As one of the most important spectral invariants, the recollision probability (p) is a crucial physical parameter that describes the interaction process between the photon and the canopy. This study discusses the applicability of the recollision probability in the thermal infrared (TIR) domain by Monte Carlo simulation. By extending the recollision probability from the VNIR to the TIR, an Improved Canopy Brightness Temperature model based on the p theory (ICBT-P) is introduced to simulate the canopy thermal radiation directionality. A cross-validation between the analytical ICBT-P model and 3D DART model was conducted, yielding R2values over 0.99 in two homogeneous scenarios where the leaf and soil components exhibit a significant temperature difference. In other two homogeneous scenarios where the leaf and soil components have the same temperature, the R2values are over 0.98. The average brightness temperature difference across the four typical scenes is 0.08K. This new model (i.e., ICBT-P) has the potential to be used for the canopy temperature estimation and the component temperature separation of leaves and soil.

The estimation of Sea Surface Temperature (SST) from ocean satellites with large observation angles must account for the angular effects on SST. This study developed an angle-dependent non-linear split-window algorithm (A-NLSW) to retrieve SST from Chinese ocean satellite HY-1D thermal infrared data. The algorithm coefficients were obtained based on the simulated dataset and grouped by initial SSTs, total atmospheric column water vapor content (TCWV), and satellite zenith angle (SZA). The A-NLSW algorithm is validated and re-calibrated using the bulk temperature collected by the iQuam in-situ dataset. After re-calibration, the accuracy of the SST was improved from 1.53 K to 0.87 K for SZA ranging from 0 to 70.5 ° . Nearly 60% of the validation points achieved an accuracy of 0.5 K and over 90% achieved an accuracy of 1.0 K. These findings highlight the robustness of the A-NLSW algorithm in reliably retrieving SST from HY-1D satellite images, even when observations are made at large SZA.

Ecosystem services (ESs) indicate that species comprising an ecosystem can provide services that are significant to human beings. ES classification of vegetation is significant for accelerating ecological restoration and achieving sustainable development. However, current vegetation classification product cannot effectively reflect their ESs. This study focused on the north Tianshan Mountain, Xinjiang, China, and tried to classify ESs of vegetation based on machine learning and remote sensing techniques. This study utilized multiband Landsat-8 surface reflectance data from Google Earth Engine (GEE) together with meteorological data and Digital Elevation Model data. The classification map accurately portrayed the spatial distribution of seven types of vegetation ES, including agricultural production (AP) cropland, sandstorm prevention (SP) and water retention (WR) forest, grassland, and shrub. The classification results indicated that, the two types of grassland occupy the largest area besides desert, followed by AP cropland, whereas SP forest and WR shrub have the smallest area. The accuracy of ES classification for vegetation is very high, with an impressive overall accuracy of 89.61% and a kappa coefficient of 0.874. This study provides valuable insights into ES classification of vegetation, which can serve as a reference for formulating an ES development plan in north Tianshan Mountain, Xinjiang.

The fraction of mulched film is of great significance for evaluating the agricultural water-saving effect and controlling environmental plastic pollution. Unfortunately, there is no work has been done to obtain this parameter due to the mixed pixel issue of satellite imagery with medium and low resolutions, till now. In this study, we proposed a novel two-step framework for mapping fraction of mulched film based on very high resolution satellite observation and deep learning. The first step is extracting the extent of the mulched film based on a new few-shot learning model named PT-CNN (Parameter Transition Convolutional Neural Network), which aims to increase the extraction accuracy and overcome the lack of labeled training data. The second step is retrieving the fraction of the mulched film at pixel scale based on a spectrum analysis method. The result shows that the proposed PT-CNN outperforms several state-of-the-art methods in mulched film extraction, with F1-scores at 97.09% and 98.65% for white and black mulched film, respectively. Meanwhile, the retrieved pixel scale fraction of mulched film shows high consistency to in-situ measurement, with a MAE of 0.0321. The proposed method can be useful in agricultural water resource management and environmental governance.

Accurate estimation of the surface emission longwave radiation (SELR) has important scientific significance for understanding its spatiotemporal dynamics and surface thermal environment. High spatial resolution thermal infrared images provide better data support for studying SELR of complex surfaces such as urban surface. This paper focus on proposing a new urban-oriented hybrid method to estimate urban surface emission longwave radiation from top-of-atmosphere thermal radiance images, by taking the GF-5/VIMI thermal image as an example, and conduct the parameter sensitive analysis of the model as well as application over Beijing city. The experimental results of the simulation dataset showed that the developed method has relatively high precision, with SELR errors of less than 12.0 W/m2under low water vapor conditions and less than 17.0 W/m2under high water vapor conditions. The application of method in GF-5 image also demonstrated the rationality and effectiveness of the method.

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.

The fraction of absorbed photosynthetically active radiation (FAPAR) is directly linked to the estimation of canopy gross primary production and is a key input parameter in terrestrial ecosystem models. Diffuse FAPAR calculation is necessary for cloudy conditions. Direct and diffuse FAPAR require distinct simulation methods due to different transfer paths, yet most models do not consider diffuse FAPAR independently. In this study, an improved FAPAR-P model （ FAPAR-Pro ） based on the spectral invariant theory, which considers direct and diffuse FAPAR separately, was proposed to simulate all-sky vegetation FAPAR. Based on the proposed FAPAR-Pro model, along with the hourly ratio of diffuse radiance data calculated from Himawari-8 products, the all-sky vegetation FAPAR was calculated. Validation performed with ground measurements showed that the overall RMSE and MAE values are 0.056 and 0.067 for diverse types of vegetation canopies and different diffuse radiation conditions. The results indicate that the model was applicable to diverse types of vegetation canopies and different radiation conditions, and therefore, may provide support for continuous FAPAR time series simulation and various ecological applications in the future.

The Advanced Geostationary Radiation Imager (AGRI) on board of the Chinese geostationary satellite FengYun-4B (FY4B) designs four thermal infrared channels, which has the characteristics of wide observation range, high observation frequency and fixed point observation, with a spatial resolution of 4 km at nadir and a full-disk observation every 15 minutes. Therefore, it can monitor the surface temperature changes on a large time scale, providing important data support for agricultural drought monitoring and climate change. However, there is currently no algorithm for land surface temperature retrieval with this sensor. This paper proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the LST and emissivity from three thermal-infrared (TIR) images. The analysis shows that the algorithm can theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.016, respectively.

Plastic mulch has been widely used in agricultural cultivation for its significantly increasement toward crop yield since last century, and has recently draw lots of attention from the government side, due to the environmental concerns caused by the agricultural plastic mulch. In this study, a few-shot learning based deep learning model is designed for film mulching mapping using very high resolution satellite imagery. Firstly, the image restoration model is pre-trained by massive unlabeled very resolution satellite imagery samples. Then, the film mulching mapping model is set by the pre-training model weights and trained by few labeled film mulching imagery samples. Results show the proposed method could achieve well film mulching mapping performance, and the F1-score reaches 0.9, which is useful in the agricultural irrigation management and yield prediction.

