Soil models are widely used to characterize the reflectance properties of dry and wet soils. By considering detailed physical processes, the improved multilayer radiative transfer model of soil reflectance (MARMIT-2) model significantly improves the accuracy of simulating wet soil properties. However, the MARMIT-2 model relies on measured dry soil reflectance as an input, which limits its applicability in practical scenarios, especially when detailed information about specific soils is unavailable. To address this issue, this study first evaluated the ability of the general spectral vector (GSV) model of dry soil to represent the reflectance properties of dry soil. Then, we coupled these dry soil vectors with the MARMIT-2 model to propose the GSV + MARMIT-2 model. Finally, we assessed the accuracy of all three models using a wet soil database. The main conclusions of this study include: 1) the dry soil spectral vectors from the GSV model demonstrated high accuracy in describing the reflectance properties of dry soil, achieving an$R^{2}$of 0.988 and a root mean square error (RMSE) of 0.016. 2) All three soil models exhibited high fitting accuracy for the wet soil database ($R^{{2}} = \sim 0.992$and RMSE$= \sim 0.012$). Compared to the GSV and MARMIT-2 models, the GSV + MARMIT-2 model showed slightly improved accuracy under different soil moisture content (SMC) conditions. This study developed a more versatile and flexible soil model framework as it directly integrates the dry soil spectral vectors from the GSV model into the MARMIT-2 model. This coupling significantly expanded the applicability and improved the stability of the MARMIT-2 model.

Considering the simplicity of flight route planning, orthorectified images obtained from nadir observations are widely used in remote sensing. However, they are always insufficient to represent the anisotropic reflectance and three-dimensional (3D) structural information of objects. Therefore, multi-angle observation information can enhance target information and potentially improve the accuracy of target classification and recognition. In this study, we investigated the potential of anisotropic reflectance information in land cover classification. By employing the DJI P4M multispectral observation system, multi-angle multi-spectral reflectance images for five land cover types were captured at bare soil, concrete roads, grassland, apricot tree, and red broom cypress areas. Subsequently, the AFX-based BRDF archetypes model and the kernel-driven model were used to reconstruct the bidirectional reflectance distribution function (BRDF). Finally, land cover classification was performed using three types of machine learning algorithm considering different BRDF features and band combinations. The results indicate that, compared to nadir directional reflectance, multi-angle feature sets can improve the overall classification accuracy up to 24%. Compared to using single-band information, band combinations can also improve that up to 54%. The overall accuracy using the feature set of kernel-driven model parameters and nadir reflectance was also enhanced significantly, which can reach 86% using green-red-near infrared band combinations. This work demonstrates the contribution of multi-angle multi-spectral information to natural and artificial land cover classification.

The anisotropic reflectance characteristic (i.e., bidirectional reflectance distribution function (BRDF) effect) lays the foundation of quantitative remote sensing, while it is difficult to reconstruct at high spatial resolution due to near-nadir small-angle observations. Based on the traditional archetype inversion algorithm, this study explores and evaluates a two-step method considering vegetation structure clustering to retrieve fine-scale BRDF, using LESS simulated sufficient multiangle reflectances in forest scenes at multiple scales. First, full inversion of the kernel-driven model RTLSR_C was performed to investigate the BRDF feature at 1–50 m. Subsequently, the sensitivity of normalized difference vegetation index (NDVI) and anisotropic flat index (AFX) to forest structure was comprehensively analyzed. For reflectances at small view zenith angles (VZAs) of 0° and 15° close to Sentinel multispectral instrument (MSI) observations at 5, 10, and 25 m, forest structure cluster-based BRDF parameters were first retrieved based on accumulated intraclass reflectances, where prior BRDF information from 50-m LESS data and 500-m MODIS global representative sites were compared. Finally, cluster-based BRDF parameters were used as prior archetypes to retrieve pixel BRDF. Results show good fitting root mean square errors (RMSEs) for full inversion, with most average RMSEs below 0.05, and spectral and angular reflectance are sensitive to the fraction of vegetation cover (FVC), tree height, and crown length at 5 m and larger scales. In addition, the two-step method based on structure clustering yields a slightly higher BRDF inversion accuracy than that of the one-step method, and the traditional one-step archetype inversion algorithm demonstrates its simplicity and accuracy. This study provides new insights for fine-scale BRDF inversion.

Earth observation technology plays an important role in military reconnaissance, agriculture and forestry plant protection, emergency disaster relief, etc. In this paper, we mainly apply a prototype inversion algorithm: based on the multi-angle multi-spatial scale data of three scenes including trees, buildings and mixed objects simulated by the three-dimensional radiative transfer model LESS, we take the 500m/pixel coarse-resolution BRDF as an archetype priori information to reconstruct the high-resolution multi-angle information at 10m/pixel level. Then, sensitive feature indices are used to categorize the reconstructed 10m images, and the simulated 10m images are used as standard values to evaluate the classification accuracy. The results show that the average accuracies of BRDF inversion for the three scenes are 96.2%, 50.08% and 71.43%, respectively, and the plant class is more suitable for this inversion model. In terms of the classification accuracies, the three scenes are 60.52%, 96.84% and 76.60%, respectively, with building scene shows the highest accuracy.

The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.

