Land surface temperature (LST) is an important variable in Earth science research and can be measured using various thermal infrared (TIR) observations. Due to variations in data sources and inversion algorithms, LST products yield inconsistent results, further affecting subsequent applications (e.g., drought and vegetation monitoring). Although many evaluation studies have been conducted, most of them have focused on product validation or differences between inversion algorithms. There is a lack of analysis of the data sources, which is important for the data fusion. Therefore, a consistency analysis was conducted herein. Mainstream polar-orbiting satellite data were selected, including data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea and Land Surface Temperature Radiometer (SLSTR), and Visible Infrared Imaging Radiometer Suite (VIIRS). The same inversion algorithm (split-window) for LST was employed across all datasets, thereby ensuring that differences in satellite data were the primary factor. Following validation based on in situ measurements, the polar-orbiting LST results were intercompared and analyzed with the LST results derived from the Himawari-8 Advanced Himawari Imager (AHI) observations. The results indicated that 1) similar conclusions were obtained from the intercomparison results and the ground-based validation results, with root mean square errors (RMSEs) for intercomparison results ranging from 2.903 K to 3.353 K; 2) based on the intercomparison results, regression analysis revealed that surface temperature status, land cover and vegetation information, and angular factors had a significant impact on the evaluation results, with t tests yielding p values less than 0.05 for all of these factors; and 3) based on a decision tree analysis, the contributions of angular factor, surface temperature status, and land surface structure were 50.9%, 31.3%, and 17.8%, respectively. These findings enhance knowledge regarding the impact of different satellite data on LST inversion results and emphasize the necessity of preprocessing before the joint application of satellite data, such as angle normalization and radiometric calibration.

As an essential input parameter, Leaf Area Index (LAI) plays significant value in global climate models.. Acquisition of LAI products with extensive long-term series is crucial for various applications. Presently, numerous remote sensing inversion algorithms have been proposed for the generation of LAI products. However, the low temporal resolution is a huge challenge for producing LAI at a medium to high spatial resolution scale. In addition, the cloud has resulted in a significant reduction of accessible data. In this study, a LAI reconstruction method considering vegetation phenological period was developed. A total of 241 Sentinel-2 images from Saihanba in northern China and cloud probability data provided by Sentinel Hub were collected. The results indicate that the proposed method can effectively reconstruct LAI value in the data-missing period and areas. This study evaluated the performance of LAI reconstruction by implementing the "leave-one-out" method. The results demonstrate that the RMSE of LAI reconstruction is 0.5509 when using July 31st data as the validation dataset, and 0.3933 when using August 7th data as the validation dataset. In summary, this study demonstrates the potential for the LAI time series reconstruction from high-resolution data.

Wildfire is a serious natural disaster that poses a serious threat to the safety of human life and property. Currently, there are many researches related to satellite wildfire detection, but few can achieve near real-time monitoring results. Himawari-8 geostationary satellite can provide full disk data every 10 minutes, making near real-time monitoring of wildfires possible. In this paper, a wildfire detection method based on Himawari-8 for multi-temporal data is proposed. In our method, we use temporal convolutional network (TCN) to predict the brightness temperature and achieve excellent prediction results, the mean absolute error (MAE) is 0.28 K, mean square error (MSE) is 0.30 K2, and mean absolute percentage error (MAPE) is 0.10 %. Then, the predicted values combined with other features as model inputs, and machine learning classification models were used for wildfire detection. The experimental results showed that the combination of multi-layer perceptron (MLP) model and strategy 2 containing brightness temperature predicted values achieved an accuracy of 90.91% in wildfire detection.

Land Surface Temperature (LST) is crucial for studying various surface processes. Previous validations of remotely sensed LST products gained mostly the combined results of remotely sensed observations overlaid with inversion algorithms, failing to account for the impact of multi-source data on inversion results. To address this, data from six polar-orbiting satellites within China region in 2019 were collected using a uniform inversion algorithm. The derived LST values were then validated against in-situ measurements from 13 ground sites in China. Results showed minor disparities in LST validation results among satellites, with an root mean square error (RMSE) ranging from 2.6K to 3.0K. Variation analysis revealed higher errors in extreme temperature levels and a decrease in RMSE with increasing angular distance. These findings enhance understanding of multi-source data's influence on LST inversion quality and promote collaborative utilization of satellite data.

Land surface temperature (LST) with fine spatiotemporal resolution is a much-needed parameter in the earth surface system. The LST downscaling is an efficient way to improve the spatiotemporal resolution of LST and has been developed rapidly in recent years. Due to the simple operations and discernable effects of statistical regression and its extension algorithms, these algorithms have been widely researched. However, most statistical regression models assume scale invariance, which makes the downscaled LST inaccurate. This study analyzed the scale effect in the process of LST upscaling/downscaling, then proposed a new algorithm based on Taylor expansion for Moderate Resolution Imaging Spectroradiometer (MODIS) LST downscaling. The Taylor expansion algorithm estimates regression coefficients between LST and auxiliary parameters in the consistent scale. It is tested in three typical areas of different landscape with different auxiliary parameters, and the results are significantly improved compared to the traditional algorithm. However, the new algorithm may introduce the temporal discrepancy between MODIS LST and empirical concavity factor (S), which is estimated with Landsat 8 data, into the downscaling procedure in some circumstances. To discuss the influence of temporal discrepancy, we designed three schemes for pairing MODIS and S and analyzed the downscaled results. The results show that the proposed algorithm got the best downscaled results when the MODIS LST acquired time is consistent with the time of S. When the time is inconsistent, the pairing scheme of a similar season gives better results than that of different seasons. The algorithm performs generally well so long as the spatial distribution of auxiliary parameters in the date of Landsat 8 acquisition is similar to the date of MODIS acquisition.

Landslides are common natural disasters that often cause serious impact and damage to human society. Since landslide disasters threaten people's production and life all the time, it is particularly important to predict the risk of landslides and to control landslide disasters. When studying landslide risk and deciding whether to treat the landslide, it is meaningful to classify and compare the risk of landslides so as to select those landslides with a higher degree of danger for priority treatment. The target of this paper is to extract factors related to landslide risk, and train a classification models for landslide risk. It employs ensemble machine learning algorithms to classify landslide hazards. Because the landslide feature has a large number of dimensions, this paper uses the PCA method to reduce the dimension. Due to the imbalance of the samples, this paper uses the SMOTE method to handle the imbalanced learning. The results of study show that the selected factors are highly related to landslide risk, the classification model in this paper has good accuracy.

