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.

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.

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.

As the only multispectral earth observation data in the 1970s, Landsat MSS plays a significant role in time-series researches. However, as the bands of MSS are much more different compared with the later Landsat TM sensor, the consistency and correction method of reflectance and vegetation indices needs to be studied. In this paper, we use the PROSAIL model to simulate vegetation-covered reflectance profiles and calculate the effective reflectance over the MSS and TM bands. The method of correcting MSS-based NDVI to TM-based NDVI and the method of calculating the MSS-based NDVI after correcting the MSS reflectance to TM reflectance are proposed. A case study in Qinshui, China shows that the correction coefficients of PROSAIL simulated data perform well in the real NDVI image comparison for the vegetation-covered area.

Magnetotelluric detection is an efficient method, which is being tried to detect coalbed methane. However, the interpretation of electromagnetic signals for coalbed methane is now empirical. The forward and inversion modeling results obtained by only treating coalbed methane as a low-resistance body are not completely consistent with the measured data (such as radiation anomalies in specific frequency bands). We think the electromagnetic radiation of coalbed methane due to electrokinetic effect and piezoelectric effect should not be ignored. Starting with the simplified underground electric dipole model, we establishes a new electromagnetic model of coalbed methane to better match the measured data of coalbed methane reservoir regions.

The PROSAIL (PROSPECT + SAIL) model has been utilized in the retrieval of many vegetation biophysical and biochemical variables such as the leaf area index (LAI) and the leaf chlorophyll content (LCC). However, less attention is paid to the impact of spatial heterogeneity on the PROSAIL model. This paper uses a linear-mixing model to simulate within-pixel spatial heterogeneity. Simulated spectra are resampled to Sentinel-2's bands, and several common spectral indices are calculated. Results show that spectral indices calculated from mixed spectra can deviate up to 10% compared to those calculated from pure spectra when spatial heterogeneity comes to 0.5 (measured in Coefficient of Variation, CV). We further use four widely used machine learning methods for LAI inversion, i.e., Supporting Vector Regression (SVR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Neural Network (NN). Concerning R-square and RMSE, all models trained with pure spectra show performance decline when tested on mixed spectra, which can be partially mitigated by using mixed spectra for model training.

Based on the existing geological data and a large amount of research data in the study area, the article compared and analyzed the difference in hyperspectral reflectance between the potential enrichment area and the reference area. The use of Sentinel-2 multispectral images has been investigated to construct the surface recognition model of coalbed methane enrichment areas from two aspects: vegetation covered area and bare areas. Firstly, semi-automatic endmember extraction is implemented by adopting a more pratical spectral un-mixing method. And the vegetation research area and min-eral research area are separated accordingly. In the vegetation research area, the rich red edge information of Sentinel-2 is used to construct an anomaly recognition model. While in bare soil areas, advanced information processing technology is used to extract weak mineral alteration anomalies. The re-sults are validated with field data.

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.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) products at regional and global scales have already been extensively and routinely generated from medium-resolution sensors. However, there is a lack of high-resolution LAI/FPAR product, which is especially essential for crop growth and drought monitoring of cropland in patches. This article proposes a processing framework for the derivation of decameter cropland LAI and FPAR in the Northern China plain from Sentinel-2 surface reflectance data with a random forest (RF) algorithm by exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The training database is generated from the spatially aggregated Sentinel-2 surface reflectance and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR product over homogeneous cropland, and the training samples are strictly filtered for the best quality. RF is then trained over the processed Sentinel-2 surface reflectance and the filtered MODIS LAI/FPAR under two input groups—one group is for Sentinel-2 spectral bands of 10-m resolution only, and the other group supplements the Sentinel-2 red-edge (RE) and shortwave infrared (SWIR) bands of 20-m resolution. Extensive comparisons and validation are carried out, and they demonstrate that the new method can generate spatial and temporal consistent LAI/FPAR with MODIS at high spatial resolution. The retrieval accuracy is slightly better for 20-m input groups than that for 10-m input groups, confirming the value of RE and/or SWIR in cropland LAI/FPAR estimate. This article also demonstrates that GEE is a suitable high-performance processing tool for high-resolution biophysical variables estimation.

Land surface temperature (LST) is a crucial parameter in the energy and material balance of land surface system. The angle effect of LST makes the accuracy of LST restricted and limits the application of remote sensing LST product. In order to eliminate the influence of viewing angle, this study proposed a novel method to perform angular normalization by constructing a feature space of surface emission radiance and fractional vegetation coverage (Radiance-FVC space). The proposed approach is applied in Hetao Plain as an example. It is found that the Root Mean Square Error (RMSE) can reach 5.1K, and the angular normalization effect is more significant for pixels with larger viewing zenith angle.