The surface reflectance anisotropy is usually described by bidirectional reflectance distribution function (BRDF), and the BRDF archetypes extracted based on the Anisotropic Flat Index (AFX) have been used as the prior reflectance anisotropy knowledge of quantitative inversion. In this study, we introduce a new index, i.e., the Perpendicular Anisotropic Flat Index (PAFX), based on the AFX in a prior study, to refine the BRDF classification of the land surface. Based on the MODIS BRDF parameter products sampled at global intervals over multiple land cover types in 2015, we used AFX and PAFX as classification indicators to divide MODIS BRDF parameter space into some orthogonal clusters through use of the ISODATA (Iterative Self organizing data Analysis technique) Clustering Algorithm. As a case study, the MODIS BRDF parameters are divided into three classes by using the AFX and PAFX, respectively, in an orthogonal way, and therefore a total of nine cluster classes of BRDF parameters are generated, correspondingly generating nine BRDF archetypes. We use the average BRDF fitting error, algorithm complexity and classification accuracy to evaluate this classification method for classifying BRDF parameters, showing that the difference within the BRDF archetypes class is significantly reduced after the introduction of PAFX (RMSE = 0.0083) compared with only using AFX (RMSE = 0.0139), and the error for fitting all BRDF shapes with the average BRDF shape, i.e., the BRDF archetype presents a V-shaped trend as functions of AFX and PAFX, indicating an optimized minimum. In general, jointing the AFX and PAFX has improved the classification accuracy of the MODIS BRDF parameter space and is expected a further application in near future.

Clumping index (CI) field measurements based on the logarithmic gap fraction averaging (LX) method are widely used. However, some challenges regarding this method have been recognized; e.g., CI overestimation or underestimation occurs in the sampling units where there is no measurement gap, which creates major uncertainties in CI field measurements. To address this issue, we proposed an improved eight-connected LX method that replaces null gap units with the arithmetic mean of the gaps in the eight connected neighbouring units, considering the neighbouring connections of the natural foliage extension. To validate this method, we designed two controlled experimental schemes based on simulated digital hemispheric photography (DHP) images through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model considering the leaf area index (LAI) and leaf angle distribution (LAD), respectively, together with collected field measurements. The results showed that our method could almost prevent overestimation and improve underestimated CIs by nearly 20%. In addition, CIs of our method had the smallest error compared to the “true” CIs (error<0.1). In conclusion, our method can significantly improve the data quality of the simulation results relative to the existing methods and present potentials in the CI measurements of upcoming field campaigns.

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

Foliage Clumping Index (CI) is an important structural parameter within vegetation canopy, and current satellite-borne CI products mainly retrieved by using the linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD), while there is no directly model to calculate the CI of mixed forest. The objective of this paper is to propose a new method to calculate the mixed forest CI (MFCI) and access the ability to response the spatial heterogeneity of mixed forest pixels. The results show that: (1) The accuracy of MFCI is significantly higher than that of existing MODIS CI products, the average error can be reduced by 6.3% (2) the total sensitivity of MFCI to spatial heterogeneity is high(>0.6), and with the highest sensitivity to bare soil.

The surface anisotropy, usually described as bidirectional reflectance distribution function (BRDF), plays a key role in the quantitative remote sensing. Numerous BRDF studies are focus on sensors onboard polar-orbiting satellites such as POLDER and MODIS based on multi-angle sensors or accumulative observations through multiple days. Notably, sensors onboard geostationary satellites can also obtain multi-angle reflectances benefited from their high revisit frequencies, while by which only a few BRDF studies have been completed. In this study, we aim to evaluate the BRDF information collected from time-series directional observations of the Advanced Himawari Imager (AHI) onboard geostationary satellite Himawari-8. The multi-angle reflectances of 6 × 7 km POLDER and 0.05° AHI at mixed forest area during a whole month were collected. Generally, AHI has a good weight of Determination (WoD) of 0.03, as well as small fit-RMSEs of 0.0055 and 0.0235 in the red and NIR bands based on the kernel-driven Ross-Li BRDF model, which shows promising potential to provide BRDF information with a good quality.

Bidirectional Reflectance Distribution Function (BRDF) is an important component in quantitative remote sensing. In this study, we explored the relationship of sampling distribution and reconstructed BRDF curve for snow surface. In order to get enough observations, we utilize the field-measured data to analyze the different BRDF under different sampling pattern. This work can be very meaningful because it is hard to acquire sufficient measurements in all directions especially for snow in the real life.

Clumping index (CI) characterizes the clumping degree of vegetation canopy foliage relative to the random distribution, which is an important vegetation structure parameter. Digital Hemispherical Photography (DHP) is widely used in ground CI measurement and one of the key steps using this method is to determine the sampling resolution. This study presents a set of sampling way including 30 sampling methods with 17 levels of sampling resolutions. They were applied to four vegetation types and different growth stages of crops to explore the variation of CI with the decrease of sampling resolution of 17 levels. The results show that with the decrease of sampling resolution at 17 levels, the average increase of CI in the four vegetation types was 26%, 29%, 14% and 35%, and in different growth stages of soybean crop, the increase of CI was different which could be up to 60% at the maximum increase.

Clumping Index (CI) is an important vegetation structure parameter, which describes the grouping of leaves relative to the random distribution. Multi-angle data of the POLarization and Directionality of Earth Reflectance (POLDER) sensor (about 6×7 km) and the MODerate resolution Imaging Spectradiometer (MODIS) (500 m) are two main sources for global CI products. To better understand the variability inherent in CIs of such different spatial resolutions and optimize the used of CI products, extensive POLDER CIs and corresponding MODIS CIs were retrieved and compared in this study. Field measurements were conduct in one selected POLDER pixel in Hebei, China. Our results showed that POLDER and MODIS CIs presented relative good consistency (R2=0.65, RMSE=0.07, bias=0.003), and the correlation coefficient can reach 0.98 at the class level. Both POLDER CI (0.67) and MODIS CI (0.62±0.06) showed good consistency with field CIs (0.64±0.10) and POLDER CI was more likely to overestimate than MODIS CI.