With the development of urbanization in China, the land economy accounts for more and more percentage in the gross domestic product (GDP) of China. Therefore, the accurate assessment of urban residential land price is of great interest for the governments. In the paper, we introduced the transfer learning to assess the urban residential land price, taking the Shenzhen city in China as a case. First, we collected housing price, land price data and points of interest (POI) data. The POI data was quantified as the influencing factors of two price data. Second, we used housing price data and influencing factors to train a high-accuracy model based on deep belief network (DBN). Third, we kept the DBN model unchanged, and then used three new models connected in order to better assess land price. The three models are back propagation (BP) neural network, support vector machine (SVM), and random forest (RF). Finally, we used land price data and influencing factors with three models to find the best one. The models were trained by the method of five-fold cross validation. Our results showed that all of three transfer learning models had good accuracy but the model with random forest was more suitable for the urban residential land price assessment. Therefore, our proposed method is an efficient approach to assess the urban residential land price with transfer learning.

With the vigorous advocacy of the concept of green development, the protection and management of natural resources become more and more important. It is of great significance to study the classification of surface natural resources by remote sensing. In this paper, a high-resolution net (HR-Net) model is used to classify surface natural resources by Gaofen-1 (GF-1) satellite images and digital elevation model (DEM) data. First of all, we obtained the GF-1 satellite images, DEM data and census data of geographical conditions of the study area. And the first two kinds of data are integrated into five channels, which are red (R), green (G), blue (B), near infrared (NIR) and elevation channels. Second, we chose an area to make the labeled image with several classes contain surface natural resources. Third, we cut the image into training images and testing images, and the training images were made into 5000 images, 128 × 128 pixels to train the HR-Net model. Also for comparison, the experiment was carried out used the image without DEM data. Finally, we compared the accuracy, and our results showed that HR-Net model is useful and the image with DEM data has the better accuracy. Therefore, HR-Net and DEM data can be applied in practice to support the classification of surface natural resources.

In the presented article, machine learning (ML) is employed on advanced Himawari imager (AHI) to examine real-time fire and map damaged zone over Yunnan, China. The main emphasis lies in employing machine learning as an alternative to primeval thresholding, extricating, and scrutinizing thermal anomaly using infrared (IR). Firstly, Himawari brightness temperature (BT), band ratio, albedo, and BT differences are utilized to scrutinize fire break out. Then, to ensure pixels are clear and free from clouds, the Himawari cloud product is implemented. Finally, machine learning models such as random forest (RF), artificial neural network (ANN), and time-series long short-term memory (LSTM), a deep learning model, are used to precisely classify the active fire pixels and achieved accuracies of 0.96%, 0.95%, 0.92% respectively. The results evaluated using another AHI wildfire product and inter-compared with multi-source fire products.

Wildfires may cause great casualties and heavy wildfires are becoming more and more frequently all over the world in recent years. However, due to the environmental limitation, high manual-dependent operation is often impractical with other limits. In this paper, a transfer learning neural network based on long short term memory (LSTM) was used to detect wildfire based on Himawari-8. The real time dynamic threshold value detection for cloud mask based on the modified Otsu algorithm was used to fast and accurately remove cloud areas where wildfire detection is failed due to signal blocking. Then, the experiments were conducted with LSTM and other models. The experimental results showed that our method was positive for wildfire detection.

The retrieval of aerosol properties over land from satellite sensors has always been a challenge. At present, several different algorithms for retrieving aerosol optical depth (AOD) have been developed from different satellite sensors. While each algorithm has its own advantages, the accuracy of AOD retrieval still needs to be further improved. To improve the retrieval accuracy of aerosol algorithms, it is necessary to provide a better method to describe the surface properties. In the current study, a new aerosol retrieval algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) images at a high spatial resolution of 500 m is proposed based on$a$prioribidirectional reflectance distribution function (BRDF) shape parameters database, which is reconstructed via the 3-D discrete cosine transform (DCT-PLS) method. Then, the surface reflectances are calculated from BRDF model (i.e., RossThick-LiSparse), and a non-Lambertian forward model used to describe the surface anisotropy. The new algorithm is used for processing the MODIS over the Beijing–Tianjin–Hebei of China, and Southeastern United States of America regions, and results are validated against AERONET AOD measurements as well as compared with the MODIS AOD products. The comparison showed that the estimation scheme of surface reflectance in this new algorithm significantly improved the AOD retrievals accuracy, with average correlation coefficient ~0.965 and root-mean-square error ~0.125; the number of AOD retrievals falling within expected error has increased to ~80.1%, and the overestimation uncertainty has been reduced compared with MODIS products. Due to the high spatial resolution and continuous spatial distributions of the AOD retrievals by the new algorithm, therefore, it can well-captured aerosol details over mixed surfaces and better useful for air pollution studies than the MODIS products at local and urban scales.

The angular and spectral kernel-driven (ASK) model distinguishes soil and vegetation spectral features by the component spectra and is a promising model which combines multisensor data for inversion. However, its global application is limited by the component spectra. This article proposes parameterization of the ASK component spectra of soil and leaf from global spectra libraries as ANGERS, GOSPEL, LOPEX, and USGS. A statistical ratio (y) of various leaf to soil spectra is used to capture their spectral differences and variations, with mean (m) + u (0, ±0.5, ±1) standard deviations (σ) [i.e., y (m + uσ)]. Optimization inversion is applied to determine the ratio candidates y(m + uσ), allowing more tolerance for spectral uncertainty, which releases the semiempirical nature of the kernel-driven model. Simulation data analysis proves its feasibility and good capture of vegetation-soil spectral differences. The model's bidirectional reflectance factor (BRF) fitting error [root-mean-square error (RMSE)] of 0.0245 is slightly larger than the true component spectra of 0.0178, and albedo RMSE is 0.0116 in Black Sky Albedo and 0.0182 in White Sky Albedo. The result also shows its good robustness to the noises, where the====level up to 20% noise conducts a 0.0277 error in BRF fitting and an ignorable influence in albedo. The synergistic-retrieved albedo from multisensor satellite data consists of in situ measurements with an RMSE of 0.0171, compared to 0.0131 from true component spectra retrievals. The new parameterization sacrifices some accuracy, but it is simple and operational for global retrieval with a satisfactory precision.

Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth's radiation budget and the fundamental quantities of energy available at the earth's surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003-2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m2and a root mean square error of 28.83 (32.84) W/m2at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m2with a standard deviation of 0.8 W/m2over a 13-year (2003-2015) period.

This letter presents a new algorithm that allows the retrieval of the aerosol optical depth (AOD) at a high (500 m) spatial resolution from Landsat 8 Operational Land Imager (OLI) data over urban areas. Because of the complex structure over urban surfaces, the bidirectional reflectance characteristic is obvious; however, most of the current aerosol retrieval algorithms over land do not account for the anisotropic effect of the surface. This letter improves the quality of AOD retrieval by providing the surface reflectance based on the multiyear MODIS bidirectional reflectance distribution function (BRDF)/Albedo model parameters product (MCD43A1) and the RossThick-LiSparse reciprocal kernel-driven BRDF model. The ground-based Aerosol Robotic Network (AERONET) AOD measurements from five sites located in urban and suburban areas are used to validate the AOD retrievals, and the MODIS Terra Collection 6 (C6) dark target/deep blue AOD products (MOD04) at 10-km spatial resolution are obtained for comparison. The validation results show that the AOD retrievals from the OLI images are well correlated with the AERONET AOD measurements (R = 0.987), with a low root-mean-square error of 0.07, a mean absolute error of 0.036, and a relative mean bias of 1.029; approximately 95.3% of the collocations fall within the expected error. The analysis indicates that the BRDF is essential in ensuring the accuracy of AOD retrieval. Compared with the MOD04 AOD retrievals, the OLI AOD retrievals have better spatial continuity and higher accuracy. The new algorithm can provide continuous and detailed spatial distributions of the AOD over complex urban surfaces.

In this paper, the land surface bidirectional reflectance distribution function (BRDF) and albedo on a small timescale are retrieved by the multisensor combined BRDF inversion (MCBI) model with improved accuracy. The accumulation period for this BRDF/albedo retrieval is shortened to 8 and 4 days with data from four satellite sensors, the Moderate Resolution Imaging Spectraradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer (VIIRS), and Medium Resolution Spectral Imager (MERSI), to obtain the dynamic features of land surfaces. All the four sensors have high revisit frequencies and dense angular sampling. The MCBI model provides an algorithm to form a virtual MODIS observation network with these four sensors, resulting in a multiband and multiangle sampling reflectance data set. It also provides a multisensor reflectance quality control index, the net information index (NII), for a robust BRDF/albedo retrieval. The performance of the MCBI is assessed by comparisons with MODIS BRDF/albedo product and the in situ measurement. The results show that the highly frequent angular sampling with four sensors allows for a full retrieval of BRDF/albedo with a shorter accumulation period of 8 and 4 days. The NII reduces the uncertainties when using different sensors' reflectance and allows for a high-quality BRDF/albedo retrieval. It reveals that the MCBI has the potential to generate a multisensor-based BRDF/albedo on a small timescale. The MCBI is a key algorithm for the BRDF/albedo product in China's multisource data synergized quantitative remote sensing production system and operationally implemented to generate a global product.

Land surface component temperatures are important inputs in longwave radiation and evapotranspiration estimation models. Most component temperature inversion approaches focus only on two components, namely, soil and leaves, because space-based multiangle observations are lacking. This approach is inconsistent with ground-based measurements, which suggest that the temperatures of sunlit and shaded soil may significantly differ. This paper explores a three-component temperature inversion scheme that uses airborne multiangle thermal infrared observations to decrease the difference between the retrieved data and the actual subpixel temperature distribution. The FR97 model, which is an analytical directional brightness temperature model that was modified by dividing the soil component into sunlit and shaded portions, is adopted to calculate the matrix of component effective emissivity, which links multiangular observations and component temperatures. The new forward model and the inversion scheme are assessed using simulated data sets from the Scattering by Arbitrarily Inclined Leaves (4SAIL) model. The results indicate that the modified FR97 model provides good precision and that the inversion scheme based on the modified FR97 model is appropriate because of the model's simplicity and accuracy and the inversion's low sensitivity to noise. The inversion scheme is validated using airborne data collected by the wide-angle infrared dual-mode line/area array scanner over an area planted with maize and ground measurements collected during the Heihe Watershed Allied Telemetry Experimental Research campaign. The results indicate that the root mean square errors of the component temperatures of the leaves, sunlit soil, and shaded soil were 0.72 °C, 1.55 °C, and 2.73 °C, respectively. Because of the modified FR97's straightforward form and acceptable precision, we recommend this new retrieval scheme as an option for retrieving the component temperatures of leaves, sunlit soil, and shaded soil.

Current satellite broadband emissivity (BBE) products do not correctly characterize the seasonal variation of vegetation abundance. This paper proposes a new method to estimate the BBE of vegetated surfaces to better describe the seasonal variation of vegetation abundance. The method takes advantage of the radiative transfer models' ability to calculate multiple scattering with a physical basis and uses the 4SAIL model to construct a lookup table (LUT) of BBE for vegetated surfaces. The BBE of the vegetated surface was derived from the LUT using three inputs: leaf BBE, soil BBE, and leaf area index (LAI). The validation results show that the accuracy of the new method exceeds 0.005 over fully vegetated surfaces. As a case study, this method was applied to data from 2003 to generate global vegetated surface BBE products for that year. An analysis of the results indicated that the derived BBE can correctly reflect seasonal variations in vegetation abundance that the data converted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS spectral emissivity products have been unable to reveal. The new method was also compared to the vegetation cover method (VCM). The VCM can correctly characterize seasonal variations in vegetation abundance. However, the classification of bare soil and vegetation in the VCM may produce step discontinuity in the calculated BBE. The new method is being implemented to produce a new version of the Global LAnd Surface Satellite (GLASS) BBE product over vegetated surfaces.

