well as improve our understanding of terrestrial ecosystem carbon cycle. In this study, we estimated a time series of NPP and the spatial and temporal variations from 1982 to 2012 in China. Subsequently, the correlations between the NPP and climate factors (temperature and precipitation) were evaluated to show the responses of vegetation NPP to climate changes. The results showed that NPP in China decreased from southeast to northwest due to the spatial variability of vegetation types and climate characteristics. Annual NPP had a fluctuating increase tendency during our study period with values ranging from 1.92 to 2.73 PgC•a-1, with an annual increase of 0.02 PgC•a-2. In addition, NPP in north China correlates positively with precipitation and negatively correlates with temperature, this is owing to the fact that this region is relatively dry and increasing precipitation extends the growing season of vegetation. In south China the results were the opposite.

Geostationary Operational Environmental Satellite-R Series (GOES-R) is a new generation of geostationary satellites with high temporal resolution. In this study, a new algorithm was developed to simultaneously retrieve leaf area index (LAI) and aerosol optical depth (AOD) with high temporal resolution from GOES-R top of atmosphere (TOA) reflectance data.Firstly, fourth-order polynomial is used to fit the AOD corresponding to the cloudless observation data, thereby converting multiple AOD into five coefficients of the polynomial and improving the efficiency;Then, the AOD values calculated by the fitted polynomial are input into the model to simulate the TOA reflectance, and the combination of optimal parameters is retrieved by minimizing the distance between the simulated reflectance and the satellite observation reflectance;Finally, the comparison between the retrievals and existing product data shows that the method has certain feasibility and can meet the research need of the intra-day variation of AOD to some extent.

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.

Fine spatial details of vegetation growth are usually lost in leaf area index (LAI) products obtained from coarse spatial resolution satellite sensors. This may bring uncertainties in ecosystem process models, which usually require LAI products with fine spatiotemporal resolutions. Successful downscaling of LAI dynamics to fine spatial resolution is very important for meeting the demands of these models. Hence, a multiscale multisensor approach using the ensemble Kalman smoother (EnKS) technique is proposed in this paper. The LAI dynamics at a coarser spatial resolution are incorporated as prior information into the remotely sensed observations for time series LAI estimation at a finer spatial resolution. Downscaled LAI dynamics are evaluated based on spatial distribution and temporal trajectory. The results indicate the assimilated LAI to be in good agreement with the reference values at the different spatial scales. For example, the coefficient of determination (R2) between the reference values and fine-resolution LAI results retrieved by the proposed approach is 0.71 with a root-mean-square-error (RMSE) value of 0.65 on Julian day 185 at the Agro site. The method has proved to be effective for downscaling LAI dynamics, which improves the spatiotemporal patterns of fine-resolution LAI retrievals with respect to earlier methods.

The time-consuming modeling of physical remote sensing models restricts their application to parameter estimation from satellite observations. Machine learning techniques have become highly developed in recent years and show good capacity for model fitting. Based on our previously developed coupled soil-canopy-atmosphere radiative transfer model (RTM) and a multiple parameters estimation scheme, this paper evaluates the performance of four machine learning algorithms [Gaussian process regression (GPR), back-propagation neural networks (NNs), random forest regression, and general regression NN] on emulating the coupled RTM, where the traditional lookup table (LUT) algorithm is also compared. The results show that the GPR algorithm can emulate complex RTMs with excellent accuracy and efficiency. GPR emulators of photosynthetically active radiation (PAR), fraction of absorbed PAR, and incident shortwave radiation were applied to the multi-parameter estimation scheme to replace the traditional LUT algorithm, which avoids the need to integrate over the spectra while achieving an acceleration ratio of 16. A test of the updated multi-parameter estimation scheme at the Bondville site using 18 years of clear-sky observations demonstrates that replacing the computationally expensive integration processes with GPR emulators is practical. The emulators can also be used to simulate the corresponding parameters independently, and this GPR acceleration method for complex models is universal and can be easily applied to other time-consuming models.

Current global leaf area index (LAI) products are generally produced from single-temporal satellite observations acquired by a single sensor. These LAI products are usually spatiotemporally discontinuous and inaccurate for some vegetation types in many areas, which limit the applications of these LAI products to the understanding of land dynamics. In this paper, a new data assimilation method was proposed to estimate multiscale and temporally continuous LAI values from multi-sensor time-series satellite observations with different spatial resolutions. An ensemble multiscale tree (EnMsT) was used to establish the conversion relationships between different spatial resolution LAI values, and dynamic models of the LAI at different spatial scales were constructed to evolve LAI at the corresponding spatial scales over time. At each time step, a multiscale Kalman filter (MKF) was introduced to fuse the predicted LAI values from the dynamic models at different spatial scales and to construct a forecasted EnMsT. When satellite observations were available, an ensemble multiscale filter (EnMsF) technique was applied to update the LAI values at each node of the EnMsT. The method was applied to estimate temporally continuous multiscale LAI values from the time series of Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at several sites with different vegetation types. The estimated multiscale LAI values were compared with the MODIS and GEOV2 LAI products, and the reference LAI values at the corresponding scales aggregated from the high-resolution LAI surface images. The estimated LAI values with the finest spatial resolution were also validated by ground measurements from the selected sites. The results show that the new method is able to simultaneously estimate temporally continuous multiscale LAI values by assimilating satellite observations with different spatial resolutions, and the estimated multiscale LAI values are well consistent with the reference LAI values at the corresponding scales over the selected sites. The root-mean-square error (RMSE) and coefficient of determination of the retrieved LAI values at the finest spatial scale against the ground measurements over the selected sites are 0.539 and 0.788, respectively.