Leaf chlorophyll content (LCC) is a critical indicator for precision agriculture. Accurately estimating winter wheat LCC based on remote sensing at high spatial resolution is of great significance for agricultural management and decision. In this study, a two-step method was used to retrieve wheat LCC from UAV hyperspectral imagery. The first step is converting canopy reflectance to leaf reflectance using Look-up tables (LUTs) generated from the unified model of bidirectional reflectance distribution function (BRDF). The second step is retrieving wheat LCC from derived leaf reflectance using the PROSPECT-PRO model. Retrieved wheat LAI, leaf reflectance, and LCC were validated against field measurements. The results indicate a good agreement between retrieved and measured LAI with RMSE of 0.092 and R2of 0.605. Leaf reflectance retrieved from UAV canopy reflectance exhibit good consistency with measured leaf reflectance with RMSE of 0.017 and R2of 0.962. Retrieved wheat LCC is of good quality compared to measured LCC, with R2of 0.7675 and RMSE of 4.33 μg/cm2. In conclusion, this study holds potential in estimating wheat LCC from UAV hyperspectral imagery.

Leaf area index (LAI) is a critical indicator for modeling global biosphere–climate interactions. Accurately measuring forest LAI significantly affects the objectivity and accuracy of LAI remote sensing products. DHP is the most widely used instrument to measure forest LAI. However, it is challenging to measure forest LAI accurately using DHP at meter-scale as the wide viewing angle range of DHP is not appropriate for validation of high-resolution pixels. To address this issue, a geometric-based method was proposed to obtain the optimal view zenith range for DHP-based LAI field measurements at meter-scale in this study. Three virtual forest scenes were generated by LESS (LargE-Scale remote sensing data and image Simulation framework) and field measurements at Saihanba, in northern China was collected. The validation results indicate a good agreement between LAI estimation using proposed method and reference LAI datasets. The R2between the LESS-derived LAI and DHP-derived LAI are all greater than 0.9, and the RMSE are all less than 0.1 in simulated scenes. Meanwhile, the R2between the UAV lidar-derived LAI and DHP-derived LAI is 0.668 with RMSE of 0.376. In conclusion, this study holds potential in measuring forest LAI at meter-scale.

Forests are essential for stabilizing the biosphere and providing ecological service functions that are significant to human beings. However, current forest landcover products cannot effectively reflect their ecological service functions. This study focused on the north Tianshan Mountain, China and proposed an ecological service function-based forest classification method using multi-source landcover products and Digital Elevation Model (DEM) data. The proposed method included spatial superposition, clustering, and window sliding techniques. The resulting forest classification map accurately portrayed the spatial distribution of three ecological service function-based forests: water and soil conservation (WSC), farmland shelter (FS), and windbreak and sand-fixation (WSF) forests. Results indicated that WSC forests dominate the forest distribution in the north Tianshan Mountain, constituting over half of the total forest area, while FS forests are mostly spread across the western part of the region, accounting for 34.1% of the entire forest area. WSF forests are relatively small and mainly distributed in the central part of the region. This study provides valuable insights into forest classification based on their ecological service functions, which is critical for understanding the spatial distribution and dynamic changes of forests, promoting sustainable development and preserving their ecological service values.

Leaf area index (LAI) field measurements based on digital hemispherical photography (DHP) and LAI-2200 instruments have been widely used for remote sensing validation in forestry. Both DHP and LAI-2200 utilize fish-eye lens to capture the largest footprint of a canopy with a wide range of view zenith angle (VZA). However, accurately measuring field LAI at high spatial resolution poses a significant challenge since the view scope of fish-eye sensor is much larger than the size of high spatial resolution pixel. Therefore, selecting appropriate VZA ranges is crucial to address this issue. In this letter, we propose an improved geometry-based method that considers the average tree height, crown depth, and high-resolution pixel size. To validate this method, we designed four simulated forest scenes with different crown shapes through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model and conducted field measurements. The results indicate that our proposed method significantly enhances the accuracy of fisheye-based LAI field measurements at high spatial resolution compared to previous method, with an almost 70% reduction in RMSE. In addition, our method exhibits greater improvement in measuring forest LAI at high resolution with DHP (RMSE < 0.3) compared to LAI-2200 (RMSE < 0.5). In conclusion, our method holds great potential in accurately measuring fisheye-based forest LAI for remote sensing validation at high spatial resolution.

Error estimation of precipitation products is an important procedure in the data quality evaluation. It is a challenging task due to the lack of the in-situ ground observations and the variations of the geophysical characteristics in regions with complex terrain. Compared with the traditional methods, the double instrumental variable (DIV) method has the merits of being able to estimate the errors between two products. In this study, the double instrumental variable method for data error estimation is applied and validated on precipitation products in regions with complex terrain. The DIV-based Errors for two state-of-the-art precipitation products IMERG and SM2RAIN are being further verified by using another high-accuracy ground-based precipitation products CMPA. The results indicate that the DIV-based Errors of IMERG and SM2RAIN range from 0 to 25 mm per day and 0 to 15 mm per day, respectively. The RMSEs of IMERG and SM2RAIN compared with CMPA, which are defined as CMPA-based Errors, are ranging from 0 to 23 mm and 0 to 22 mm, respectively. It is concluded that the spatial distribution of the DIV-based Errors shows the consistency with the CMPA-based Errors, which further demonstrates the potential of using double instrumental variable method for precipitation products fusion.

Thermal infrared (TIR) remote sensing observation signal is influenced by both atmospheric and land surface conditions that are difficult to separate with conventional multichannel TIR data. Because of the advantage of channel wealth, hyperspectral TIR data can simultaneously estimate the land surface and atmospheric parameters using neural network models or integrating them with physical models. However, the commonly used neural network models do not fully explore the correlation between different channels by treating the input data as discrete features. Thus, this study aims to develop a new deep neural network (DNN) by combining the long short-term memory (LSTM) network and convolutional neural network (CNN) for estimating land surface temperature (LST), emissivity, atmospheric transmittance, upward radiance, and downward radiance more accurately. By applying on the thermal airborne hyperspectral imager (TASI) simulation dataset covering global atmospheric conditions with 32 channels in$8.0- 11.5\,\,\mu \text{m}$, the proposed model achieved results with the LST error of 0.95 K, the emissivity error of less than 0.012 for each channel, and the accuracy of three atmospheric parameters has also been improved compared with the current neural network models. Our model has been applied to a real TASI image, and its validity was further proved by the ground measurement validation data. Therefore, it can provide more reliable initial values for physical optimization models.

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature at the top of the atmosphere in two adjacent TIR channels and thus is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides 2-channel TIR images with the same 100m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this paper, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 K to 2.87 K in various regions.