Ocean water albedo (OWA) plays an important role in the global climate variation. Compared with the achievements in land surface albedo studies, the global distributions of ocean water and sea ice albedo are seldom addressed. This study designed an operational global OWA algorithm based on the three-component reflectance model of the ocean water: sun glint, whitecaps, and water-leaving reflectance. The related achievements in these three areas are reviewed and integrated into the operational algorithm. After the sensitive analysis, the algorithm is compared with previous studies and validated with ground observations at COVE site located 25 km east of Virginia Beach (36.91° N, 75.71° W), and the results indicate that the proposed algorithm is generally consistent with previous parameterization scheme. As an example, the global OWAs in summer and winter 2011 are generated using the remote sensing reflectance data sets via the Moderate Resolution Imaging Spectroradiometer and Modern-Era Retrospective analysis for Research and Applications meteorological reanalysis data set. The generated product includes instantaneous (e.g., local noon) and daily mean OWAs under both clear-sky and white-sky conditions. Upon the examples, the local noon clear-sky OWA shows a significant latitude variation due to the dominance of the solar angle, whereas the white-sky OWA is sensitive to wind speeds and optical constituents. The global distribution of the daily mean OWA exhibits a similar trend to the local noon OWA. However, the daily mean clear-sky OWA is significantly larger than the local noon OWA; this finding should be noted when using OWA products for energy balance research. Additionally, all forms of OWA products exhibit increase in coastal areas with high input of terrestrial matters.

Fraction of Absorbed Photosynthetically Active Radiation (FPAR), determined from remote sensing data, can vary with the spatial resolution, the different retrieval algorithms and viewing angels of the used data. This paper aimed at evaluating MODIS, POLDER and CYCLOPE global FPAR products, and found that the MODIS FPAR was larger than CYCLOPES, and their difference ranged within 0.1~0.2, especially at the forest area where MODIS product always presented seasonal variation in this area while CYCLOPES products kept relatively stable. For other vegetation covers, their difference was less than 0.1. Furthermore, the comparison of MODIS and POLDER FPAR products shown that the MODIS FPAR was also larger than the POLDER and their difference was up to 0.1 to 0.2.

Fraction of abstracted Photosynthetically Active Radiation (FPAR) is a fundamental terrestrial state variable in most ecosystem productivity models and is also one of the key terrestrial products. This paper proposed a new Direct-Algorithm to retrieve FPAR from apparent reflectance of MODIS's seven bands in the visible, near-infrared and short-wave wavelengths. The Direct-Algorithm developed from the dataset simulated by radiative transfer models of canopy and atmosphere with different canopy structures and atmosphere conditions, estimated direct FPAR (FPARdir), and scattering FPAR (FPARsct), and total FPAR of the canopy (FPARtot) by using linear equations of TOA reflectance. Result showed that the estimated FPAR product were close to that of MODIS products except the forest, perhaps because the homogenous canopy of the SAIL model is not suitable for the forest canopy.

The influence of topography on land-surface bidirectional reflectance and albedo should be considered in rugged terrain. However, land-surface albedo algorithms neglect topographic effects, leading to errors in estimating the albedo in rugged terrain. This letter investigates the Angular Bin (AB) algorithm of land-surface albedo and shows that it should be improved when albedo is estimated in rugged terrain. The Terrain AB (TAB), an improved algorithm for albedo estimation with the AB algorithm and digital elevation model (DEM) data set, is presented in this letter. The accuracy and performance of the TAB algorithm was investigated by using the simulated DEM and Bidirectional Reflectance Function as well as the MODIS daily reflectance of the Heihe River Basin. The results show that the TAB algorithm has a better albedo estimation performance in rugged terrain and gives an acceptable accuracy.

Land surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. The polarization and directionality of the Earth's reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is${\sim}{0.009}$for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly.

This paper aimed at the case of nonisothermal pixels and proposed a daytime temperature-independent spectral indices (TISI) method to retrieve directional emissivity and effective temperature from daytime multiangular observed images in both middle and thermal infrared (MIR and TIR) channels by combining the kernel-driven bidirectional reflectance distribution function (BRDF) model and the TISI method. Four groups of angular observations and two groups of MIR and TIR channels with narrow and broad bandwidths were used to investigate the influence of angular observations and bandwidth on the retrieval accuracy. Model sensitivity analysis indicated that the new method can generally obtain directional emissivity and temperature with an error less than 0.015 and 1.5 K if the noise included in the measured directional brightness temperature (DBT) and atmospheric data was no more than 1.0 K and 10%, respectively. The analysis also indicated that 1) large-angle intervals among the angular observations and a larger viewing zenith angle, with respect to nadir direction, can improve the retrieval accuracy because those angle conditions can result in significant difference for components' fractions and DBT under different viewing directions; 2) narrow channels can produce better results than broad channels. The new method was finally applied to a multiangular MIR and TIR data set acquired by an airborne system, and a modified kernel-driven BRDF model was used for angular normalization to the surface temperature for the first time. The difference of the retrieved emissivity and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity was found to be approximately 0.012 in the study area.

Land surface albedo is critical in earth radiation budget and global climate monitoring. At present, models and algorithms for land-surface albedo retrieval from diverse satellites have been well developed. However, limited sampling angles from mono-sensor and coarse temporal resolution restrict the further promotion and application of albedo. Multi-sensor observations offer more information of land surface anisotropy and result in multi-angular, multi-spectral, high temporal resolution measurements. Modeling BRDF and albedo with multi-sensor data presents a unique opportunity to improve the temporal resolution and recover the consistency of surface albedo. This paper proposes a novel Multi-Angular and Multi-Band inversion model for BRDF and albedo retrieval with multiple sensors. The preliminary result based on MODIS and AVHRR band reflectance indicates that the model could obtain higher temporal resolution albedo with the same or even higher accuracy when compared with MCD43B3 albedo product.

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.

Albedo is essential in earth radiation budget and global climate monitoring. GLASS albedo is a newly developed global broadband land-surface albedo product with 1-km spatial resolution and 1-day temporal resolution. AB algorithm is employed to generate daily albedo product by building a linear regression relationship between narrowband directional reflectance and broadband albedo. AB algorithm avoids the restrictions on the observing angles, but suffers from the assumption that the land surface should be homogeneous and flat without terrain relief. In our research, we proposed a technique to improve AB algorithm for retrieving albedo in rugged terrain, which is called TAB algorithm. The preliminary validation using simulation data indicated that the accuracy of AB algorithm could be significantly improved when the terrain effect is considered.

