Retrieval Of Surface Reflectance Research Articles

The Impact of Choice of Solar Spectral Irradiance Model on Atmospheric Correction of Landsat 8 OLI Satellite Data

Many atmospheric correction schemes of radiance-based optical satellite data require the selection of normalized solar spectral irradiance models at the top of atmosphere (TOA). However, there is no scientific consensus in literature as to which available model is most suitable. This article examines five commonly used models applied to Landsat 8 Operational Land Imager (OLI) TOA radiance and reflectance products to assess the accuracy and stability between models used to derive surface reflectance products. It is assumed that the calibration of the United States Geological Survey (USGS) Landsat 8 OLI TOA reflectance and radiance products are accurate to currently claimed levels. The results show that the retrieved surface reflectance can exhibit significant variations when different solar irradiance models are used, especially in the OLI coastal blue band at 443 nm. From the five solar irradiance models, the Kurucz 2005 model showed the least bias compared with OLI TOA reflectance product and least variance in surface reflectance. Furthermore, improvement was obtained by adjusting the total solar irradiance (TSI) normalization, and additional validation was provided using observed in situ water leaving reflectance data. The results from this article are particularly relevant to aquatic applications and to satellite sensors that provide TOA radiance such as previous Landsat and other current and historical missions.

A new method for atmospheric correction and de-noising of CRISM hyperspectral data

We propose a new method to perform atmospheric correction and de-noising on hyperspectral image cubes acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on board NASA’s Mars Reconnaissance Orbiter (MRO). The CRISM imager has had an important role in advancing our understanding of many aspects of Martian mineralogy. Many mineral detections from CRISM data have been facilitated by significant efforts in the development of the CRISM data processing pipeline to retrieve surface reflectance. However, some residuals remain in CRISM spectra after atmospheric correction, causing difficulty in the interpretation of processed reflectance spectra. In addition, CRISM images are occasionally corrupted with high noise levels exhibiting heterogeneous statistical properties. This paper identifies the cause of such spectral distortions and describe a technique that simultaneously performs both atmospheric correction and de-noising for each image cube individually. In particular, our method focuses on the 1.0–2.6 μm wavelength region of CRISM images and is applicable to images of non-icy surfaces. Experimental results show that our technique is able to significantly mitigate noise and distortions from various sources like gaseous absorptions, detector temperature, and water ice aerosols, compared with the atmospheric correction method in the CRISM official processing pipeline called volcano scan correction, for a variety of scenes. Careful validations that include the qualitative examination of noise and artifacts both on ratioed and non-ratioed spectra and comparison using multiple overlapping images strengthen confidence in our approach.

Below-Cloud Atmospheric Correction of Airborne Hyperspectral Imagery Using Simultaneous Solar Spectral Irradiance Observations

Retrieving surface properties from airborne hyperspectral imagery requires the use of an atmospheric correction model to compensate for atmospheric scattering and absorption. In this study, a solar spectral irradiance monitor (SSIM) from the University of Colorado Boulder was flown on a Twin Otter aircraft with the National Ecological Observatory Network's (NEON) imaging spectrometer. Upwelling and downwelling irradiance observations from the SSIM were used as boundary conditions for the radiative transfer model used to atmospherically correct NEON imaging spectrometer data. Using simultaneous irradiance observations as boundary conditions removed the need to model the entire atmospheric column so that atmospheric correction required modeling only the atmosphere below the aircraft. For overcast conditions, incorporating SSIM observations into the atmospheric correction process reduced root-mean-square (rms) error in retrieved surface reflectance by up to 57% compared with a standard approach. In addition, upwelling irradiance measurements were used to produce an observation-based estimate of the adjacency effect. Under cloud-free conditions, this correction reduced the rms error of surface reflectance retrievals by up to 27% compared with retrievals that ignored adjacency effects.

Analysis of Surface Reflectance Retrieval Over Four Typical Surfaces Via Gaofen-1 Satellite WFV4 Imagery

As a basis for the quantitative application of satellite remote sensing, surface reflectance can be retrieved through atmospheric correction methods. Currently, most studies have focused on developing or comparing atmospheric correction methods. However, few studies have quantitatively analyzed the effects of input parameters in an atmospheric correction method on retrieved surface reflectance. In this study, we evaluated the effects of the calibration coefficient, aerosol optical depth (AOD), aerosol type, and satellite zenith angle over four typical surfaces using wide field-of-view sensor four data of the Gao Fen-1 satellite. The results showed that (1) the relative errors of shrub, corn, grass, and soil reflectance increased as the calibration coefficient error increased; (2) the calibration coefficient, AOD, aerosol type, and satellite zenith angle affected corn reflectance retrieval the most, whereas they had the smallest effect on soil reflectance retrieval; and (3) the accuracy of the satellite zenith angle on the retrieved surface reflectance was the least pronounced, whereas the accuracy of aerosol type was the most pronounced.

Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes

Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).

An Atmospheric Correction Method over Bright and Stable Surfaces for Moderate to High Spatial-Resolution Optical Remotely Sensed Imagery

Although many attempts have been made, it has remained a challenge to retrieve the aerosol optical depth (AOD) at 550 nm from moderate to high spatial-resolution (MHSR) optical remotely sensed imagery in arid areas with bright surfaces, such as deserts and bare ground. Atmospheric correction for remote-sensing images in these areas has not been good. In this paper, we proposed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial-resolution imagery in arid areas with bright surfaces. Land surface in arid areas is usually bright and stable and the variation of atmosphere in these areas is also very small; consequently, the land-surface characteristics, specifically the bidirectional reflectance distribution factor (BRDF), can be retrieved easily and accurately using time series of satellite images with relatively lower spatial resolution like the Moderate-resolution Imaging Spectroradiometer (MODIS) with 500 m resolution and the retrieved BRDF is then used to retrieve the AOD from MHSR images. This algorithm has three advantages: (i) it is well suited to arid areas with bright surfaces; (ii) it is very efficient because of employed lower resolution BRDF; and (iii) it is completely automatic. The derived AODs from the Multispectral Instrument (MSI) on board Sentinel-2, Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Gao Fen 1 Wide Field Viewer (GF-1/WFV), Gao Fen 6 Wide Field Viewer (GF-6/WFV), and Huan Jing 1 CCD (HJ-1/CCD) data are validated using ground measurements from 4 stations of the AErosol Robotic NETwork (AERONET) around the world.

Elevation-Dependent Removal of Cirrus Clouds in Satellite Imagery

Masking of cirrus clouds in optical satellite imagery is an important step in automated processing chains. Firstly, it is a prerequisite to a subsequent removal of cirrus effects, and secondly, it affects the atmospheric correction, i.e., aerosol and surface reflectance retrievals. Cirrus clouds can be detected with a narrow bandwidth channel near 1.38 μ m and operational detection algorithms have been developed for Landsat-8 and Sentinel-2 images. However, concerning cirrus removal in the case of elevated surfaces, current methods do not separate the ground reflected signal from the cirrus signal in the 1.38 μ m channel when performing an atmospheric correction, often resulting in an overcorrection of the cirrus influence. We propose a new operational algorithm using a Digital Elevation Model (DEM) to estimate the surface and cirrus cloud contributions in the 1.38 μ m channel and to remove cirrus effects during the surface reflectance retrieval. Due to the highly variable nature of cirrus clouds and terrain conditions, no generic quantitative results could be derived. However, results for typical cases and the achieved improvement in cirrus removal are given for selected scenes and critical issues and limitations of the approach are discussed.

Improvements of 6S Look-Up-Table Based Surface Reflectance Employing Minimum Curvature Surface Method

We propose a methodology employing an interpolation technique on the Second Simulation of a Satellite Signal (6S) look-up table (LUT) to improve surface reflectance retrieval using Himawari-8/Advanced Himawari Imager (AHI). A minimum curvature surface (MCS) technique was used to refine the 6S LUT, and the solar zenith angle (SZA) and viewing zenith angle (VZA) increments were narrowed by 0.5°. The interpolation processing time was relatively short, about 3172 s per channel, and the interpolated xa and xb were well represented by the changes in SZA and VZA. An evaluation of the interpolated xa and xb for six cases revealed a relative mean absolute error of less than 5% for all channels and cases; however, a slight difference was evident for higher values of SZA and VZA. To evaluate the surface reflectance, we compared the surface reflectance derived using 6S LUT with that calculated using 6S only. Application of the interpolated 6S LUT showed a lower relative root mean square error (RRMSE) of 0.65% to 9.29% for all channels, than before interpolation. The improvement in surface reflectance measurements increased with the SZA. For a SZA above 75°, the RRMSE improved significantly for all channels (by 11.33–45.1%). In addition, when the MCS method was applied, the surface reflectance measurements improved without spatial discontinuity and showed good agreement with 6S results in a linear profile analyses. Thus, the method proposed can improve LUT based surface reflectance measurements in less time and increase the availability of surface reflectance data based on geostationary satellites.

DeepEmSat: Deep Emulation for Satellite Data Mining.