Satellite observations are affected by clouds, aerosol and other factors, resulting in temporal discontinuities and spatial incompleteness in leaf area index (LAI) products. Furthermore, the currently available LAI products are generally retrieved from mono-temporal remote sensing data acquired by a single sensor, without comprehensive utilization of multi-source satellite observations. This paper proposes a new data assimilation method to retrieve temporally continuous LAI at different spatial scales from time-series multi-source remote sensing data with different spatial resolutions using an ensemble multiscale tree model (EnMsT). A dynamic model was constructed to describe the change rule of LAI in time series. At each time-step, the forecast of LAI from the dynamic model was used to construct an initial EnMsT. Then, satellite surface reflectance data with different spatial resolutions were used to update the LAI at each node of the EnMsT using an ensemble multiscale filter (EnMsF) technique. The final results demonstrate that this new method can estimate temporally continuous LAI at different spatial scales and the retrieved LAI values are in good agreement with the field measurements.

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 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).

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).

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.

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).

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.

The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.

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.

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.

Time series leaf area index (LAI) derived from remote sensing data is a key parameter for environment researches especially on dynamic changes of land surface. In this study, a new approach was developed to retrieve LAI from time series Moderate Resolution Imaging Spectroradiometer (MODIS) multi-angular remote sensing data. Based on radiative transfer theory, we used Ross Thick-Li Sparse Reciprocal (RTLSR) kernel driven model to generate specific directional BRFs and corresponding anisotropy information, employed Scattering by Arbitrarily Inclined Leaves with Hotspot (SAILH) model to fill in missing data and Data-Based Mechanistic modeling (DBM) procedure to model and estimate time series vegetation LAI. The preliminary results indicated that the LAIs derived in this study have good agreement with ground LAI measurements and their continuity of the time series are superior to MODIS LAI product.

The new data assimilation algorithm is developed to estimate LAI from time-series MODIS reflectance data (MOD09A1). The canopy radiative transfer model (ACRM) is coupled with an empirical LAI dynamic model, and the ensemble Kalman smoother (EnKS) is used to estimate the parameters of the coupled model from MOD09A1 data. The preliminary analysis using MODIS surface reflectance data at some AmeriFlux network sites was performed to validate this method. The results show that the algorithm is helpful to produce the temporally continuous LAI estimation of cropland efficiently. By comparing with the field measured LAI, the retrieved LAI has been significantly improved and shown more smooth in time series than the MODIS LAI product.

LAI is the more important parameter of vegetation canopy, so LAI inversion from remote sensing observations is the hot study field, especially for the high spatial and high temporal resolution remote sensing data. Beijing-1 microsatellite is an applied earth observing microsatellite of China, which can also give us the good data of short cycle time and wider coverage. So it is necessary to generate the quantitative product of BJ-1 remote sensing data. In this paper, the main object is to study on the method of the leaf area index inversion for producing BJ-1 LAI product. The neuronal network method is used to get the relationship between LAI and reflectance in green, red and NIR band. Based on the BJ-1 LAI inversion, the second object of this paper is to generate of high spatial and high temporal resolution LAI product. A method is proposed to get high spatial and temporal resolution LAI product by fusing the time-series MODIS LAI product(1 km, 8-day product)and BJ-1 LAI. Through this study, we can get the LAI products of BJ-1, which is with the high spatial resolution and high time resolution. This product will provide more information of vegetation for BJ-1 microsatellite data applications.

Satellite remote sensing enables derivation of LAI globally at available spatial resolution and temporal frequency, and several LAI products have been produced. However, there are problems for the current global or regional LAI products, which restrict the application of these products. On the one hand, there are gaps between the large number of parameters of physical models and the small amount of data obtained by single sensor, which may cause the decrease in accuracy that LAI products should have. On the other hand, there are gaps between instantaneous observation of remote sensing and parameters which have change rules. Custom methods to retrieve LAI from remote sensing data are just involving the transient observations, discarding the information about process. To resolve these problems, we develop a methodology to retrieve LAI by involving diverse data from multiple sensor and LAI change rule. The methodology can take full advantage of the different band and angle information of time series MODIS and MISR data to improve the accuracy of the retrieved LAI over the MODIS LAI product compared to the field measured LAI data. And the retrieved LAI is also temporally continuous.

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.

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.

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.

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.

Leaf area index (LAI) is an important parameter for estimating growth status of crops. An important method for retrieving LAI from remotely sensed observations is by the canopy reflectance model inversion. But many model inversion methods didn't take into account the relationship between estimated LAIs in different crop growth stages. In this research, we consider the crop growth model which describe how LAI change with crop growth stages. The main method for this research is assimilating multiple crop's canopy reflectance observed in different growth stages into the cost function for LAI retrieval. Data assimilation algorithm we used in this study is the variation algorithm. The model inversion results have showed that time series observations of canopy reflectance can decrease uncertainty of estimated LAI in model inversion.

This paper gives a brief description of an Airborne Multi-angle Power Line Inspection System (AMPLIS). AMPLIS is composed by 3 CCD cameras, a Position and Orientation System (POS), a stabilized platform, the data collection and control subsystem. It can be equipped on a helicopter and fly along the lines at a speed of about lOOkm/h at a relative height of 100 m over the power lines. AMPLIS is capable of detecting the distance between the power lines and the ground surface with an accuracy of less than 0.5 m. It can automatically find the dangerous objects beneath the lines which can greatly decrease the man power and cost in power line inspection. It has been successfully tested with good performance in Wuhan, China, 2005.

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.