GLASS albedo is a newly developed global daily land- surface broadband albedo product with 1-km spatial resolution. There are two main deficiencies in GLASS albedo products: 1) large areas of missing data mainly caused by cloud coverage; 2) sharp fluctuations in time series due to noise and uncertainties in inversion algorithm. This paper proposed a temporal filtering algorithm to reconstruct daily albedo series from GLASS albedo products. Validation results show that this algorithm can fill data gaps and smooth albedo series effectively.

Clumping index is an important vegetation structure parameter to describe the foliage clumping in canopy quantitatively. It is defined as the ratio of the effective leaf area index to the true leaf area index. In previous studies, it is generally considerate that cluster of canopy and below canopy scale in pure pixel. However, the in-pixel spatial heterogeneity need be taken into account estimating clumping index in mixed pixel, which is different from clumping index of pure pixel. A new method to calculate clumping index of mixed pixel based on fine spatial resolution image is proposed in this paper. The sensitivity analysis has been processed and its results show that the pixel spatial heterogeneity and view zenith angles cannot be ignored for calculating the mixed-pixel clumping index. The method is capable of correcting the scale difference caused by the heterogeneity of the vegetation cover inside the mixed pixel the view zenith angle. The formula presented can estimate clumping index of the mixed pixel more accurately, which is significant for LAI inversion of coarse spatial resolution and the precision accuracy application of carbon cycle model.

This paper presents a new forest leaf area index (LAI) inversion method from multisource and multiangle data combined with a radiative transfer model and the strategy of -means clustering and artificial neural network (ANN). Four scenes of Landsat-5 Thematic Mapper (L5TM) and Beijing-1 small satellite multispectral sensors (BJ1) images, acquired at different times, were selected to construct multisource and multiangle image data in this study. Considering a vertical distribution of forest LAI from both overstory and understory, a hybrid model of the invertible forest reflectance model (INFORM) was used to support the retrieval of forest LAI to eliminate the dependence of understory vegetation. The simulated data from INFORM outputs, added with a random noise, were first clustered by -means method, and were then trained by ANN to obtain the inversion model for each group (cluster). Next, the inversion model was applied to the different combinations of multiangle data to retrieve the forest LAI. Finally, a validation of inverted results with Moderate Resolution Imaging Spectroradiometer LAI product and field measurements was conducted. The experimental results indicate that the accuracy of the inverted forest LAI can be improved through the addition of observation angle data, if the quality of the image data is ensured. The inversion accuracy of LAI with the multiangle image data is improved by 30% compared to the average accuracy of the inverted LAI with the single angle data after considering the addition of random noise to the ANN training data.

In this paper, we proposed a localized land surface temperature retrieval method using water vapor and in situ data, and applied it in Yingke area. With the ground measurement of emissivity and water vapor simulated, we recovered LST from MODIS/Terra Level-1b data. ASTER temperature product was used to compare with the MODIS retrieval result. The comparison showed the MODIS retrieval result agreed with ASTER data with an average difference of 1.64 K.

The radiation from the sun to satellites in the sky is always modulated twice by atmosphere. Aerosol is one of the most active components in atmosphere and it usually contaminates the remotely sensed imagery severely so that most remotely sensed imagery cannot be used without atmospheric effect correction. However, the remotely sensed imagery is always the coupling of atmosphere and land surface information, which makes it very difficult to decouple the remotely sensed information to retrieve accurate atmospheric information and land surface information respectively from remotely sensed imagery alone. Based on the physical mechanism of radiative transfer model, many researchers assumed the specific surface condition, such as dark objects and invariant objects, so the atmospheric information like aerosol optical depth (AOD) can be decoupled from remotely sensed information. Since these assumptions are just for some specific surface conditions, the atmospheric information of many other surface conditions, such as sparsely vegetated areas and snow covered areas, are not available. Therefore, we propose an algorithm of aerosol information retrieval for agricultural areas based on land surface modeling. By analyzing the soil, the leaf and the canopy spectra, we selected a set of models to calculate the surface reflectance of different land surfaces, which include bare soil areas, sparsely vegetated areas and densely vegetated areas. In addition, the water vapor effect of 2.1μm spectrum band has been considered in this paper for retrieving more accurate canopy and soil reflectance. Finally, North China Plain is selected as experimental area for aerosol information retrieval through the proposed algorithm, and the AOD value measured by sunphotometer is taken as true value to evaluate the algorithm's accuracy.

Due to limited vertical resolution, the waveform of vegetation whose height is relatively low will superpose on soil waveform. Therefore, lidar full-waveform data were mainly used in forestry, but no research in the crop. In this paper, in order to derive crop height, a gaussian decomposition algorithm based on transmitting waveform is adopted to distinguish the crop waveform from soil waveform, and to extract peak location and pulse width from raw waveform data, proving it is a reliable and highly accurate decomposition algorithm. Moreover, the decomposition algorithm lays the proper foundation for obtaining other crop biophysical parameters.

This paper proposes a new multi-angular & multi-spectral BRDF model (ASK Model) base on the kernel-driven conception, and outlines an algorithm suitable for broadband albedo retrieval with the new model. By adding component spectra into kernels as prior known driven variables, the new model express BRDF as a linear combination of wavelength independent kernel coefficients and kernels expressed as functions of both observation geometry and wavelength.

The high spatial and high temporal satellite data is necessary in the operational agricultural applications of remote sensing. But till now the advantages of high spatial and high temporal resolution still can not be realized in single sensor. The PSP method (Patch Spectral Purification Method) is capable of retrieving field patch average information from high temporal but moderate spatial resolution satellite data, which meets the requirement of high spatial and high temporal resolution information in the real monitoring applications. In this paper a PSP-based methodology is proposed to retrieve the high spatial and high temporal resolution information for the growth and drought monitoring using multiple satellite data. An application demonstration was made in Xinjiang, China to monitor the cotton growth and drought with MODIS and Landsat/TM data. And the processing software-AgRsis (Agricultural Remote Sensing Inversion System) was realized to generate the daily crop parameters standard maps(e.g. NDVI, TVDI) for the crop growth and drought monitoring.