Thermal infrared remote sensing can acquire large-scale land surface thermal radiance effectively. However, the observed data are affected by surface and atmospheric conditions. Traditional methods require some prior knowledge, such as emissivity in split-window algorithm and atmospheric correction in temperature–emissivity separation algorithm. This information is difficult to obtain directly and accurately. Hyperspectral thermal infrared data provide the possibility for simultaneous retrieval of atmospheric parameters, land surface temperature (LST), and emissivity because of their abundant band information. This study proposed a feature-band linear-format hybrid (FebLihy) algorithm by combining a deep neural network (DNN) model and a physical model with thermal airborne hyperspectral imager (TASI) data. The proposed algorithm was divided into three steps. First, the radiative transfer equation was converted into a linear form, and seven feature bands were chosen to reduce the unknowns. Second, the initial values of atmospheric and land surface parameters were estimated with the DNN model. Finally, least-squares optimization was used in the physical model to retrieve the final results. Results of the simulation data showed that the root-mean-square error (RMSE) of LST was 0.86 K, the RMSE of emissivity was less than 0.015, and the accuracy of atmospheric parameters was improved effectively by the physical model. The FebLihy algorithm was applied in a real TASI image in Fuyun County and verified with CE312 ground measurement data. Accurate results were achieved. The FebLihy algorithm will be optimized in terms of model and data in the future study.

Highly quality soil moisture is significant for hydrological, meteorological and agricultural applications. At present, active and passive remote sensing are the only ways to monitor soil moisture directly at regional scale. However, the quality of single satellite-based soil moisture product is insufficient to meet the requirements of these applications. Hence, fusing these two soil moisture products to improve their quality of change capture ability and accuracy is a necessary and challenging work. This study proposes an improved double instrumental variable method to fuse active and passive soil moisture products. First, the method is improved in finding the best instrumental variables in time series based on correlation coefficient. Second, fused weights of input soil moisture products are estimated using the improved method. Finally, fused soil moisture products are obtained with higher change capture ability and higher accuracy. The Tibetan Plateau was selected as the study area to test the algorithm using both of the Climate Change Initiative (CCI) active and passive soil moisture products from the European Space Agency (ESA). The ground validation results show that, compared with the original soil moisture products, the change capture ability, expressed by the correlation coefficient (R), and the accuracy, expressed by the unbiased root mean square deviation (ubRMSD), have been both improved by about 10% on average. This study indicates that the proposed fusion method can effectively improve the quality of soil moisture products to further understand the global changing water cycle.

As a surface component, the tree trunk affects the top-of-canopy (TOC) emissivity and thermal infrared (TIR) radiance over a forest with fewer leaves, which is important for the inversion of land surface temperatures (LSTs) and further applications such as predicting forest fires and monitoring drought conditions. Therefore, the tree trunk effect was analyzed in this article using a thermal radiation directionality model, in which the forest structure was considered by the geometric optical (GO) theory and the spectral invariance theory was introduced into the GO framework for the single-scattering effect between components. The model used was evaluated using unmanned aerial vehicle (UAV)-based measurements with root-mean-square errors (RMSEs) lower than 0.25 °C for directional anisotropies (DAs) of brightness temperatures (BTs). Comparison with a 3-D radiative transfer model, discrete anisotropic radiative transfer (DART), also indicated an acceptable tool of the proposed model for the trunk effect with RMSEs lower than 0.003 °C and 1.2 °C for DAs of emissivity and BTs, respectively. In this study, the root-mean-squared difference (RMSD) levels between the vegetation-soil and vegetation-trunk-soil canopies, which were viewed as an equivalent indicator of the trunk effect, were provided for the TOC emissivity and BTs as well as their DAs, by combination with the changes in the leaf area index (LAI), stand density, trunk shape, and component temperatures, which can help identify the cases in which the trunk effect should be considered. According to a comprehensive analysis, for cases with sparse stand density ( α0.04), the tree trunk should be considered for a BT RMSD level lower than 0.5 °C when the LAI value was lower than 0.6. The corresponding LAI value was 0.8 for an RMSD level of BT DA lower than 0.3 °C. Moreover, for the cases with low soil emissivity, the difference in the TOC emissivity with and without trunk can reach up to 0.035, and the RMSD was still larger than 0.01 when the stand density and LAI were 0.05 and 0.6, respectively.

Spatial distribution and disappearance of sandy land is important for ecosystem management of desert regions and provides highly valuable information on desertification and climate change studies in arid environments. Based on the field measurement in the Gurbantonggut Desert, Xinjiang, China and the analysis of the spectral features of sandy land, a new sand differential emissivity index (SDEI) was proposed first for sandy land detection. Compared with the previous vegetation index, which can only distinguish green plants from bare land, SDEI can make a distinction well between sandy land and dry vegetation. For large regional mapping of sandy land, SDEI was applied on the ASTER Global Emissivity Dataset based on the Google Earth Engine platform. And then, four emissivity simulation schemes of different mixed pixels were conducted to determine the best threshold of sandy land mapping. The results show that when the threshold value is larger than 0.041, the sand distribution can be well extracted. Finally, the sandy land area of China extracted by SDEI is 160.67×104km2for year 2008, which is close to the data released by the China’s State Forestry Administration. These experimental results indicated that SDEI is applicable to identification of sandy land, and therefore satellite remotely-sensed thermal infrared observations have good potential in sandy land detection.

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere. Long time series of and spatio-temporal continuous soil moisture is of great importance to meteorological and hydrological applications, such as weather forecasting, global change and drought monitoring. In this study, the Essential Climate Variable (ECV) soil moisture product of the Tibetan Plateau (TP) from 2002 to 2015 was reconstructed using the General Regression Neural Network (GRNN) based on reconstructed MODIS products, i.e., LST, NDVI, and Albedo. Results show that the ECV soil moisture could be well reconstructed with R2higher than 0.71, RMSE less than 0.05 cm3cm-3and absolute Bias less than 0.03 cm3cm-3for both grids of 0.25°×0.25° and 1°×1°, compared with the in-situ measurements in 2012 over the TP. The reconstructed long time series of and spatio-temporal continuous soil moisture could be valuable in hydrometeorological studies of the TP.

Housing vacancy data, providing useful information on building occupancy rates, are used extensively by public and private organizations to evaluate the need for new housing development programs and also the vacancy rate is regarded as a leading indicator to measure the economic climate. Such a topic has received considerable attentions in China due to its overheated real estate development. Previous house vacancy studies use governmental statistics or private user positioning data, the collection of which is time consuming and difficult. In this work, an efficient indicator Vacancy Area Index (VAI), ranging from 0 to 10, is proposed to measure the county level housing occupancy and ghost cities over China based on MODIS products and DMSP-OLS nighttime light data. Results show that VAI is a good measure of house vacancy with a detection rate 94.44% of ghost cities and a precision 92.48% of non-ghost cities. About 320 among 2430 county areas in China have a VAI no less than 7, indicating national wide spread high house vacancy. The VAI can also reveal the spatial characteristics and temporal development of cities with different features such as tourism cites, industrial cities, and real "Ghost Cities" accurately.