The growing volume of Earth science data available from climate simulations and satellite remote sensing offers unprecedented opportunity for scientific insight, while also presenting computational challenges. One potential area of impact is atmospheric correction, where physics-based numerical models retrieve surface reflectance information from top of atmosphere observations, and are too computationally intensive to be run in real time. Machine learning methods have demonstrated potential as fast statistical models for expensive simulations and for extracting credible insights from complex datasets. Here, we develop DeepEmSat: a deep learning emulator approach for atmospheric correction, and offer comparison against physics-based models to support the hypothesis that deep learning can make a contribution to the efficient processing of satellite images.

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation

The MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) algorithm enables simultaneous retrieval of aerosol and bidirectional surface reflectance at high resolution of 1 km. Taking advantage of multi-angle and image-based information, the MAIAC algorithm has great potential for improving retrieval of aerosols over both dark and bright surfaces. Here, by comparing MAIAC aerosol products with the ground-based observations at 9 typical sites spread out in China, we gain the insights regarding the performance of MAIAC algorithm, for the first time, over Asia that has complicated surface types, diverse aerosol sources, and heavy loading of aerosols in the atmosphere. While aerosol products from MAIAC show similar spatial distribution as that from MODIS Dark-Target (DT) and Deep-Blue (DB) algorithms, they are superior to reveal numerous hotspots of high AOD values in fine scales due to their higher resolution at 1 km. Moreover, since MAIAC algorithm for cloud screening uses time series of observations, it shows higher effectiveness to mask cloudy pixels as well as the pixels of the melting and aging ice/snow surfaces. While MAIAC and ground-observed AOD values show high correlation coefficient of ∼0.94 in two AERONET sites of Beijing and Xianghe, considerable bias is prevalent in other regions of China. Systematic underestimation is found over the deserts in western China likely due to the high bias of single scattering properties of aerosol model prescribed in MAIAC algorithm. In eastern China, the distinct positive bias is found in conditions with low-moderate AOD values and likely results from errors in regression coefficients in the surface reflectance model. Given its advantages in cloud and snow/ice screening and retrieval in fine spatial resolution, MAIAC algorithm can be improved by further refinement of regional aerosol and surface properties.

Land surface reflectance retrieval from optical hyperspectral data collected with an unmanned aerial vehicle platform.

We present a physical-based atmospheric correction algorithm for land surface reflectance retrieval based on radiative transfer model MODTRAN 5, with which the aerosol optical thickness @550 nm (AOT@550nm), columnar water vapor (CWV) could also be estimated from the hyperspectral data collected over UAV platform. Then, the method was tested on both the synthetic and field campaign-collected hyperspectral data by an UAV-VNIRIS (UAV visible/near-infrared imaging hyperspectrometer) with the spectral range covering from 400 to 1000 nm. The retrieval results were validated with theoretical values from synthetic data and truth values from field campaign measurements. The results show that the averaged MAE (mean absolute error) and RMSE (root mean squared error) of measured and retrieved surface reflectance based on estimated AOT@550nm and CWV is 0.0134 and 0.0130. Meanwhile, the averaged MAE and RMSE of measured and retrieved surface reflectance based on ground measured AOT@550nm and CWV is 0.0101 and 0.0112. The results show that our introduced method has good agreement with the method based on ground-measured AOT@550nm and CWV. These encouraging results also indicate that the introduced physical-based atmospheric approach provides a quick and reliable way to acquire the land surface reflectance from UAV platform-observed hyperspectral data for further quantitative remote sensing applications.

Joint retrieval of surface reflectance and aerosol properties with continuous variation of the state variables in the solution space – Part 2: application to geostationary and polar-orbiting satellite observations

Abstract. This paper presents the simultaneous retrieval of aerosol optical thickness and surface properties from the CISAR algorithm applied both to geostationary and polar-orbiting satellite observations. The theoretical concepts of the CISAR algorithm have been described in Govaerts and Luffarelli (2018). CISAR has been applied to SEVIRI and PROBA-V observations acquired over 20 AERONET stations during the year 2015. The CISAR retrieval from the two sets of observations is evaluated against independent data sets such as the MODIS land product and AERONET data. The performance differences resulting from the two types of orbit are discussed, and the information content of SEVIRI and PROBA-V observations is analysed and compared.

Joint retrieval of surface reflectance and aerosol properties with continuous variation of the state variables in the solution space – Part 1: theoretical concept

Abstract. This paper presents a new algorithm for the joint retrieval of surface reflectance and aerosol properties with continuous variations of the state variables in the solution space. This algorithm, named CISAR (Combined Inversion of Surface and AeRosol), relies on a simple atmospheric vertical structure composed of two layers and an underlying surface. Surface anisotropic reflectance effects are taken into account and radiatively coupled with atmospheric scattering. For this purpose, a fast radiative transfer model has been explicitly developed, which includes acceleration techniques to solve the radiative transfer equation and to calculate the Jacobians. The inversion is performed within an optimal estimation framework including prior information on the state variable magnitude and regularisation constraints on their spectral and temporal variability. In each processed wavelength, the algorithm retrieves the parameters of the surface reflectance model, the aerosol total column optical thickness and single-scattering properties. The CISAR algorithm functioning is illustrated with a series of simple experiments.

Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data

Remote imaging spectroscopy in the 0.4–2.5-μm visible and shortwave infrared (VSWIR) range captures the majority of solar-reflected energy and enables a wide range of earth surface studies. This spectral range is also influenced by atmospheric effects including absorption from atmospheric gases and aerosols, Rayleigh scattering, and particle scattering. Globally consistent surface measurements must compensate for these atmospheric effects. This article reviews the physical and mathematical foundations of modern VSWIR atmospheric retrieval, focusing on imaging spectrometers. We assess sensitivity of the retrieval to errors in atmospheric state estimation. Finally, we describe some promising avenues of future research to support the next generation of orbital imaging spectrometers.

Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites.

Retrieval of Hyperspectral Surface Reflectance Based on Machine Learning

Many methods based on radiative-transfer models and empirical approaches with prior knowledge have been developed for the retrieval of hyperspectral surface reflectance. In this paper, we propose a novel approach for atmospheric correction of hyperspectral images based on machine learning. A support vector machine (SVM) is used for learning to predict the surface reflectance from the preprocessed at-sensor radiance image. The preprocessed spectra of each pixel are considered as the spectral feature and hypercolumn based on convolutional neural networks (CNNs) is utilized for spatial feature extraction. After training, the surface reflectance of images from totally different spatial positions and atmospheric conditions can be quickly predicted with the at-sensor radiance image and the models trained before, and no additional metadata is required. On an AVIRIS hyperspectral data set, the performances of our method, based on spectral and spatial features, respectively, are compared. Furthermore, our method outperforms QUAC, and the retrieved spectra have good agreement with FLAASH and AVIRIS reflectance products.

Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA

The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform cloud detection and atmospheric correction. For the correction of aerosols effects, MAJA makes an estimate of the aerosol optical thickness (AOT) based on multi-temporal and multi-spectral criteria, but there is insufficient information to infer the aerosol type. The current operational version of MAJA uses an aerosol type which is constant with time, and this assumption impacts the quality of the atmospheric correction. In this study, we assess the potential of using an aerosol type derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis. The performances, with and without the CAMS information, are evaluated. Firstly, in terms of the aerosol optical thickness retrievals, a comparison against sunphotometer measurements over several sites indicates an improvement over arid sites, with a root-mean-square error (RMSE) reduced by 28% (from 0.095 to 0.068), although there is a slight degradation over vegetated sites (RMSE increased by 13%, from 0.054 to 0.061). Secondly, a direct validation of the retrieved surface reflectances at the La Crau station (France) indicates a reduction of the relative bias by 2.5% on average over the spectral bands. Thirdly, based on the assumption that surface reflectances vary slowly with time, a noise criterion was set up, exhibiting no improvement over the spectral bands and the validation sites when using CAMS data, partly explained by a slight increase in the surface reflectances themselves. Finally, the new method presented in this study provides a better way of using the MAJA processor in an operational environment because the aerosol type used for the correction is automatically inferred from CAMS data, and is no longer a parameter to be defined in advance.

A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain

Radiometric correction is an important issue in the quantitative remote-sensing community. By integrating dark object subtraction (DOS)-based atmospheric correction with physics-based topographic correction, a coupled land surface reflectance retrieval algorithm (coupled atmospheric and topographic correction algorithm, named the CAT algorithm) for rugged mountainous regions is proposed. Terra MODIS-derived atmospheric characterization data (including aerosol optical depth, integrated precipitable water, surface pressure, and ozone concentration) are employed as inputs for the proposed algorithm. A physics-based path radiance estimation model is proposed and embedded in the CAT algorithm, and band-specific per-pixel path radiance values are calculated. After the CAT algorithm was performed, the correlation between reflectance and terrain was dramatically reduced, with correlation coefficients nearly equal zero, especially for the near infrared and short-wave infrared bands, meanwhile the image information content increased over 20%. To provide a comparison with previous studies, two commonly used methods in the literature (DOS + Cosine and DOS + C) were employed. The results of the comparison show that the proposed algorithm performed better in both atmospheric and topographic corrections without empirical regression.

A Method for Consistent Estimation of Multiple Land Surface Parameters From MODIS Top-of-Atmosphere Time Series Data

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.