Surface albedo is closely related to the Earth’s energy budget and is usually estimated by integrating remotely sensed bidirectional reflectance distribution function (BRDF) data based on the widely used Ross–Li kernel-driven models. However, for large solar zenith angles (i.e., SZAs > 70°), albedo estimation using the operational algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS), i.e., RossThick-LiSparseReciprocal (RTLSR), is not recommended because it is reported to somewhat underestimate the black-sky albedo (BSA) at large SZAs based on ground albedo measurements. Recently, various combinations of the Ross–Li BRDF models with improved capabilities have been developed, and the assessments of these models based on worldwide satellite BRDF data with good spatial sampling, particularly at the large view and solar angles, will be important to improve an understanding of their performance in estimating intrinsic albedos. Following previous studies, the objective of this study is to further assess a series of hotspot-corrected Ross–Li models by demonstrating their ability to fit the POLarization and Directionality of the Earth’s Reflectances (POLDER) data sets and estimate albedo, especially at large SZAs, based on selected concurrent POLDER and MODIS data. The hotspot-corrected RTLSR model obtained by combining the RossThickChen and LiSparseReciprocalChen kernels (RTLSR_C) shows the best fitting ability, with a high cumulative frequency of small root-mean-square errors (RMSEs), thus confirming previous conclusions. Model differences mainly appear in albedo estimates, especially BSA estimates at large SZAs. The BSAs estimated by other models are significantly different from the RTLSR_C estimates in the near-infrared (NIR) and red bands as the SZA increases to approximately 60° and 70°, respectively. In this case, RossThinChen-LiSparseReciprocalChen (RTNLSR_C) yields higher BSA estimates than those of RTLSR_C. Comparisons of the MODIS and POLDER albedos estimated with Ross–Li models show that models with the RossThinChen kernel yield higher BSA estimates than those of the RTLSR_C model as the SZA increases. The results indicate that the retrieved albedo is likely to be more accurate with appropriately selected kernels for BRDF models at large SZAs, providing guidance for selecting suitable combinations of multiple kernels.

A series reflectance models for snow have been developed in recent years, but there is no trusted pure snow database to test the models yet. The Polarization and Directionality of Earth Reflectances (POLDER) BRDF data have been widely used in quantitative remote sensing community, but the recent research has revealed that an obvious wrong classification has occurred in snow POLDER BRDF database. In this study, we propose a snow index (SI) to characterize the scattering feature of snow based on RTLSRS model and develop an effective method to identify the pure snow data in POLDER database. Finally, we build a snow database with higher purity, providing a data support for model validation in the future.

The Leaf Area Index (LAI) is a key structure parameter of plant canopy and is a basic input variable in various terrestrial ecological models. Previous studies indicate that the spatial heterogeneity and the mixture of different land cover types in the moderate resolution pixels will cause large errors in the retrieval of moderate resolution LAI product. Therefore, the influence of spatial heterogeneity should be corrected to retrieve a more reasonable LAI. In this study, we propose a method to improve the accuracy of moderate resolution LAI retrievals based on the mixed-pixel clumping index. The data simulated by the LESS model are used to validate the proposed method. Results show that the LAI estimated by the MODIS operational algorithm become smaller with the increase in spatial heterogeneity of pixel. The proposed method can correct the influence of mixed land cover types and spatial heterogeneity to obtain a more reasonable LAI, and thus shows the potential in generating the global moderate resolution LAI product with improved accuracy.

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth's Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations.

Anisotropy information from moderate-to-coarse-resolution sensors [e.g., 500-m Moderate Resolution Imaging Spectroradiometer (MODIS)] is widely applied to estimate high-resolution surface albedo. Simulated albedos using MODIS bidirectional reflectance distribution function (BRDF) parameters as prior knowledge based on the kernel-driven model are employed to build and assess the lookup table (LUT) of the direct-estimation method, which is then used to estimate high-resolution albedos directly from top-of-atmosphere (TOA) reflectance data (e.g., Landsat albedo). Previously, the errors in the simulated albedos were not considered in building and assessing the LUT. In this article, daytime time-series (30 min) of snow-free albedo measurements with sufficient solar zenith angles (SZAs) were introduced to build the LUT for snow-free Landsat TM surface shortwave broadband albedo (TM albedo) retrievals, together with TOA-simulated reflectance by concurrent daily MODIS BRDF parameters. The assessment utilizes an independent data set and shows larger discrepancies between the estimated and measured albedos [root-mean-square errors (RMSEs) of >0.03 at SZAs ≥ 60°] than those in previous articles. To reduce inconsistencies between the MODIS BRDF parameters and the observed albedos, as well as possible spatial resolution differences between the MODIS and Landsat data, we adopted a correction strategy that first linearly adjusts the MODIS BRDF parameters to match the albedo measurements by a magnitude method, and second, the TOA reflectance simulations were further corrected by concurrent TM reflectances. The developed algorithm shows a significant improvement after using such corrections as a priori (RMSE <; 0.02 at SZA ≤ 75°). The validation indicates improved accuracies in the TM albedo estimation. These improvements may provide potential albedo estimations for nadir-viewing high-resolution sensors using coarse-resolution anisotropy information.

Surface albedo is a key parameter controlling the earth energy budget, which can be estimated by integrating the Bidirectional Reflectance Distribution Function (BRDF). The semi-empirical kernel-driven BRDF models has been widely used in BRDF/Albedo products, MODIS products for instance (Schaaf et al., 2002). However, these albedo products are suspect under large SZA (Liu et al., 2009). With the hotspot improved kernel-driven models, it is necessary to assess the property of these models. In this study, two POLDER datasets are utilized to access these models by root-mean-square error (RMSE) and relative RMSE (RMSE_r). Then, cross-comparison between albedos estimated by improved models under several SZAs is analyzed by POLDER and the concurrence MODIS pixels. This study is aimed at choosing suitable models for albedo estimation under different angular situations to retrieve more accurate albedo.