With the deepening of urbanization and industrialization in China, large amount immigration flocked into the metropolis. As the capital of China, Beijing has become a huge immigration community. Most of the immigrants live in temporary buildings in urban-rural fringe areas because of high rents. It is difficult to supervise the quantitative changes of immigrants living in urban-rural fringe areas using traditional method. In order to study the variation of immigration communities, we try to detect and analyze the changes in temporary buildings using remote sensing data, in Daxing district, Beijing. Firstly, the TBI (Temporary Building Index) extraction method is used to extract Temporary buildings from 5 periods Sentinel-2A remote sensing images of Daxing District from 2016 to 2018. According to the extraction results, we found the immigration community is changing rapidly and intensely in three years. Affected by policy, the immigrants gathered close to the urban area, is rapidly moving away and the number of immigrants lived in suburban is increasing significantly.

Winter wheat is one of the main food crops in China, accurately forecasting the yield of winter wheat is of great significance for agricultural management and decision. UAV remote sensing has the advantages of high spatial-time resolution, low cost, flexibility and repeatability. In this paper the growth condition remote sensing and yield estimation of winter wheat were carried out using UAV hyperspectral sensor in Xiaotangshan Town, Changping District, Beijing. Based on the DSD (directional second differential) method and AIVI (Angular Insensitivity Vegetation Index), LAI (leaf area index) and LNC (leaf nitrogen content) of winter wheat at heading and filling periods were retrieved, and according to the result of DSSAT simulation, the forecasting model between LAI, LNC at heading and filling periods and yield of winter wheat was established by random forest algorithm. The R2of yield estimation model is 0.787 and RMSE is 727.87 kg/ha, which shows the yield estimation model can accurately and effectively estimate winter wheat yield.

The directional anisotropy of canopy emissivity and brightness temperature in the thermal infrared band has widely been studied. However, the contribution of different scattering orders has been an open scientific question for many years. The recently proposed CE-P model enables us to analytically evaluate the different scattering orders. Herein, we derive expressions for the first double collisions (DCE12) and first triple collisions (DCE123). Our result shows that DCE123can simulate the observed emissivity with an error less than 0.001 and that DCE12is reasonably accurate when leaf emissivity is greater than 0.96. Numerical analysis shows that the contribution of quadruple or greater collisions can be ignored when the leaf (soil) emissivity is no less than 0.90. Furthermore, we develop the CBT-P model to simulate the directional brightness temperature (DBT) based on the new optimized CE-P model (DCE123) and validate it by 4SAIL (4-Stream Radiative Transfer Theory of Scattering by Arbitrary Inclined Leaves) and DART (Discrete Anisotropic Radiative Transfer) models. Both of isothermal (soil temperature is equal to leaf temperature) and nonisothermal (soil temperature is higher than leaf temperature) cases are considered. The maximum differences between the CBT-P model and 4SAIL (DART) are less than 0.35 K (0.42 K), the average differences between CBT-P and 4SAIL (DART) are less than 0.10 K (0.13 K), and the R2is over 0.99 (0.95) with component emissivities larger than 0.90 and the difference between soil and leaf temperatures less than 20 K. The directional anisotropy of DBT is dominated by the zero-scattering and the single scattering terms according to the new developed CBT-P model.

Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and RL model, gave the best fitting results for all the scenes. The R2of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions.

Multi-angle information extraction is very important for the validation of land surface models, such as bi-directional reflectance distribution function (BRDF) model and directional brightness temperature (DBT) model. The experiment can be done in the near surface, on the plane, and on the satellite. The spatial-temporal average method is widely-used on the scale of plane. In this paper, we try to extend it to extract the multi-angle information from near surface observation sensors. We found this method can obtain good result over homogeneous land surface, but the observation protocol is critical over heterogeneous land surface. For instance, the observation plane should be perpendicular to the row direction. In addition, we found that the hot spot area will be shaded by the observation platform which leads to the BRDF result to be affected seriously.

A new directional canopy emissivity model (CE-P) based on spectral invariants can separate the multiple scattering effect and single scattering in vegetation canopy. So we can further evaluate the contribution of scattering effect to the canopy emissivity and brightness temperature based on CE-P model. Numerical analysis shows that the contribution of more than three times scattering can be ignored when the leaf (soil) emissivity is no less than 0.90. Then, we optimize CE-P model and obtain the expressions containing the first twice collisions (ε2) and first three times collisions (ε3). The result shows that ε3 can simulate the emissivity in any case with an error less than 0.001. Furthermore, we simulate the brightness temperature distribution using the optimized model and compare it with DART model. The difference between them is less than 0.3K and the R2of them is over 0.96 in all of the selected samples.

While change of lakes in the Tibetan Plateau (TP) is generally attributed to change of natural conditions, it is also important to evaluate the influence of human activities on lakes with the rapid increase of population in the plateau. Using a total of 786 clear-sky images of Landsat, we try to study the trend of lakes' area and shape in the TP and investigate the role human activities play. During the past 25 years, the area of lakes in the TP has experienced a rapid increase, and reservoirs and salt lakes also appeared to expand, but the shape and area of the lakes close to cities and roads have little change. In general, total 43 lakes affected by human activities increased 841.3 km2in the surface area, and all of them located in the northeast or southwest of the Tibetan Plateau.

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors.

GPP (Gross Primary Productivity) is an important index to reflect the productivity of vegetation. The MODIS 8-day GPP time-series products in the study area were rebuilt using Savitzky-Golay filter from 2001 to 2012. After phenological parameters' variation was analyzed based on dynamic threshold method, the time series of GPP were analyzed in this paper. Correlation analysis between annual accumulated GPP and influencing factors showed that annual accumulated temperature rather than precipitation has a remarkable positive correlation on GPP. The growing season length of study area was prolonged obviously over the dozen years but no obvious correlation was found between growing season length and annual accumulated GPP.

The clumping index (CI) is an important factor for deriving forest leaf area index (LAI) at both pixel and point scale. Due to the non-uniform distribution of leaves within trees and random layout of trees under natural conditions, the CI varies with view zenith angle (VZA) as well as the gap fraction. The variation tendency of gap fraction and CI with VZA dominates the potential uncertainty of deriving LAI from directional observations. In this research, the continuous change of gap fraction and CI with VZA was simulated under virtual tree scenarios. Meanwhile, the directional CI was calculated using an algebraic model developed based on previous researches on gap fraction. Both results were validated using ground measurements. The CI discrepancy distributes within ±0.06 coming from the random number distribution restriction and the influence from tree trunk. The analysis using the algebraic model shows that the forest CI reaches an asymptotic level after 55° for forest canopy dense enough. This is an interesting and important phenomenon, which reveal a stable feature of CI to be further used to extract forest information.

Fraction of vegetation coverage (FVC), an important index to depict the conditions of land covered by vegetation, is increasingly used in monitoring grassland degradation in ecological researches. Hulunbuir Steppe is one of the best grasslands in China. So, in this study, we set Xinbaerhuyouqi, Xinbaerhuzuoqi, Chenbaerhuqi, and Ewenkezuzizhiqi as the study area. Pixel Decomposition Models was introduced to retrieve the vegetation coverage, and the time series of vegetation coverage was reconstructed. Then we analyzed the temporal-spatial changes FVC time series for study region over the 15-year period from 2000 to 2014. The results showed that the higher level vegetation coverage mainly distributed in the east of study area; on the contrary, the lower level of that mainly distributed in the west of study area. The vegetation coverage of whole study area was decreased in the first 10 years, while that was increased slowly in the latter 5 years. Additionally, the break points which occurred in green-up and green-end periods had much more significant correlation with temperature; the break points occurred from July to August correlated with precipitation.