It is suitable for remote sensing monitoring and precision agricultural for Xinjiang cotton for its unique natural and ecological condition and growing and cultivating characteristic. However, precise monitoring is weak in Xinjiang cotton. We took the farm land of Xinjiang Production and Construction Corps as an example and made the cotton leaf area index (LAI) inversion. It is befitting to invert cotton LAI for Mini-satellite of Beijing-1' wide scope (600 kilometer), middle spatial resolution (32 meter) and high temporal resolution (2-3 days). The LAI is inverted for Beijing-1 mini-satellite data based on the physical canopy reflectance model and lookup-table inversion method. The LAI is also inverted for TM data considering scaling problems. It is feasible to invert LAI for Beijing-1 through the comparison between the inverted LAI and the LAI from the experiment.

Row planted crop is the transitional crop type with the discrete structure and continuous structure. There are different canopy structures for different growth stages. The canopy structure will transfer from row structure to continuous structure around the elongth growth stages. Furthermore, the elongth growth stage is key growth stages for the crop. For parameter inversion, it is significant to propose the key growth stages to simplify the model selection and to improve the parameters inversion accuracy. We put the object on one row period for the similar structure of the row planted crop. For each period, four components (sunlit vegetation and soil; viewed vegetation and soil) can be computed based on the bidirectional gap probability model, and structure parameters (W (row width), H (row height), S (row spacing), etc.) and view and solar zenith/azimuth angles. At the same time, the equivalent radiance for vegetation and soil from direct illuminated light and from the diffused and multi-scatted light will be computed. The key growth stages model (KGSM) is the sum of the four component radiance which is the product of each component area and the corresponding equivalent radiance. Through the validation based on the RGM and SAILH model, the canopy bidirectional reflectance can be simulated based the KGSM. The model validation is also done for experiment measurement.

A simultaneous airborne, satellite and ground based remote sensing experiment which is aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at catchmental scale is implemented in a typical inland river basin of northwest China. The experiment is composed of the cold region, forest, and arid region hydrological experiments as well as a hydro/meteorological elements and Doppler radar precipitation observation experiment. Airborne microwave radiometers at L, K and Ka bands, hyperspectral imager, thermal imager, and lidar are used. Various satellite data are collected. Based on these observations, the remote sensing retrieval models and algorithms of water cycle variables can be developed or improved, and a catchment-scale land/hydrological data assimilation system is going to be developed.

More and more wide-view angle or multi-angular sensors provide the possibility to retrieve vegetation parameters. It is an important issue to access the accuracy and uncertainty of the products retrieved from different view angle observations. This paper presents an approach to evaluate the information content of the multi-angular remote sensing data. The proposed method is based on information theory. By using the entropy difference between all unknown parameters and non-target parameters for the remote sensing data, the information content is quantified. The presented methodology revealed the information content in the remote sensing data. The accuracy of the vegetation parameters retrieved from canopy reflectance depends mainly on the information about target parameter contained within observations. The relationship between information content and the LAI inversion accuracy is listed in this paper.

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

The consistent end-to-end simulation of them is an important task, sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. It is essential to accomplish simulation of atmospheric radiative transfer, if a complete imaging simulating system is to be expected. Based on given resolution and directional capabilities of the instrument, and combination with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contribution of atmosphere were considered. Through analysing results, it indicates that analytic model and adjacency effect model is more adequate for thermal infrared imaging simulation than others existing models.

Image fusion, widely used in remote sensing, is an efficient method to combine the high spatial information in panchromatic (Pan) image with the essential spectral information in mutispectral (MS) images. In most fusion methods, distortion of spectral information or spatial information commonly exists in the resulting image. The component substitution (COS) fusion methods always merge Pan and MS images with distiortion in spectral information. To overcome this shortcoming, a fusion method based on a general COS (GCOS) framework is proposed in this paper. Landsat TM and SPOT images are used in several experiments to test our fusion algorithm. Some classical indices of images, such as entropy, correlation coefficient, the relative difference and universal image quality index et al, are also used here to evaluate the ability of preserving spectral and spatial information. And the resulting of several methods including IHS, PCA, BT, Gram-Smiths (GS) and SFIM are listed to compare effects of different fusion methods. Experiments show that the proposed method provided satisfied results with richer information than the other methods in both spatial and spectral domains.

The ear is the top layer of mature wheat and has very different geometric and thermal characteristics from that of leaves. Compared to the directional brightness temperature (DBT) of wheat canopy without ears, the DBT at the ear stage has specific features, and the ear effects could not be explained by previous models. This paper proposes a hybrid geometric optical and radiative transfer model to reveal the combined influences of the geometric structure of ears and leaf; the temperature distribution of ear, leaf, and soil; and the Sun-target-sensor geometry on the canopy DBT. The soil, leaf, and ear layers are taken into account in the model so it is named as the Soil Leaf Ear Combined (SLEC) DBT model. We compare the model prediction with the field measurement data. The results show that the new SLEC DBT model can simulate the DBT of wheat at the ear stage with an accuracy of 0.78 K.

A high-precision method for mapping the fraction of wheat Area in Luancheng County, North China Plain is presented and validated. The method is based on a spectral mixture analysis model and a optimal temporal TM image election where six main endmembers (greenhouse , soil, wheat, roof, nursery garden, shadow), constitute the observed pixel reflectance from the satellite. Given the reflectance observation, the fractional wheat area(FWA) is solved from the spectral mixture analysis(SMA) model. The high-precision estimation for six endmember SMA can be expected at an optional temporal range, which is the last ten days of March month every year. The simple landcover in March month specifies the effect of best simulation on the FWA of TM image pixels while no disturbed vegetations have grown green leaves. This approach enables operational wheat planting area mapping for extensive areas with an almost 95% classification precision to sub-pixel level. Our study area covers 346 km2. comprising whole Luancheng County of Hebei Province. Applying SMA to Landsat/TM cloud-free data acquired on March 21th, 2004, we estimated the areal fraction of wheat cover for the whole County field. The validation against statistical data from the Luancheng County Statistic Bureau indicates that with finite SMA, 92.3% accuracy is gained. Better results were also obtained from the validation against statistical information, for example, 95.8% of wheat-covered area were recognized when heavy haze area-free TM image is applied. A general formula for deriving the fractional wheat planting area mapping provided by SMA is presented, too. It can be concluded that wheat planting area to sub-pixel level in NCP can been operatively monitored with limited endmember SMA in a good precision.

