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.

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.

Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial–temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.

In this study, a temporal shape–based fusion method using a spatially and temporally moving window is proposed to incorporate time lag of fine and coarse resolution observations, and to fully utilize target fine resolution pixel and similar coarse resolution pixels in the process. This method provides high accuracy fused images with Pearson’s r of ~0.95, root mean square error of ~0.04, and bias of ~0.01 for commonly used fine spatial resolution satellites, including Landsat 7 and 8, Sentinel 2, and Gaofen 1, over different heterogeneous regions, such as urban, mountain, forest, and savanna regions. The fused fine resolution Enhanced Vegetation Index (EVI) time series using different fine spatial resolution satellites data as input are all highly correlated with the PhenoCam monitored green chromatic coordinate, with no temporal lag. Compared with commonly used data fusion method, this method provides equivalent and slightly higher accuracy because both neighboring similar pixels and the annual temporal variation are fully considered. This temporal shape–based fusion method does not require each input fine resolution image to be cloud-free; therefore, it can be used at a large spatial scale without further preprocessing and generates continuous datasets over a long-time range with only one input preparation process. The factors that could affect the method accuracy are the cloud detection accuracy of fine resolution data and the temporal continuity of the coarse resolution data. The method may also be used to produce spatially and temporally continuous surface reflectance and other surface reflectance derived indices.

To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000–2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean$R = 0.851$) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean$R = 0.776$) and LMGM (mean RMSE = 0.074; mean$R = 0.734$). For the 12 years (2000–2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and$R = 0.938$. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications.

Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.

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.

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.

Leaf chlorophyll content (Chlleaf) is a crucial parameter in carbon cycle modeling and agricultural monitor. Taking advantage of remotely sensed red-edge vegetation index (VI) is an easy approach to estimate Chlleafat a large spatial scale. However, the spectral signals of Chlleafand other canopy/foliar/background factors (e.g. leaf area, leaf angle, soil moisture, etc.) are always coupled together, leading to the relatively low accuracy in direct Chlleafestimation. A new chlorophyll sensitive index (CSI) based on the red-edge band is proposed to estimate Chlleaf, with minimal canopy structural influences. Validation results using in-situ measurements show CSI performed better to estimate Chlleafof winter wheat and soybean at canopy scale: RMSE=8.24μg/cm2for CSI; RMSE=10.04 μg/cm2for the best existing index, MTCI. CSI also has the potential ability to estimate Chlleafacross diverse structural species types with high accuracy. Therefore, CSI provides an effective and convenient way to estimate Chlleafover large areas using satellite data.

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.

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.

Land surface albedo is one of the key parameters in the radiation budget, the hydrological cycle and climate modeling studies. It is wildly known that large errors may occur in the estimation of surface albedo without taking into consideration the anisotropy reflectance effect, which is a general feature of the earth surface. In the present study, a new approach that utilizes prior BRDF knowledge, which is extracted from MODIS BRDF product, for estimating surface albedo has been proposed. The density plot of MODIS BRDF model parameters shows model parameters was in obvious assembled distribution. The prior BRDF knowledge was extracted from the most concentrated (top 60 percent) area. Results show that the prior BRDF-based albedo from simulated nadir reflectance enables 90 percent of all pixels have the absolute difference between -0.02 and 0.02.

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.

Due to the high spatial and spectral resolution, hyperspectral data have attracted many researchers' attention. Field Imaging Spectrometer System (FISS) is a newly hyperspectral sensor with high quality spectra in visible bands. But the spectral reflectance in near-infrared band is much lower than the normal spectra. In order to calibrate the FISS image data, the ASD spectra of locust leaves are applied to PROSAIL model for validation. By dividing the data into two parts, the visible band and near-infrared band, the parameters of PROSAIL model are obtained by finding the best match of visible band. Then, the parameters are applied to the near-infrared band for validation and thus applying the method to FISS image data for calibration. The results show that this method can calibrate the data effectively and make the data more practical.

Into the 21st century, the human exploration of the moon increasingly frequent, China, the United States, Russia, India, Japan and other countries have joined the ranks of lunar exploration which provided a lot of data. And the platforms of Digital Moon are rewritten based on third-party plug-ins, which is poor in real-time, operability and interaction. On this research the web platform of Digital Moon is built based on WebGL specification for rendering 3D graphics within any compatible web browser without the use of plug-ins. So that the paltform is suitable for many kinds of browers, and it is convenient to the public to understand the moon and help the scientist to explore the universe.

Land surface albedo is one of the crucial parameters affecting the land surface energy. The radiation reflected by earth's surface is anisotropic, and it's one of the main factors that affect the accuracy of albedo retrieval from remotely sensed observations. Based on the MODIS BRDF/albedo product and the six archetypal BRDFs, this study learned the effect of reflectance anisotropy on albedo retrieval. First, the six archetypal BRDFs were used to fit the multi-angular observations or the nadir reflectance by the least square method, then the retrieved albedo were compared with the real surface albedo to describe the effect of reflectance anisotropy on albedo. Results show that the effect of reflectance anisotropy on albedo is closely related to the pattern of reflectance anisotropy and the observation geometric. When the multi-angular observations are sufficient and well distributed, the differences between the albedos retrieved from various BRDF archetypes are unremarkable. Meanwhile the intermediate BRDF archetype (No.3 or No.4) or the mean MODIS BRDF has an ability to improve the accuracy of albedo retrieval from insufficient observations.

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.

Inversion of FeO and TiO2by passive microwave radiometer not only provide support for high-precision quantitative inversion of lunar surface material component by optical remote sensing but also explore resources among the deep lunar regolith better. Using CE-2's passive microwave data, the daytime microwave map of the Moon is calculated based on the hour angle. The lunar regolith radioactive transfer model, integrated with a temperature model and the lunar regolith thickness model, has been established in this paper. On the basis of aforementioned models, the imaginary part of dielectric constant in the whole lunar was numerically simulated. Thus the FeO+TiO2content of the global lunar regolith layer can be obtained on the basis of the relationship between dielectric constant and FeO+TiO2content which was built from the measured data of the Apollo sample. The accuracy of the inversion based on CE-2 MRM data approach would be assessed by comparing our results with the Clementine data, Lunar Prospector gamma-ray data, and the laboratory data of lunar soils.

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.

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.

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.