Net Primary Productivity (NPP) is crucial in modelling global carbon cycle. There are a lot of studies focused on NPP evaluation using remote sensing method, resulting in different evaluation models. Most of the models are based on large spatial scale such as national or global, leading to retrieval errors in heterogeneous pixels and difficulties in field validation. This paper develops a new, remote sensing NPP evaluation method to estimate NPP on high spatial resolution. The model uses a newly improved Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Model which is based on the recollision probability (FAPAR-P Model) to calculate the Absorbed Photosynthetic Active Radiation (APAR), which improves the accuracy of APAR estimation. The study area was the midstream of Heihe River Basin, located mostly in Zhangye, Gansu province, China.

The canopy bidirectional reflectance function (BRDF) characterizes the radiometric interface among vegetation, solar and the sensor, a stable, accurate and operational canopy BRDF model is the basis of retrieving canopy structure and biophysical parameters such as leaf area index and albedo by remote sensing method. The canopy reflectance can be divided into single scattering reflectance and multiple scattering reflectance. After the bidirectional gap fraction was derived by Poisson distribution, a new single scattering reflectance model was developed with Geometrical Optic model at leaf scale. The continuous canopy and discrete vegetation were unified by clumping index. So the area ratio of four components (sunlit crown, sunlit background, shadow crown and sunlit background) is parameterized. In order to simplify the expression of multiple scattering, a recollision probability based scattering model was used instead of the Radiative Transfer model. The validation using ground measurements has proved the reliability of the model.

Remote sense values of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) suffer from the effect of ragged terrain. In this study, the effect of ragged terrain was internalized into the FAPAR model based on recollision probability (FAPAR-P), by improving FAPAR-P in two aspects: calculating the shielding factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting for the interception probability according to the slope and aspect of each pixel. Then, the new model FAPAR-PR (FAPAR-P Model for Ragged Terrain Area) was established. To validate the new model, we chose Saihanba National Forest Park of Hebei Province as the study area, and compared the FAPAR values derived from the models with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., UK). The validation result shows that the FAPAR-PR model is applicable to ragged terrain areas and achieves a high level of accuracy.

A physical model for simulating crop albedo based on the law of energy conservation and spectral invariants was developed. The idea referred to the primary PARAS albedo model for forest. The developed model concerned two key problems for crop monitoring, one is the anisotropy of soil reflectance and the other is the clumping effect of crop leaves at canopy scale, which contributed to the improvement of the model accuracy. The comparison between the model results and those simulated using a Monte Carlo method shows that the calculated clumping index, soil absorptance and canopy albedo all have high accuracy, indicating that the model is able to reflect the interaction mechanism between radiation and the canopy-soil system.

Climate and land-atmosphere models rely on accurate land-surface parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR). It is known that FAPAR values retrieved from remote-sensing images suffer from scaling effects. Scaling transformation aims to derive accurate FAPAR values at a specific scale from values at other scales. In this letter, the scaling-effect mechanism and the scale-transformation algorithm are derived using a Taylor series expansion method based on the FAPAR model based on P after simplification. The scaling algorithm was validated in the Heihe River Basin. The multiscale FAPAR values are inverted from 5-, 50-, and 100-m hyperspectral reflectance data. The scale-transformation formula was used, and the results agreed well with actual values.

The physically-based vegetation canopy reflectance model is the basis for accurate inversion of important vegetation parameters. According the interactive process between photons and canopy, canopy reflectance could be divided into two parts: single scattering and multiple scattering reflectance. Recollision probability is a useful tool linking leaf optical properties to canopy reflectance or absorption. In this paper, based on the recollision-probability, a new and practical multiple scattering model was approached. To estimate the accuracy of this model, Monte-Carlo simulation and 3D radiosity model were used and the results showed high accuracy of this model. Then the difference between first recollision probability (p1) and multiple recollision probability(pm) was discussed. Both of p1and pmwere needed in modeling multiple scattering model. At last, the contributions of soil reflectance, leaf albedo and proportion of sky radiation to multiple scattering reflectance were discussed.

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is the fraction of the incoming solar radiation in the Photosynthetically Active Radiation spectral region that is absorbed by a photosynthetic organism. This biophysical variable is directly related to the primary productivity of photosynthesis and some models use it to estimate the assimilation of carbon dioxide in vegetation. The solar radiation reaching to the canopy can be divided into direct radiation and diffuse radiation. The processes of two kinds of radiation are different. But a lot of retrieval models of FPAR did not consider this difference. In this paper, we established a FPAR inversion model which divided direct and diffuse FPAR. And MODIS LAI and surface albedo products were used as the model input data. And at the end of this paper, the inversion FPAR was verified with observation data and MODIS FPAR product.

Climate and land-atmosphere models rely on accurate land-surface parameters, such as the Fraction of Absorbed Photo synthetically Active Radiation (FAPAR). It is known that FAPAR values retrieved from remote sensing images suffer from scaling effects. Scaling transformation aims to derive accurate FAPAR values at a specific scale from values at other scales. In this paper, scaling effect mechanism and the scale transformation algorithm are derived using Taylor series expansion method based on FAPAR-P model, after the model was simplified. The scaling algorithm was validated in Heihe River Basin. The multiscale FAPAR are inversed from of 5 m, 50 m and 100 m hyperspectral reflectance data. The scale transformation formula was used and the results agreed well with the actual values.

Row crop is an important cultivation form in China. It is considered to be a transitional canopy structure between continuous vegetation and discrete vegetation. Lots of physical models of row crop were established to invert vegetation parameters of row crop. Monte Carlo model is a reliable reference for those physical models. In this paper, row crop was modeled as a series of parallel rectangles with infinity length in one direction. The Monte Carlo (MC) simulation model of row crop was established according to the process of photons tracing. Meanwhile, dissipated energy was counted during the simulation process so that FAPAR and BRDF were calculated. The simulation result was analyzed and compared with continuous vegetation. Some influencing parameters of the simulation were analyzed, including LAI (leaf area index), width of rows, width of space between the rows, solar zenith angle and azimuth angle. And the field experiment data was used to compare with MC simulation result. Result shows row crop MC model is reliable.

Grassland is one of the most important parts of ecosystem on the earth. In China, one of the best grass lands is degrading because of draught or effects of human activity. It is important to monitor the growing condition of degrading grasslands. LAI is an important variable which can accurately represent the growing situation of grass. With DSD method, hyper-spectral images from HJ-1A satellite are used to inverse LAI accurately by restraining the effect of background. In this paper, multi spectral image was used to retrieve LAI with BRDF method for comparing. The maps of LAI and degradation level in study area were made. According to the results, the DSD method can retrieve LAI of grassland accurately and the HSI image of HJ-1A is a potential ideal data source for monitoring grassland.