Modeling the soil component temperature distribution is useful to study multi-angular thermal remote sensing. SVAT (soil-plant-atmosphere transfer) model could be a good choice because it can predict canopy temperature distribution. However, most of them, including CUPID model 111. were unable to separate shade soil and sunlit soil. They only gave a single temperature for the soil surface. In this paper, based on the difference of net radiance and evaporation rate between the shade and sunlit soil, an extended model from CUPID was proposed to simultaneously retrieve the shaded temperature and sunlit temperature of soil surface. The comparison showed good agreement between simulated soil temperatures and measured ones.

In this paper, a synthetic strategy has been employed to model 3D canopy's radiation transfer in the whole optical spectral domains. 3D plant architecture model (the Clumped Architecture Model of Plants: CLAMP) (1) is used to generate the realistic vegetation scene. In the visible and NIR region, the canopy BRDF was decomposed into three parts: single scattering contribution from leaves, single scattering contribution from the soil, and multiple scattering part of the canopy. The single scattering contributions come from illuminated leaves and soil components which are computed by the reverse ray-tracing procedure (2) with their corresponding reflectance. The multiple scattering contribution is approximated by the four-stream theory. As a result, the modeling of VNIR region is more efficient and fairly accurately describes the anisotropically scattering features of vegetation. In the TIR region, the directional brightness temperature of canopy is calculated as the linear combination of four component's (illuminated leaves, illuminated ground, shadowed leaves, and shadowed ground) brightness temperature multiplied by its fractional cover computed by the reverse ray-tracing procedure. Initial modeling results show typical features of vegetation's anisotropic scattering and directional temperature distributions, for example, hot spot, bowl shape and reach a good agreement with theoretical results in those three domains. This strategy shows potential of exploring the impact of canopy structure on the radiometric response measured by remote sensors.

The effect of crop leaf angle on the canopy-reflected spectrum cannot be ignored in the inversion of leaf area index (LAI) and the monitoring of the crop-growth condition using remote-sensing technology. In this paper, experiments on winter wheat (Triticum aestivumL.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectionalin situreflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

It is feasible to retrieve LAI over large area from remote sensing data with physical models;however,it is quite difficult to get accurate LAI and thus limit the remote sensing application without enough prior knowledge due to the underdetermined parameters in the physical inversion models.A spectrum database system of typical objects in China(SpecLib) has been set up recently,which may provide a priori knowledge of typical land cover for LAI inversion.MODIS data is used to retrieve LAI after atmosphere correction,geometrical correction and cloud identification.The SAIL(Scattering by Arbitrarily Inclined Layers) model is applied for the inversion of LAI for MODIS data.The vegetation coverage of the mixed pixels of the MODIS data are calculated based on the TM data sets.The LAIs of pure pixels(computed from the retrieved LAIs and vegetation coverage) are compared with the field measurement data in Luancheng,Heibei Province,China.Meanwhile,the LAIs of pure pixels are also compared with the MODIS LAI data products.The inversion results show that the!SpecLib effectively improved the accuracy of leaf area index inversion.

Usually we use multi-channel image data, such as TM, and empirical relationship, such as NDVI-LAI relation or SR-LAI relation, to estimate LAI. Multi-angular remote sensing data provide more information for canopy structure. This paper presents a method to estimate LAI using multi-angular data and model inversion method. The airborne multi-angular data were acquired by AMTIS (Airborne Multi-angle TIR/VNIR Imaging System), which was a prototype sensor designed by the Institute of Remote Sensing Applications of Chinese Academy of Science. Our study is based on two datasets: one was acquired in Beijing Shunyi in April 11, and the major crop is sparse winter wheat; another was acquired in Haerbin in August 24, and major crops are dense corn and soybean. Both datasets have been geometrically atmospherically corrected. Ground based measurements were carried out during the flight experiment. SAIL model is chosen to predict reflected radiance of a presumed LAI. Various view angles relate to the different components ratio in view field, and the reflected radiance is different accordingly. Hence, a certain LAI value was given, SAIL model predicts a set of reflected radiances of various angles. We compare the model predict radiance with the radiance viewed by an multi-angular sensor, to find the optimized LAI which can make the radiance predicted by the model be closest to the viewed radiance, then take this LAI value as the right value

Crop growth monitoring is one of major directions of remote sensing applications. A widely used way is to draw out the NDVI curve of the interesting region in crop growth seasons, then determine whether crop is good or not according to the characters of the NDVI curve and some empirical knowledge. Whether the pixel used to draw NDVI curve exactly describes the target crop species strictly affects the accuracy of the result. The objective of this research is to design an algorithm to find out pure pixel of target crop species from hyperspectral image. The algorithm is based on the spectral library, and it will obtain the sample spectra from the library. If the library returns zero sample canopy spectra, the algorithm will automatically simulate the sample spectra. Then it will aggregate narrow bands into broad bands to match the sensor bands, and at last compare the pixel spectra with the sample spectra. In this research, we use Hyperion, OMIS and MODIS data of different spatial and spectral resolution and use different methods to calculate distance

The polynomial expression is a new and powerful model to interpret the light scattering process inside leaf/soil system and decipher the nonlinear relationship between component spectra and canopy reflectance. In our previous work, we have outlined the forward model and analyzed its feature. For models with large number of parameters, their inversion is a challenging problem. This paper presents the algorithm and strategy that makes the complex multivariant inversion problem efficient and stable

In this paper, a generalized split-window method to derive land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer) data is applied. A major problem in land surface temperature inversion is that there are too many unknown variables, especially for MODIS data which is in low resolution, one pixel is a mixture of several cover types. To analysis the uncertainties of the LST retrieval algorithm based on MODIS images, the field measurements, together with fine resolution images, AMTIS (the airborne multi-angle TIR/VNIR imaging system) data and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been used