Leaf area index (LAI) is an important structure parameter to illuminate the fractions of solar radiation absorbed, transmitted and reflected by the plant canopy, and also a useful reference for ecological and meteorological modeling. The GLAS full-waveform Lidar data of ICESat satellite are easily available and global coverage, which can also provide detailed forest canopy structure information in the GLAS footprint. In this study, we show a LAI estimation method from the GLAS waveform Lidar data at footprint level. Firstly, Gaussian decomposition method is used to process the raw GLAS waveform data to identify ground echo energy and canopy echo energy. In addition, the optical height threshold (HT) to separate the canopy and ground in the GLAS waveform has been discussed, and the result show that 3 m is the optical HT in our study area. Secondly, a reflectance correction method is used to calculate the laser penetration ratio (PC) of forest covered GLAS footprints based on the ground echo energy and canopy echo energy. Thirdly, the relationship between the between the field-measured LAIs and PCis constructed based on the Beer-Lambert law. The determination coefficient (R2) is 0.69 and the root mean square error (RMSE) is 0.64. The performance of the GLAS-derived LAIs is also evaluated using the 20 field-measured LAIs. The result indicates that the GLAS-derived LAIs have a high accordance with the field measurements (R2=0.67, RMSE=0.52). The result suggests that the GLAS waveform data can be used to retrieval LAI for various ecological applications.

Recently, Jiao et al. developed a snow kernel in the kernel-driven bidirectional reflectance distribution function (BRDF) model framework to better characterize the anisotropic reflectance of pure snow surface. In this study, we assess performances of this snow kernel in the kernel-driven model framework and original kernel-driven model (hereinafter named the RTS and RTR models) in retrieving snow albedo based on the bicontinuous photon tracking (bic-PT) model. Our results show that: (1) The spectral albedo retrieved by these two models has a high consistency with the bic-PT model. However, the results of the spectral albedo for RTR model has a slight underestimation, especially at SZA=0° in the visible bands, and the RTS model performs well compared with simulated data. (2) The albedo retrieved by these two models agrees reasonably well with the simulated data (R2=~0.9). Yet, the result of the RTR model underestimates 0.50% and 0.52% compared simulated albedo in the red and near-infrared bands, respectively, and the RTS model has a negligible bias for all bands. This assessment provide a priori knowledge of these two models performances, and thus, suggests the RTS model can be applied to future researches of estimating snow albedo.

The foliage Clumping Index (CI) is a key vegetation structure parameter for leaf area index (LAI) estimating and ecological modelling. Previously, several global CI products have been retrieved from the Collection V005 Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) products with a temporal resolution of one year, one month or 8 day. The Collection V006 MODIS BRDF products provide a chance to retrieve a global CI product with higher temporal resolution and data accuracy. However, the large size of the Collection V006 MODIS BRDF products (~150 terabyte from January 2001 to December 2017) and the retrieved CI products (~9 terabyte) increases the difficulty in retrieving and publishing the global CI product. In this study, we develop a software tool that enable users to produce CI product of their desired date, region and temporal resolution based on the Collection V006 MODIS land cover type and BRDF products. The software tool reduces the requirements of the processing and storage capacity for researchers and thus facilitate the publication and widespread application of the CI product.

The linear kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was originally developed for modeling the simplified scenarios of the continuous and discreet vegetation canopies, and has been widely used to fit the multiangle observations for the vegetation-soil system of the land surface in many fields. However, there is a need to develop this model to characterize the light scattering properties of snow, which tends to exhibit strongly forward scattering behaviors. This study proposes a snow kernel to describe the reflectance anisotropy of snow, mainly based on the asymptotic radiative transfer theory (ART) for a semi-infinite weakly absorbing layer of snow, and then applies this kernel to the framework of kernel-driven BRDF model. This snow kernel adopts the analytic form of the ART model with an improved ability in forward scattering direction, particularly in a case of a large viewing zenith angle (> 60°) where the simulation accuracy of the ART model somewhat decreases in the principal plane (PP). Validation of this method was implemented using observed multiangle data. Pure snow targets were selected from the entire archive of the POLDER BRDF data. This validation demonstrates that this proposed snow kernel in the framework of the kernel-driven RTLSR model show potentials for many potential applications, particularly in the field of Earth's water cycle and radiation budget where snow cover plays an important role.

Clumping index (CI) is an important vegetation structure parameter in the estimation of leaf area index (LAI) and the modeling of ecological and meteorological process. With the development of surface process modeling and remote sensing technology, high resolution CI product is urgently needed but no appropriate high resolution multi-angle reflectance satellite data is currently available to produce such product. In recent years, random forest algorithm has been widely used in the derivation of high resolution products from remote sensing data. In this study, the random forest algorithm was used to estimate Landsat CI basing on MODIS and field data. The developed predictive model was validated using 26 field measurements and the predicted CI shown a good consistency with the field CI (R2=0.63, bias=0.005, RMSE=0.10).

The surface anisotropy plays a key role in the quantitative remote sensing inversion, which is usually described as bidirectional reflectance distribution function (BRDF). Studies show that BRDF sampling has a significant effect on parameter inversion such as albedo. However, BRDF samplings are complex, and only specific samplings were considered in previous studies. In this study, we investigated the sensitivity of BRDF sampling to albedo and the normalized difference between hotspot and dark spot (NDHD) angular index based on the kernel-driven Ross-Li BRDF model. Albedo and NDHD calculated by a set of dense sampling airborne data were used as the reference data, and inversion results from many sparse samplings were compared to the reference results. The result shows the overall number, plane, range and symmetry in observing condition of BRDF sampling can affect albedo and NDHD estimation. Among typical sensors, POLDER shows best sampling while Landsat shows largest errors.