Hyperspectral field spectra are an essential foundation for the construction and evaluation of many vegetation indices and vegetation models. However, they are often contaminated by water vapor absorption and instrument noise. In this paper, a new denoising method based on a denoising autoencoder (DAE) is proposed, which was trained with simulated spectra generated by the radiative transfer model, PROSAIL. The performance of this new method was evaluated on both simulated and real-world spectra and was compared to that of several most used spectral denoising methods. Although limited by the soil spectra used for dataset generation, DAE looks remarkably promising in the denoising of vegetation field spectra, and works especially well in the removal of water vapor effects.

Agricultural drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. The global 0.05° spatial resolution data set has been obtained using the data-driven algorithm method. However, the broken farmland is not conducive to regional agricultural drought monitoring. Hence, 0.05° SIF products should be downscaled. On this basis, a convolutional neural network (CNN) downscaled work was conducted in this article to obtain 0.008° spatial resolution SIF results. The downscaled SIF and land surface temperature (LST) data were used to establish the temperature fluorescence dryness index (TFDI). The new TFDI was subsequently used for monitoring agricultural drought in Henan province (China) during the corn-growing season (from June to October 2013-2017). Results showed that the downscaled SIF data exhibit a good correlation with gross primary productivity (GPP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) than 0.05° SIF products. During the study period, the soil moisture fluctuation corresponded well with precipitation, and the value of TFDI had an opposite fluctuation with soil moisture. Meanwhile, the annual averaged TFDI had a high correlation with summer corn yield (R = -0.84). In conclusion, the SIF results through the CNN-based downscaled method were reliable, and the new TFDI was suitable for region agricultural drought monitoring.

The IASI NH3R-I total column products provide a new perspective for monitoring global atmospheric ammonia (NH3) concentration. However, there are few evaluations on its correlation with vegetation conditions. In this paper, we use binning and resampling to evaluate the relationship between IASI-retrieved NH3concentration and MODIS-retrieved vegetation indices from 2008 to 2016. As the results show, there is a significant positive correlation between NH3concentration and vegetation conditions. The relationship has a clear intraannual pattern, while it has great spatial heterogeneity. Another finding is that nighttime NH3observations generally have a better correlation with vegetation conditions. Still, deeper investigations and model simulations are needed to explain the mechanism behind this relationship and its spatial and temporal patterns.

The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5-7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images.

Soil moisture content is an important parameter in hydrological, meteorological, and agricultural applications. Balenzano et al. proposed the alpha approximation method in 2011 for solving some complex issues during the retrieval of soil moisture over agricultural crops with synthetic aperture radar data. However, determining the constraints and solving the underdetermined system of equations in this method add new challenges. Considering the questions of constraints and underdetermined system of equations, the alpha approximation method is used to augment the measured data, and can avoid solving the underdetermined system of equations with constraints directly. Then, these data are applied in a support vector regression machine for soil moisture estimation. It is found that when an optimal model is determined, the method proposed in this article is superior to the direct use of the alpha approximation method, and the root-mean-squared error (RMSE) decreased from 0.0775 to 0.0339 and R2increased from 0.0467 to 0.6491. In addition, the method obtained a good result from a data set collected that included a different growing period of crops by changing the standardized method from StandardScaler to Scale, where the RMSE is 0.0501 and R2is 0.3204. This indicates the good generalization capability of this method. In conclusion, the proposed method solves the two questions effectively and provides a potential way for long-time or large-scale soil moisture monitoring with much less in situ measurements.

Urban area is the main and important space of human activities with a large number of population. Compared with rural and other natural areas, the dense buildings and high-intensity land use are the most different features of urban areas. Therefore, the urban area has obvious texture in remote sensing images. Effective and accurate identification of urban area can play an important role in urban study, urban planning and other urban-related fields. In this paper, a new method based on urban and non-urban scene classification using Convolutional Neural Network (CNN) technique is developed to identify the boundary of urban areas and is applied in Beijing as an example. An acceptable result of the urban area identification was obtained, indicating a great potential of deep learning method in urban related studies.

Nitrogen is crucial for crop growth and nitrogen fertilization is essential for agriculture production. Nitrogen deficiency can reduce the yields, while excessive nitrogen fertilization can pollute the environment and cause the economic losses. Leaf nitrogen concentration (LNC) is an effective indicator for assessing nitrogen content of crops and guiding nitrogen fertilization. The red edge (RE)-based and chlorophyll-related vegetation indices (VIs) have already been used to estimate LNC. However, the VI-based methods only use limited information provided by multispectral remote sensing data. The optical sensors onboard recently launched satellites, such as the WorldView-2 and Sentinel-2, have already included RE band, as well as other useful bands, for crop monitoring. To make full use of such multispectral data, the ANN-based retrieval models were constructed for LNC estimation of winter wheat using WorldView-2 and Sentinel-2 imagery, respectively. Results show that the ANN-based methods can achieve better accuracies compared with the VI-based methods. The consistency between the estimated LNC of WorldView-2 and that of Sentinel-2 is satisfactory, while the difference between the two estimated LNC remains to be improved.