Middle-resolution Leaf Area Index (LAI) data are of great importance to scientific research relating to atmospheric composition, climate and weather, and the hydrological cycle. This paper introduces a physically based LAI retrieval technique for HJ-CCD at 30-m resolution. The algorithm is based on a canopy BRDF model that characterizes the surface reflectance as a function of a series of parameters. There are three key factors that influence the LAI retrieval processes: 1) the preprocessing to estimate surface reflectance; 2) the quality of the input land cover data; 3) the accuracy of the input parameters. Accounting for these factors, a 30-m LAI product of Heihe River Basin in the whole year of 2012 is created utilizing the data from HJ-CCD. Then field measurements are used to evaluate the quality of the product. Results show that the algorithm has the ability to produce LAI products as expected. Future researches will focus on reducing the uncertainties brought by the input data and parameters and implementing this algorithm at national scale over China.

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is the fraction of incoming solar radiation that is absorbed by green vegetation in the spectral range from 400 nm to 700 nm. FAPAR reflects the energy absorption ability of vegetation canopy. It is a critical input in many land surface models, such as crop growth models, net primary productivity models, climate models and ecological models. Existing models for FAPAR retrieval are complex and difficult to retrieve, most of them cannot be used under cloudy weather. In this paper, a new quantitative FAPAR retrieval model considering the diffuse skylight and multiple scattering between canopy and background is introduced to retrieve FAPAR of vegetation canopy. The model was used to continuous vegetation and was validated by Monte Carlo (MC) simulation and field tests. The conclusion shows that the error is less than 0.32%.

Leaf area index (LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions. The scaling effect of LAI has always been of concern. Considering the effects of the clumping indices on the BRDF models in discrete canopies, an effective LAI is defined. The effective LAI has the same function of describing the leaves thick degree with the traditional LAI. The spatial scaling effect of discrete canopies showed significant differences compared with continuous canopies. Based on the directional second derivative method of effective LAI retrieval, the mechanism for producing the spatial scaling effect for discrete canopy LAI has been discussed and a scaling transformation formula for effective LAI has been suggested in this paper. Theoretical analysis showed that the mean values of effective LAIs retrieved from high resolution pixels were always equal to or larger than the effective LAIs retrieved from the corresponding coarse resolution pixels. Both the conclusions and the scaling transformation formula were validated by the airborne hyperspectral remote sensing images obtained in Huailai County, Zhangjiakou City, Hebei Province, China. The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing images.

Validation is one of the most important processes to assess the products of remotely sensed data and to evaluate whether the products accurately reflect the land surface configuration. Leaf Area Index (LAI) is a key parameter which represents the vegetation canopy structure and growth conditions. Accurate evaluation of coarse resolution LAI products is the basis to apply them to the land surface models. In this study, validation methods of coarse resolution LAI products for heterogeneous pixels are established based on the scaling effect and the scaling transformation. Taking spatial heterogeneity and growth difference into account, we transform LAI from field measurements to 1 km resolution scale with the help of higher resolution images, and validate it using MODIS (Moderate Resolution Imaging Spectrometer) and GLASS (Global Leaf Area Index Products) LAI products. Two study areas in Hebi City, Henan Province, China and Yingke Oaisis, Zhangye City, Gansu Province, China were selected for the validation of LAI products. Results show that both MODIS and GLASS LAI products underestimate the true LAI. Compared with MODIS products, GLASS products better reflect the true situation.

Clumping index is a critical physical parameter used to describe the clumping effect of vegetation canopy. In many studies, foliage elements are assumed to distribute randomly in the canopy and the clumping index is 1. However, for the irregularly or artificially spaced discrete vegetation canopies, such as savanna and sparse forests, the assumption is not in accordance with the actual case, the clumping index varies in the range of 0 to 1. As a result, the Leaf Area Index (LAI) retrieved directly from remote sensing data is always underestimated. Optical instruments, such as LAI-2000 canopy analyzer, TRAC, fish-eye camera, are difficult to measure the clumping index for sparse forests directly. In this paper, taking populus euphratica sparse forest in Heihe Basin as the research object, a new method combining hemispherical photography and high resolution images is established to estimate the clumping index. The results show that the method can accurately calculate clumping index and LAI for sparse forests and improve the validation of LAI products in water stressed regions.

Based on the research on the relationship between different vegetation indexes (VIs) at different growth stages of winter wheat, we used the ecological parameters and remote sensing data to construct the winter wheat remote sensing quality model. The results showed that the correlation of NDVI green value on May 11 the grain protein was reached a significant level, which according to optimal as the research object in Beijing area, remote sensing and ecological parameters comprehensive quality model had good prediction effect than the other two models. Therefore, it is feasible and accurate to use remote sensing and ecological data to set up a comprehensive quality monitoring model.

Monitoring grass species and coverage accurately makes a significant contribution to species diversity research and sustainable development of grassland ecosystem. Plants grown in grassland usually own unique spectral characteristics in florescence. Compared with the nutrient stage, species are more easily identified during florescence. In this study, flowers such as Galium verum Linn., Hemerocallis citrina Baroni, Serratula centauroides Linn., Clematis hexapetala Pall., Lilium concolor var. pulchellum, Lilium pumilum and Artemisia frigida Willd. Sp. PI. were identified, using some canopies spectra analysis and feature extraction methods. Validation shows that when the coverage of flowers is greater than 10%, the accuracy of identification methods will be higher than 90%. Based on this result, linear unmixing model is adopted to calculate the area ratios of flowers in quadrates. Results show that linear unmixing model is an effective method for estimating the coverage of grassland flowers with the mean retrieval error of about 4%.

Airborne LIDAR (Light Detection and Ranging) is an active remote sensing technique that measures the properties of scattered light to determine the range and intensity information of a distant target. Many studies have been reported on estimating a suite of forest characteristics such as fractional vegetation cover, leaf area index and canopy height using LIDAR data. The three characteristics of crop canopy also play key roles in vegetation radiative transfer models and yield estimation. But crops are so small and low that more than 95% pulses have ground hit, it is difficult to separate the crop and soil completely, so the methods used in forest may not be suitable for crops. In this paper, based on theoretical analysis, we propose a new method, trying to derive gap fraction of crop field using the airborne LIDAR intensity of ground hits, so we can manage to retrieve the fractional vegetation cover, LAI and the height of crop canopy. We choose corn field as study object, field validation shows that our method can accurately retrieve the three structural parameters of corn field. This study documents the great potential of LIDAR remote sensing for accurately characterizing crop canopies.

Fraction of absorbed photosynthetically active radiation (FAPAR) is one of key variables required in modeling primary production and global climate. FAPAR can be derived from satellite images, for example the MODIS FAPAR. However, validation of the product is a prerequisite for using it to estimate local net primary production (NPP). For this purpose, we carried out in situ measurements of FAPAR in two 2km×2km areas within the temperate meadow-steppe grassland in Hulunber during growing season in 2008. The MODIS FAPAR product reflected very well the seasonal dynamics of in situ FAPAR, but tended to overestimate the value with averaged relative error of 13.7% in the Stipa baicalensis site and 18.7% in the Leymus Chinensis site. More fieldwork for various types of the grasslands is necessary.