The hotspot effect is a typical angular reflectance signature of vegetation canopies and contains important information for the retrieval of vegetation structural parameters. To date, the hotspot effect of various analytical bidirectional reflectance distribution function (BRDF) models (e.g., the PROSAIL model) has rarely been assessed by multiangular measurements with sufficient hotspot observations due to the lack of accurate hotspot measurements (for field measurements) or appropriate methods (for airborne and spaceborne measurements). In this paper, we develop a method to further improve the hotspot effect of the kernel-driven model and design a framework to utilize the improved kernel-driven model as a bridge to assess the hotspot effect of the PROSAIL model with Polarization and Directionality of the Earth Reflectance (POLDER) hotspot observations. The results indicate that the proposed method further improves the fits between the models and the observations in the vicinity of the hotspot direction, particularly in the rare situations where the geometric-optical scattering component governs the target reflectance. In addition, the hotspot signature indicated by the PROSAIL multiangular data shows a larger variability than that of POLDER observations. C1and C2in the improved kerneldriven model can be used as benchmarked parameters to qualify the amplitude and width of the hotspot effect for the simulated multiangular data of physical BRDF models and thus present the potential for the assessment and analysis of the hotspot effect of physical models, which, in return, helps retrieve the structural parameters of vegetation canopies from hotspot signatures.

It has been a hot study field to extract forest structure parameter using Airborne LiDAR. Since footprints of Airborne LiDAR data are discontinuously distributed with small data coverage, therefore, it is impossible to obtain the forest structure information of continuous region using Airborne LiDAR data alone. The MODIS BRDF shape indicators contain the information regarding 3-D structure of forest and have the possibility to retrieve the structural parameters of forest. In this study, we select Howland Forest, Harvard Forest, La Selva Forest and Bartlett Forest as experimental areas, and aim to construct a canopy height estimation model from the airborne Laser Vegetation Imaging Sensor (LVIS) data and MODIS BRDF shape indicators. Firstly, H100 canopy height was extracted from the LVIS data and the MODIS BRDF shape indicators were calculated based on MODIS data. Secondly, using the Random Forest algorithm to develop a canopy height estimation model with H100 canopy height data and MODIS BRDF shape indicators. Finally, 10-fold cross-validation method is used to evaluate the accuracy of the model, and the validation results show that the MODIS BRDF shape indicators can be estimated forest canopy heights in high accuracy.

The foliage Clumping Index (CI) quantifies the level of foliage grouping within a distinct canopy structure relative to a random distribution. It is a key structure parameter for the ecological, hydrological, and land surface models. In this study, we investigate the influence of snow cover on the seasonal variation of global CI products derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) parameter products using the improved RTCLSR kernel-driven model. Results indicated that the cover of snow can lead to a much larger CI and thus considerably decrease the quality of the CI product. Statistics in 2006 indicates that more than 85% low quality pixels are covered by the snow. The average CI for evergreen needleleaf forests in winter will decrease about 0.1 after deducing the influence of snow covered pixels. The influence of snow cover should be carefully considered and corrected when analyzing the seasonal variation of the global CI product.

The accuracy of hotspot signatures is crucial to the development of algorithms for the retrieval of various biophysical parameters of terrestrial surface targets. The hotspot effect determined by the kernel-driven RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model fully relies on the performance of the two kernels in modeling the hotspot effect. Previously, a method has been developed to correct the volumetric scattering component of the RTLSR model (Jiao et al., 2016); however, in few cases that the weight of the volumetric scattering component (fvol) is no longer significant (e.g., fvol= 0) in the framework of the RTLSR model, the slight underestimation of the hotspot effect still exists. In this study, we propose a method to enhance the overlap function inherent in the geometric-optical (GO) kernel using a physical hotspot factor. The hotspot observations extracted from the entire archive of the POLDER-3 BRDF database are used to determine two parameters of this hotspot factor. Result shows that the proposed method further improves the model-observation fits in the vicinity of hotspot direction, particularly in some extreme cases where the GO scattering component is fully dominant in the multiangle measurements. Such an improved GO kernel, combining with the hotspot adjustment method for the volumetric scattering kernel, reconstructs hotspot effect more accurately; therefore, necessarily further improves the performance of the kernel-driven BRDF model particularly for the retrieval of the canopy structure parameters that is associated with the hotspot effect.

Semi-empirical kernel-driven Bidirectional Reflectance Distribution Function (BRDF) model has been developed to retrieve the BRDF shapes of the observed surface from multi-angle measurements. At present, hemispherical diffuse irradiance is usually blended in the multi-angle measurements. The blend of diffuse irradiance will smooth the intrinsic BRDF shapes of observed surface. Therefore, there is a need to correct the diffuse irradiance to get the ideal BRDF shapes when the multi-angle measurements is processed by the kernel-driven model. In this article, we develop a method for kernel-driven model to correct the blended hemispherical diffuse irradiance in the multi-angle measurements. Multi-angle data simulated by the PROSAIL model are used to validate the efficiency of the method. The result indicates that the simulated reflectance after correction agree well with the measurements without diffuse irradiance.

Previously, a few bidirectional reflectance distribution function (BRDF) archetypes were distilled from the routine MODIS BRDF product for capturing the major variability of anisotropic reflectance of a large number of land surfaces from MODIS, based on the RossThick-LiSparseReciprocal (RTLSR) model. Since the routine RTLSR BRDF model tends to underestimate the hotspot effect, the resulting hotspot signatures of these MODIS BRDF archetypes are underestimated to some degree inevitable. In this study, we use the entire available POLDER hotspot data as a priori to optimalize two hotspot parameters regarding hotspot height and width in a new hotspot-corrected RTLSR model. Then, the corrected model with the optimal hotspot parameter values is used to reconstruct the hotspot effect of the MODIS BRDF archetypes. This study assumes that hotspot signatures are not largely related to the overall pattern of the anisotropic reflectance provided with the routine MODIS BRDF product, particularly for MODIS that rarely acquires hotspot observations; therefore, the BRDF parameters retrieved by using the routine RTLSR model can be used as a baseline to generate hotspot effect in conjunction with the hotspot-corrected RTLSR model that fully inherits the property of the RTLSR model except for hotspot effect.