Vegetation indices are subjected to many external perturbations such as soil background variations, atmospheric conditions, geometric registration, and especially sensor viewing geometry. Subsequent use of these indices to estimate crop yield and monitor crops growth would result in substantial uncertainties. To reduce the uncertainties due to sun-view angle variations, some methods mere generated by use the reflectance or albedo generated from the BRDF models. MODIS vegetation composition algorithm uses the empirical BRDF model (developed by Walthall et al. to normalize the sun/view angles to certain angle, and then composite the VI by several day's data. In this paper, we present a new method based on prior knowledge to normalise vegetation index on pure pixels of crops, which can be recognized from MODIS image by high resolution land cover map. We simulated different BRDFs of winter wheat in different grow stages by radiative transfer models, using the plant canopy parameters obtained from prior knowledge. Then, we use this BRDF to normalize vegetation indices. The method was tested by the ground based measurements and MODIS Data. It shows our results are good consistent with the ground based measurements. We compare our methods with the algorithm of MODIS vegetation composition, it proved that the result calculated by our method is in better agreement with the surface reflectance characterizations and our method is more effective to monitor the crop growth in regional scale

Vegetation chlorophyll is a key indicator in ecosystem. Operational chlorophyll content estimation models by remote sensing are needed. In this paper, some modifications were made to an existing semi-empirical chlorophyll content estimation model, a new estimation model was given and validation results are promising

Vegetation Index (VI) and Leaf Area Index (LAI) are very important parameters for crop growth situation monitoring and crop yield estimation. However, it is not easy to get accurate VI or LAI. One of the reasons is because of the difficulty in the inversion of aerosol optical depth (AOD), which is the key factor in atmospheric correction. This study addresses an algorithm of AOD inversion in Agriculture region based on the support of Spectrum database. It is usually supposed that we can get the surface reflectance of blue band for most of the algorithm of AOD inversion. As the Dense Dark Vegetation (DDV) method, the surface reflectance in blue and red bands is calculated from the reflectance at 2.1 or 3.8 mum band. However, it is not easy to retrieve the AOD value of each pixel for a whole satellite image because of the unknown surface reflectance on some regions such as the sparse vegetation area. For agriculture area, the surface reflectance varies from bare soil, sparse vegetation, and then Dense Dark Vegetation, during the whole crop growth period. We have carried out a series of field spectrum measurement during different crop growth period and set up a crop spectrum database. By analyzing the soil, the leaf and the canopy spectra, we selected a set of models to calculate the surface reflectance of agriculture region during different crop growth period, which include bare soil model, sparse vegetation model and continuous vegetation model. Then, the surface condition is put to the atmospheric radiation transfer model to calculate Look-up Table (LUT) for MODIS bands, which is used to retrieve the AOD value of MODIS image. North China Plain is selected as the experiment area, the AOD value measured by sun-photometer is taken as true value to evaluate the inversion algorithm's accuracy and the results show good agreement

In the process of applying the theoretic results of BRDF model study to remote sensing image data, an important step is to extract BRDF features from multiangular images. Because of the limitations of registration, pixel alignment and the intrinsic scale scene, it is necessary to perform spatial average to the georeferenced multiangular images before extracting BRDF feature. Otherwise the feature will no be representative to the surface property. Based on the analysis of geometrical limitations, this paper discussed how the apparent BRDF feature changes from random to order after the spatial average.

Presents a new method to analyze the relation between canopy spectral reflectance and component spectral properties. The polynomial decomposition method differs from linear spectral unmixing because it takes into consideration the multiple scattering inside canopy. It is consistent with physical BRDF models and more flexible because it does not depend on certain assumption on canopy structure. This method is superior for some kinds of canopy whose structure is ambiguous between homogeneous and discrete. The output of the analysis is "angular-structural coefficients" which is possible to be related directly to canopy biophysical parameters.

The purpose of this paper is to validate the BEPS model in crops for net primary productivity (NPP) estimation and to study the spatial scaling effects of NPP using both airborne and field data. The results show that the highest differences between modeled NPP at resolution 15 m and 30 m are greater than those re-sampled from modeled NPP at 3 m resolution, especially at the boundary of winter wheat.

Data experiment of Mont-Carlo directional radiation transfer model for continuous vegetation was made to decide the optimum view angle of thermal temperature for reliable estimation of sensible heat flux. The true heat fluxes were simulated with classical two-layer model. The conclusions of this study are: 1) the optimum view angle varies within a large range according to the change of leaf area index, leaf angle distribution, soil moisture and other parameters, so it's difficult to define a universal optimum angle; 2) however, the fractional coverage of vegetation in FOV (field of view) under optimum angle is relatively stable and could be used in a new corrective method.

In order to retrieve land surface component temperature from multi-spectral remote sensing images, such as ASTER, we choose a component equivalent emissivity model, and iterative linear regression inversion algorithm. The method is tested with ASTER image of VNIR and TIR channels, as well as supplementary and validation data acquired from ground experiment. The atmospheric effect is corrected with the dark-object method; surface structural information is derived from ASTER VNIR observations; component emissivity is measured in situ with the BOMEN MR-154 spectrometer; validation data are also measured in ground experiments. Finally, the accuracy of the results and sources of error are analyzed.

Usually a vegetated area pixel is composed of the basic components, soil, leaf with different proportions, while soil and leaf have different water content. There are lots of canopy models to describe the mechanism with which these component spectra compose the canopy spectrum. For practicability, a simplified model is suggested to calculate the surface vegetation coverage, and water contents of leaf and soil from pixel reflectance, such as multi-spectral remote sensing data. Experiment results show that the accuracy of this method can satisfy the application's request.

The classical Shuttleworth-Wallace two-layer model has a modification version proposed by Norman et al. (1995) and Kustas et al. (1999) to estimate evapotranspiration over row crop surfaces. However, the assumption of this modified model is controvertible, and more theoretical analysis and data validation seem necessary to figure out whether this model is a "layer" model or a "patch" model essentially. A field data validation of this kind was carried out in this paper and it was found that a "patch" method could provide more accurate results. A new method to improve S-W model for row crop was given at the end of this paper. This model uses "layer" method to account for the in-canopy exchanges and modified surface resistances to account for the heterogeneities of row crops. The data from the composite remote sensing experiment in Shunyi, Beijing April 2001 were used to validate this model. Surface fluxes were measured by Bowen ratio system while surface radiometric temperature; component temperatures and other parameters were also measured at different atmospheric boundary layer conditions. Results show that estimation of sensible and latent beat fluxes by the new model agree well with the observed fluxes.