Soil moisture is a key parameter for drought monitoring. Crops distribute so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. The soil moisture retrieved from vegetation-soil mixed pixels is a very important problem for drought monitoring and ecological study. Focusing on vegetation-soil mixed pixels, a new method for retrieving soil moisture from hyperspectral data is provided based on scale transformation method. Yingke Oasis, Zhangye, Gansu province was selected as validation area. A Hyperion/EO-1 data acquired on Jul.15, 2008 was pre-processed and linearly interpolated to 180m and 1080m resolution images. Then a multi-scale image series was obtained. Using the above method, the soil moisture of pixels whose space resolution is 1080m were calculated. The retrieved results were verified by synchronized ground observation data. The results show that the proposed method is reliable.

Industry spatial agglomeration is a world-wide phenomenon. As the raw material of tobacco industry, tobacco is the only economic crop which is intensive produced in China at present. The statistic data is used to analyze the regional specialization and agglomeration of tobacco planting industry, but the spatial pattern of tobacco sown area is always neglected. In this paper, Henan province is chosen as the study area, and the distribution pattern of tobacco sown area has been studied using RS and GIS methods. According to the analysis of phenology characters of crops, the tobacco sown area and its growing condition were monitored using two-temporal data, which was in Jan and June 2008 respectively. Then the tobacco sown area of each county was calculated and tobacco patch was derived using ARCGIS 9.0. According to the result, the regional Geordie coefficient which indicates industry agglomeration distribution status and the fragmentation index were studied. Results showed the higher regional Geordie coefficient means the higher planting scale at present, but does not totally equal the higher level of aggregation. The fragmentation index of tobacco sown area shows different trend. So both the economic coefficient and the fragmentation index should be considered during discussing the aggregation level. In order to increase the development potential of Henan tobacco industry, it should increase the planting scale of the central part of Henan province.

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a critical input parameter to many climate and ecological models. Its calculation accuracy from remote sensing images directly influences the estimation of net primary productivity (NPP) and carbon cycle. This paper presents a hybrid model combining the characteristics of geometric optic model and radiation transfer model. It considers the illuminated and shadow area of the canopy and soil, as well as the multiple scattering between the canopy and soil. The Monte Carlo simulations of canopy FAPAR are also conducted and the results are compared with model results. In addition, we did the simulation of FAPAR daily change by the model and MC method, and compare the results with field measured daily data of FAPAR. All the results prove the model effective.

Grassland is an essential part of terrestrial ecosystems. It has a significant impact on the carbon cycle, as well as on climate and on regional economies. Till now, vegetation indices are the most popular remote sensed detecting method of grassland degradation. Although vegetation indices are useful for estimating the biomass, but detecting changes of vegetation indices are not always effective, as grassland vegetation with different characteristics may still produce similar vegetation index values. The development of hyperspectral sensors provides a new approach to solve this problem. The Hulunbeier grassland was chosen as a study object. Reflectance spectra of leaves and pure canopies of some dominant grassland species, as well as reflectance spectra of mixed grass community were measured. Using spectral feature parameterization methods such as spectral slope, spectral derivative, spectral integration, and spectral index, the spectral feather of leaves and pure canopies had been extracted. So the typical grassland vegetation species can be distinguished. Then the spectra of mixed grass community were unmixed using linear mixing models, and the proportion of all the components had been calculated. The field validation proved spectral feature parameterization and pixel unmixing methods in this research are effective.

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.

Most existing multitemporal classification researches use spectral information alone. However, adding spatial structure and temporal correlation in the classification could improve the classification accuracy. This paper proposed a new method to extract multitemporal texture by the Pseudo Cross Variogram (PCV). The derived texture features were combined with the original spectral information for multitemporal classification. The performance of the proposed multitemporal texture was evaluated in land cover classification using bi-temporal hyperspectral CHRIS/PRBOA images. The experiments showed that CHIRS/PROBA data is applicable in multitemporal classification, and including multitemporal texture in multitemporal classification could lead to a significant increase in overall classification accuracy, compared to the classification using spectral information alone.

Leaf area index (LAI) is an important structure parameter of vegetation system. The quantitative remote sensing can offer two dimensional distribution of LAI. The variation of background, atmospheric condition and canopy anisotropic reflectance were the three factors that can restrain the retrieved accuracy of LAI. Along with the emergence of hyperspectral remote sensor, such as Hyperion, it's possible to calculate LAI using the second derivative method in spectral dimension. The second derivative can reduce the influence of background and improve the accuracy of LAI inversion. In order to integrate the second derivative into physical model and eliminate the influence of canopy reflectance anisotropy, we propose a new directional spectral second derivative method. Firstly a new hybrid canopy model was used, and then the directional spectral second derivative was deduced from the hybrid model, so the effects of anisotropy of canopy reflectance and background were removed in theory. Numerical and field tests show the noise can greatly impact the directional second derivative method. We put forward an innovative noise filtering approach in spectral and space domains, the directional second derivative worked well on the LAI retrieval by Hyperion image.

The urban heat island (UHI) becomes a very serious problem in China. In this paper, the UHI pattern of Beijing in the four seasons was studied using ASTER thermal infrared data. The Land Surface Temperature(LST) maps of Beijing, China in the four seasons of 2004 were retrieved using local split window algorithm adjusted according to ASTER data and the atmosphere profiles of Beijing. And a numerical simulation showed the reliability of the retrieval results. It is found that LST in urban and rural areas display evident differences. The differences varied with seasons, and UHI is the most intensive in summer, with the intensity of around 0.6~1.5K, which is better explained by vegetation coverage differences in four seasons. Patterns of LST distribution were found to be closely related to land use types and seasons.

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.

The water vapor is the main absorb substance of the atmosphere in thermal infrared bands, but the distribution of water vapor amount in the ground varied greatly, so one radio sounding couldn’t represent the water vapor distribution of different pixel in one image. AATSR (Advanced Along-Track Scanning Radiometer) sensor could provide radiance measurements of two channels at two observation angles. So it is possible to retrieve the atmosphere parameters and SST (Sea Surface temperature) synchronously. In order to make the system of equations determined, the two atmosphere parameters that could express whole atmosphere profiles must be confirmed. As the retrieval accuracy is requested, the surface reflected atmospheric downward radiance effect couldn’t be ignored. A climatologically data set of 1761 different radio soundings, the SATIGR database, have been used to develop a new atmospheric correction method, the two atmosphere parameters have been proved that could represent the transmittance, atmospheric upward radiance and downward radiance of different wave band and different view angle, they are the transmittance and atmospheric upward radiance of 11μm at the nadir view angle. Introducing the appropriate sea surface BRDF model, the emissivity and the reflected downward radiance can be calculated precisely. So the SST, wind speed, and two atmosphere parameters are retrieved by Broyden iteration method, when the initial values are found using LUT (look up table) method. Simulations showed :(1) the physics-based algorithm described in this paper could provide more accurate calculation of atmospheric effect. Compared with split window method, the retrieval error of SST is little. (2) The new downwelling atmospheric radiance correct method described in this paper could provide more precise calculation of atmospheric effects. The field test using ATSR-2 (Along-Track Scanning Radiometer- 2) DATA proved this new algorithm can improve the retrieval accuracy significantly

The Moderate-resolution Imaging Spectroradiometer (MODIS) is an earth-imaging instrument now onboard both the Terra and Aqua polar-orbiting satellites. MODIS has 36 spectral bands, covering the spectral range from 412 nm to 14 200 nm, and provides spatial images with 0.25 km (two bands), 0.5 km (five bands) and 1 km (29 bands) resolutions at nadir. These three application products are obtained mainly through the processing on level IB product for global studies of the Earth's land, oceans and atmosphere. This paper provides a brief description of the MODIS characteristics and MODIS IB saved file format-HDF, and describes an approach for extracting the data for the purpose of high level product. The Vegetation Indices Product's generation methods were derived as the example at the end.