The surface anisotropy characteristics have important significance in the quantitative remote sensing inversion. The kernel-driven model can express the surface anisotropy well and widely used in remote sensing, and the PROSAIL model is a mature vegetation canopy model which can describe complex vegetation structure, therefore studying surface anisotropy variance of the two models is a key point to combine them for further research. We simulate surface reflectance data with complex vegetation structure through the PROSAIL model, with the RossThick-LiSparseR(RTLSR) model and its extended model of Chen(RTCLSR) considering hotspot effect, we analyze anisotropy variance. The result shows: (1) The overall fitting effect is good, the average fitting RMSE is about 0.0071 in red band and 0.0342 in near infrared band; (2) AFX is sensitive to some vegetation structure parameters; (3) C1 and C2 in Chen model is inversely proportional to each other in different Hspot, while proportional in different LAI.

Many previous studies attempted to extract prior reflectance anisotropy knowledge from historical Bidirectional reflectance distribution function (BRDF) product based on normalized difference vegetation index (NDVI) data. However, the correlation between reflectance anisotropy and NDVI is still controversial. This study used BRDF archetypes to represent different reflectance anisotropy, and analyzed the correlation between reflectance anisotropy and NDVI based on five-year time series MODIS BRDF data. Nadir reflectance and NDVI retrieved from different BRDF archetypes and same multi-angular observations were also compared with each other to further study this correlation. Results show that the six BRDF archetype classes are all contained in any range of NDVI with one BRDF archetype class accounting for a maximum of 40%. Nadir reflectance and NDVI retrieved from different BRDF archetypes have little difference with each other at different solar zenith angles. NDVI is not a reliable clue to distinguish surface reflectance anisotropy.

Normalized Difference Vegetation Index (NDVI) is one of the most widely used vegetation indexes because the NDVI of typical type of land coverage has a clear distinction on a large-scale image, especially has a valid highlight for vegetation. Anisotropic reflectance characteristics of natural land surface affect the retrieval of NDVI, which means the variation of solar zenith angle and view zenith angle leads to different values of NDVI. This paper uses kernel-driven models to simulate the reflectance of different solar and view zenith angles to calculate the corresponding NDVI values and contrasts the influences of bidirectional reflectance on NDVI under different degrees of the coverage of surface.

The semi-empirical kernel-driven Bidirectional Reflectance Distribution Function (BRDF) models have been widely used in many remotely sensed BRDF/albedo products such as MODIS products for their simplicity and physical interpretation[1]. Based on anisotropic flat index (AFX) derived from the model[2], magnitude inversion algorithm which takes BRDF archetypes as prior knowledge have been proposed[3]. In order to validate the ability of AFX to indicate land surface anisotropy, the relations between AFX and BRDF shapes of several typical land cover types are described in this paper. Besides, we compare the values of prototype inversion algorithm based on AFX with the original full inversion algorithm. Finally, we study relations between AFX and LAI with the simulation data of the two-layer canopy reflectance model of KUUSK to apply AFX, which may guide future research of retrieving the vegetation parameters.

In this study, based on kernel-driven linear BRDF model, a new spectral vegetation index named anisotropic flat index (AFX) and a hotspot kernel function are described. Anisotropic Flat Index (AFX), which is created by normalization of net scattering magnitude with the isotropic scattering, can summarize the variability of basic dome-bowl anisotropic reflectance pattern of the terrestrial surface. The hotspot kernel function is modified with the exponential approximation to generate a so-called RossThickChen kernel (KRTC). Using the POLDER-3 multi-angular observations, a classification scheme for BRDF typology is created and a BRDF archetype data is established. The results show that the AFX effectively summarizes BRDF archetypes that provide additional information on vegetation structures and other anisotropic reflectance characteristics of the land surface. The RTCLSR model can significantly capture the hotspot signatures, the BRDF archetypes derived in this way provides a significantly different hotspot signatures from those derived from the MODIS BRDF product.

Bidirectional Reflectance distribution Function (BRDF) archetype database[1] briefly summarize reflectance anisotropy into six BRDF archetypes. To evaluate the representation of BRDF archetypes from reflectance anisotropy, the shapes of BRDF archetypes are compared with according MODIS product; then the albedos and the Root Mean Square Errors (RMSE) retrieved from BRDF archetypes are compared with MODIS retrievals, Comparisons show that the shapes of BRDF archetypes agree well with the according MODIS BRDF, and the albedos and RMSEs of BRDF archetype retrieval are close to MODIS product. These archetypes can represent the characteristics of reflectance anisotropy in the retrieval of albedo.

The RossThick-LiSparse-Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model has been developped to derive the operational Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product due to its simplicity and the underlying physics; however, early research showed that this model deficiency mainly comes from its underestimation of the hotspot directional signatures near the Sun's illumination direction. In this paper, we developed an approach to improve the hot spot effect for the RTLSR model by improving the volumetric scattering kernel with an exponential approximation of the hot spot kernel (Chen and Cihlar, 1997). Compared with the RTLSR model and the further-developed model that modify the hotspot directional signatures of RTLSR model based on the calculation of an overlay function of the intersection of viewed and sunlit leaf areas (Jupp and Strahler, 1991, thereafter named RTJLSR), this newly-corrected model that modifies the hot spot effect of RTLSR model based on the theory of calculation of a canopy gap size distribution function (Chen and Leblanc, 1997, thereafter named RTCLSR) preserves the linear form of kernel-driven model, but flexibly adjust hotspot magnitude and width through two additional parameters C1 and C2. Initial validation result with airborne cloud absorption radiometer (CAR) data shows that the RTCLSR model can significantly improve the model-observation fits in hotspot region. In near future, we will focus on determining C1/C2 values from spaceborne POLDER BRDF database provided by the POSTEL Service Centre. With C1/C2 are predetermined, the newly-correctly RTCLSR model is promissing for global application with a minor update from the RTLSR model.

