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.

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.

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

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.

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.

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 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.

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).

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.