Monitoring the sowing area and the crops growth are the two basic aspects of agricultural remote sensing. With the advantage of DMC+4 simultaneously providing mid-resolution multispectral image and high-resolution panchromatic image, we choose winter wheat as investigated object, establish a vegetation canopy radiation model on the consider of sun-target-sensor geometry and clumping effect to monitor the spatial distribution of the growth condition of winter wheat, and then validate the model through mathematical simulation and field experiment. The work indicates that the model and retrieval method used are available. The spatial scaling effect of LAI is to be further studied.

Mixed pixel separation has always been a hotspot issue of quantitative remote sensing and is especially important for hyperspectral imagery. A new method based on independent component analysis is proposed in this paper to blind separate component information including the signature and the weight from the spectrum of mixed pixels. The extra information is obtained from the statistical characters of the signatures. To evaluate the performance of the algorithm some computer numerical simulations are conducted and a method to choose a best band coverage of the spectrum using for blind signal separation is proposed. Finally the algorithm is applied on the HYPERION imagery of Henshan, Shanxi Provience and the result showed that the method is effective.

Based on the matrix formula L(/spl theta//sub k/)/sub K/spl times/1/=W/sub K/spl times/J/Lb(T)/sub J/spl times/1/, the component temperature can be inversed by inverse matrix W/sub K/spl times/J//sup -1/. But the correlation of multi-angle infrared radiance was obvious, the retrievable parameters and inversion result were unsteady, it was influenced by various physical and structural parameters. We chose the row crop as the example to discuss the problem of component inversion in detail. When the physical and structural parameters were fixed, the structural pattern of row crop such as the layered mode of canopy also affected the weight of different components significantly, and the retrievable parameters were changed correspondingly. So the appropriate structural pattern should be chosen according to the retrieving parameters and components of object. The iterative method was established to inverse component temperature with the combining inverse matrix using above-mentioned research. First, we calculated the initial value by inverse matrix method, and then gradually gained the real solution by the iterative formula. The result of simulation and field experiment showed that the method can improve the retrieving accuracy and stability remarkably.

The application of TM images and digital elevation model (DEM) is rising and promising in the field of landscape ecology and land cover change detection. This study presents it, and GIS-based technology route is recommended in the TM application. The study area is Yongsheng County, Yunnan Province, P. R. China. Six Landsat TM images are acquired over Yongsheng County with geometrical correction, procession and classification. Landscape types, including farmland, urban area, forest, watershed, wasteland and selected farmland are obtained as areas of interest. The elevation and grade maps of Yongsheng County are produced on its digital elevation model (DEM) with a scale of 1:250,000. We group the elevation into five levels: 1,000-1500 m, 1500-2000 m, 2000-2500 m, 2500-3000 m, and above 3000 m; and four levels are determined for grade maps, i.e., 0-6, 6-15, 15-25, and above 25 degrees. Under the help of GIS tools, area of six landscape types on five elevation levels is calculated separately. We also obtain area of farmland on each elevation and grade level through the same methodology. All these data work in the subsequent analysis of landscape pattern. It is found that farmland mostly locates in areas with elevation below 2,000 m and grade less than 15 degrees, for example, 'candid' areas. We can interpret it by the great ease these candid areas provide for farm work. The Index of Component Fragmentation Degree (ICFD) is used to evaluate human's disturbance on farmland. ICFD is relatively small for 'candid' areas due to comparatively large farmland patches, but maximizes itself where elevation is from 2,000 to 2,500 m. It gives a signal of serious human disturbance there. This disturbance is just deforestation for farmland on this belt. We also analyze the landscape pattern of urban areas. Its area reaches a maximum on the belt with elevation between 1,500 and 2,000 m. It's also considerable that urban area decreases as the elevation increases and reaches almost ZERO where elevation is above 3,000 m. Results presented in this paper suggest that farmland landscape pattern shows its vertical variance according to elevation ranges, and human's disturbance weights much in this variance. This study also provides information on deforestation for farmland, and it seems valuable in environmental protection and region regulation

Independent component analysis (ICA) is a powerful new method that use information contained in higher order cross-moments of multivariate data to find a linear representation of non-Gaussian data, so that the components are statistically independent. As the perpendicular vegetation index (PVI) of mixed pixel can be expressed by the linear summation of the PVI value of components, we can use remotely sensed time series of PVI to retrieve the area proportion and crop condition of components in mixed pixel by ICA. An ICA based approach is proposed for quantitatively separating the area and crop condition of mixed pixel and solving uncertainty of ICA. Because the area proportion of components is fixed during the growing period, and the sum of area proportion of components in pixel is equal to 1, the uncertainty of ICA, therefore is solved perfectly. The numerical simulation shows the area of soil and wheat and the multi-temporal perpendicular vegetation index of the wheat can be quantitatively retrieved synchronously by ICA. Three components: rape, wheat and soil in mixed pixel can also be separated exactly. Even the difference of crop condition among pixels can be retrieved exactly. The retrieval error of area proportion and PVI value of crop is less than 5 %, when no input error is added. As adding 20% input error, the retrieval error can also be restricted within 10%. In order to make the method in use, we try to inverse the area proportion and crop condition of components in mixed pixels using MODIS reflectivity product. The study area lies in Hebei Province. The main crop in the period is wheat. The area and PVI value calculated by ETM image validated the retrieval result.

The retrieval of component temperatures from mixed pixel over vegetable-soil system is more valuable than the retrieving of average temperature. However, there are two essential mechanisms such as component thermal radiation and atmosphere correction must be newly considered. A good mathematic scheme should also be employed, which can make use of the ATSR-2 information in order to separate component temperatures. This paper is an attempt for the above topics. We bring forward more appropriate atmosphere parameters and retrieve them by resolving a system of nonlinear equations, which also includes vegetable and soil temperature as unknown variables. To resolve it, Broyden's method is adopted. We use the ATSR-2 imagery on a pilot field in Shunyi county on April 16, 2001 to validate our method, and errors of 2 degC and 1 degC are achieved for soil temperature and vegetable temperature respectively.