A method to derive land surface albedo from near nadir reflectance and a priori BRDF archetypes is presented. It uses kernel-based bidirectional reflectance distribution function (BRDF) models, but assumes a priori knowledge of underling surface BRDFs can be used according to Bayesian inference theory to yield a posteriori estimations of unknown kernel weights based on a BRDF classification. First, MODIS products are used to determine the best weight between observations and a priori knowledge. Then, a lookup table for the weight is built between sun zenith angle, BRDF classification and spectrum bands. Finally, we evaluate the ability of this method to retrieve albedos through 1577 sets of Polder observations which have near nadir (less than 3 degree) reflectance. The results show that, POLDER albedos retrieved from nadir reflectance and Bayes inversion method agree with criterion albedos retrieved from all POLDER observations. Compare to Lambert albedo, this method could improve accuracy of albedo by 5 percent.

Kernel-driven model has been widely used in operational production including RossThick-LiSparse-Reciprocal model in MODIS (Lucht et al., 2000) and RossThick-Roujean model in POLDER (Roujean et al., 1992). Both the operational models have used the RossThick as the volumetric kernel. However, RossThick kernel does not count for the so-called hot spot effect. In this paper, analyses have been investigated into the hotspot effects for two models. The research aims to further study the modified classic kernel-driven models' performances in the principal plane, particularly in the retro-solar direction using Bréon et al. hot spot factor (Bréon et al., 2002). The results show that the factor indeed improves the hotspot modeling of RossThick used in kernel-driven models. However, the factor is somewhat not so suitable for land cover type without strong hotspot effect.

In this study, we develop a new technique to derive a prior database of archetypal BRDF shapes from accumulated ground measurements based on a newly developed angular index named anisotropic flat index (AFX). Through the analysis of characteristics of the semi-empirical, kernel-driven, linear BRDF models, we find that the anisotropic reflectance patterns of land surface at different wavelengths are actually determined by a net scattering magnitude that is dependent of two basic spectral scattering types, volume scattering and geometric-optical surface-scattering. A further normalization of this net magnitude by isotropic scattering results in a new anisotropic flat index (AFX) that can indicate basic dome-bowl anisotropic reflectance patterns of terrestrial surface. This trait makes it possible to support a novel method to acquire some archetypal BRDF shapes, rather than directly based on conventional land cover classification schemes. The sensitivity of the derived BRDF archetypes is initially examined by cross-comparison of the AFX and other variables including model parameters, white sky albedo (WSA) and the NDVI.

A method to derive land surface albedo based on a prior archetypal BRDF (Bidirectional Reflectance Distribution Function) database is presented. The algorithm was based on kernel driven BRDF models, the 69 sets of field observations were classified into four classes according to AFX (Anisotropic Flat Index) which can indicate basic dome-bowl anisotropic reflectance patterns of terrestrial surface, and then the archetypal BRDF shapes database was created. In the inversion of surface albedo, we fit the observations using the four archetypal BRDF shapes respectively to select the shape that has least fitting error as the underlying surface anisotropy prior knowledge. The archetypal BRDF shapes do not depend on land cover. An albedo datasets for air-borne WIDAS is produced with this scheme. At last, we obtained the shortwave spectral albedo of WIDAS in the Yingke station in WATER Campaign. Comparison of the albedo with field observations shows that the absolute error is less than 0.05. This study will provide a possible method for space-borne albedo retrieval which lacks sufficient multi-angular observations.

Algorithm for Model Bidirectional Reflectance Anisotropics of the Land Surface (AMBRALS) has been developed for scientific user community by Lucht et al. since 1995 as a surrogate for the operational MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo code. It is a family of kernel-driven BRDF models including the operational MODIS BRDF/Albedo main algorithm, and. adopts command line format. In this study, we develop a visual Window's interface for AMBRALS named VAMBRALS based on the core codes of the AMBRALS to keep consistent with AMBRALS with a series of improvements and innovations. First, VAMBRALS has partly realized image data processing function, initially defined a temporary image converting format file. Second, VAMBRALS has developed a new ability to forwardly calculate reflectance and black-sky albedo at arbitrarily given view and illumination geometry. Third, VAMBRALS has accomplished hybrid programming of C++ and Interactive Data Language (IDL) to visualize model kernel functions and realistic BRDF observations.

Prior knowledge can significantly improve the retrieval of surface spectral albedo from satellite observations. This paper compares two methods that derive HJ-1 surface albedo in Heihe region by using prior knowledge based on kernel-driven BRDF model, with that derived by assuming Lambertian surface. The first algorithm (algorithm I) uses the backup algorithm of operational MODIS BRDF/Albedo product; the second algorithm (algorithm II) is developed by Li et al. (2001) that bases on the Bayesian inference theory to use prior knowledge from sets of field measurements. Our results show that both algorithms reduce the relative error by up to 10%~12% in the red and near-infrared band. Further analysis shows that the albedo would be retrieved with higher accuracy if view zenith angles provided by satellite sensor are larger than that of HJ-1.

An anisotropic flat index (AFX) is among the operational BRDF and albedo products offered in both the V004 and V005 reprocessed versions. We examine this BRDF shape indicator with 20 ground multiangular data sets as well as MODIS satellite samples, and find that the AFX routinely captures the BRDF shape. An AFX1.0 corresponds to a bowl shaped anisotropy pattern. For green vegetation, the BRDF shape is related to canopy architecture. Therefore, the behavior of the AFX provides an opportunity to infer canopy structure of the surface cover that produces the anisotropic effect.

The reflective spectrum of winter-wheat shows different features with different observed scale, which is important to accurate application of remote sensing. But in many cases, people didn't consider that and the errors were resulted. In this article, firstly the definition of three-scale: material, endmember and pixel was further explained based the previous definition, and the ground measuring data and Omis aerial image of winter wheat of Shunyi, a city in Beijing, explained the difference of three-scale spectrum. To understand the relationship between three-scale spectrums, two methods were used: physical model and statistical method. At the canopy scale, the wheat can be thought as the mixture of leaf and soil. This mixture is nonlinear, the SAIL model, which is based on radiation transfer formula, can be used to simulate the canopy spectrum with the leaf spectrum as input. Mixed pixels and atmosphere effect are the main factors for the difference between canopy and pixel spectrum. But at aerial scale, the wheat canopy is homogeneous and we found for the Omis image, the spectrum of wheat in the same region with changing window is changing in very small extend.

The paper focuses on interpreting the different spatial relationships between NDVI and Ts, a triangular or a trapezoid, and analyzing transformation condition and the physical connotation and ecological meaning of the vegetation index-surface temperature feature space. Further, using the Temperature-Vegetation Dryness Index (TVDI), we explain the existent meaning of a triangular shape after NDVI arrives at saturated state (NDVI=1) by analyzing the relationship between NDVI, LAI and evapotranspiration. The specific relations between NDVI and Ts will help us validate and update land surface models well.

How to find and make use of an up-scaling method which can transform the high-resolution image to the low-resolution and can keep the information such as the space relation, the spatial pattern and the fragmental degree which are expressed by the new image just like the reality is the core problem and must be solved in the field of remote sensing. On one hand, the paper compares the spatial analysis method in the landscape ecology with the latest histo-variogram method which was presented by Hao Zhang, et al in 2002 and then analyzes their advantages and shortcomings of their own. On the other hand, we introduce the concept based on histo-variogram and fractal theory: the turning point of the scale. On the basis of the turning point, we can proceed with the scale-transformation piecewise. How to find a feasible scale transformation algorithm between different scaling extents is the main idea in the study. Although Semi-variogram is much more intricate and full of ecological meaning, it shows good potentiality in this aspect. The image for the study is scenes of Tian-an-men district of IKONOS with 1 m resolution.

This paper presents a general method and some preliminary results on validating MODIS albedo products, and how albedo changing in the winter-wheat growing period. The available albedo observations are with different scales, including ground measurements and MODIS Albedo products. The study results show that the winter wheat's albedo of both the satellite observation scale and ground measurements scale have the same trend at every growth stage

In our former research, we raised the concept of histo-variogram to describe the spatial heterogeneity among the Earth objects compactly based on the analysis of the characteristics of other spatial analyzing methods such as variogram information entropy. Just like all the other nature phenomenon, the spatial heterogeneity is also scale dependent. That is, the heterogeneity of one kind of Earth scene at a certain scale may become homogeneous at the other scale and vice versa. In current research, we want to find: (1) if there exists one scale scope in which the heterogeneity keeps relative stabilization; (2) if the heterogeneity of a specific Earth scene has regional coherence. This is very important for model selection or parameter adjustment during inversion of quantitative remote sensing. We use the concept of "total fractal dimension" that arose in our former research to measure the heterogeneity of the Earth scene. The data source we used is stochastic sampled sub-regions from LUCC of Beijing district with different scales. The preparatory result shows that the heterogeneity of different Earth scenes has its specific scale scope and heterogeneity of most Earth scenes have regional coherence.

Kernels are always pre-determined in current kernel-driven model applications, but they seem to have some disadvantages in the requirement for more accurate remote sensing because one kernel combination is used for the inversion of all land cover types. In this paper, we use 28 different multi-angular data sets, which represent major types of land cover, to find the relations of different kernel selections with land cover types. The kernel combinations in the models we compare are volume kernels of Ross-Thick, Ross-Thin and geometric optical kernels of Li-Transit, Li-SparseR and Li-Dense. The airborne multi-angle TIR/VNIR image system (AMTIS) data set, which was obtained in Shunyi county of Beijing, China in April 2002, was used for the inversion. The inversion results of pre-determined kernel selections and class-based kernel selections are compared.

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

Multi-angular remote sensing became a hot topic after the non-Lambert characteristic of the Earth's surface had been accepted popularly. A large amount of multi-angular remote sensing data has been obtained with the launch of multi-angle remote sensing sensors. Therefore, modeling of the bi-directional reflectance distribution function (BRDF) for the Earth objects is one of the main subjects at present. A large satellite-airborne-ground synchronous remote sensing experiment was carried out during March 29 to May 10, 2001 at Shunyi, China. The main observation target in this experiment is focused on winter wheat. To describe the BRDF of winter wheat in its early growing stages, we propose a geometric-optical model that is suitable for sparse vegetation, and then try to retrieve the structure parameters based on this model using the field measurements. The purport of the model and its inversion is to inspect the ravages of drought on the wheat just as it is turning green. The winter wheat in our measurement field is sparse and disperses without clear row structures in its turning-green stage. Typical row structure based models and uniform structure based models are not suitable. Our model is developed based on the Li-Strahler geometrical-optical model proposed in 1985. Each cluster of wheat is treated as a hemi-ellipsoid in this model. All of the leaves in the cluster are assumed to cover the hemi-ellipsoid randomly. Leaf area index and leaf angle distribution are two important parameters that are related to the surface area of the hemi-ellipsoid and the leaf distribution on this surface respectively. Leaf angle distribution is also related to the shape of the hemi-ellipsoid. Due to the large uncertainty of the number of hemi-ellipsoids in a unit area, we retrieve this parameter based on our model using the most sensitive samples first, and then treat it as a priori knowledge in the later inversion. The next stage is studying how to use multi-angle remote sensing data to invert vegetation structure parameters.