Canopy water content (CWC) is one of the factors that influence photosynthesis and evapotranspiration. Vegetation indices have been proposed to describe the relationship between spectral profiles and canopy water content. In this paper, we propose a retrieval method using the common SWIR bands of satellites (such as sentinel-2), and develop a split-window algorithm to weaken the effect of other influential factors. PROSAIL model and the SPARC03 dataset are used for validation by resampling the hyperspectral data to board bands using spectral response function of Sentinel-2. The result shows that the method can retrieve the canopy water content of different species of vegetation, with a satisfactory R of 0.669. After dividing the vegetation into two classes, both classes have an improvement with R of 0.783 and 0.939.

Building extraction from high-resolution remote sensing images has widely been studied for its great significance in obtaining geographic information. Many methods based on deep learning have been tried for the task; however, there is still much to explore about designing layers or modules for remote sensing data and taking full use of the unique features of buildings like shape and boundary. In this letter, an end-to-end network architecture based on U-Net is proposed. The U-Net architecture is modified with Xception module for remote sensing images to extract effective features. Also, multitask learning is adopted to incorporate the structure information of buildings. Two standard data sets (Massachusetts building data set and Vaihingen Data set) of high-resolution remote sensing images are selected to test our model and it achieves state-of-the-art results.

Leaf chlorophyll content was a mediate indicator for monitoring winter wheat growing status. Based on 8-day composited time series dataset of MODIS surface reflectance with 1 km spatial resolution, a new Modified Ratio Vegetation Index (MRVI) was developed for monitoring leaf chlorophyll content of winter wheat in Hengshui City, Hebei Province of China. Comparing with the existing vegetation indexes, MRVI showed the better linear correlation with leaf chlorophyll content, with R2of 0.62 and RMSE of 8.34. The MRVI also revealed spatial patterns of leaf nitrogen status of winter wheat all over the Hengshui City in 2017, with R2of 0.73 in pure pixels and R2of 0.59 in all pixels. Our results demonstrated that MRVI would be a timely, economically and promising way for monitoring winter wheat nutritional condition from MODIS data in the future.

Land Surface Albedo is an important surface parameter and is widely applied to the surface energy balance, mid-term and long-term weather forecast and atmospheric general circulation model. GF-4 satellite is the first geostationary orbit satellite which combines high spatial resolution and high temporal resolution in China. In order to explore the feasibility of semi-empirical kernel-driven BRDF model applied on GF-4 satellite data, the earth's surface classification is joined to give kernel factors initial value, and Powell iteration algorithm is used to optimize the result of model. Then the land surface narrow band albedos of each band could be gained through angle integration on BRDF model. On this basis, combining spectral library with spectral response function of GF-4 satellite, the conversions of narrow to broadband albedo for GF-4 satellite data is built for the first time. And albedo inversion in short wave band (0.3-3μm) are acquired. Finally, cross validation used by MODIS albedo product indicates that an accurate land surface albedo could be acquired by this method.

Due to limitation of observation angles and resolution of satellite sensor, the resolution of surface albedo products retrieved based on angle information is usually coarse, such as MODIS albedo products and GLASS albedo products. Downscaling method of stratified linear regression proposed in this study solved this problem. First, under the assumption of non-anisotropy surface, Landsat8 primary albedo is obtained by converting narrow albedo to broadband albedo. Under the resolution of 500m, the correlation degree of Landsat8 primary shortwave albedo and MCD43A3 shortwave albedo shows higher after classification. Therefore, a linear regression model for each land cover is established. By fusing Landsat8 data with MCD43A3 albedo, downscaled shortwave albedo with high-resolution is obtained. Finally, it is validated with data of four observation sites in the United States. The results show that downscaled albedo has high precision (bias is 0.01 and sd is 0.012) and rich details, and the algorithm is reliable for different land cover, indicating its potential to become an operational algorithm for high-resolution albedo product.

Spectral vegetation indices are powerful tools in statistically estimating leaf area index (LAI) with remotely sensed imagery. However, the band selection in some generic vegetation indices influenced their performance to a great extent due to the rapid development of new sensors. As the latest launched satellite carried with multispectral sensors, Sentinel-2 provides 3 extra red-edge bands and 1 extra SWIR band. For the purpose of statistical LAI retrieval based on Sentinel-2 data, the optimal bands combination of the DVI, SR and NDVI-formed spectral indices were selected on the basis of correlation analysis. The experiment results demonstrated that band 7 (red-edge) and 8 (near-infrared) of Sentinel-2 MSI data were the optimal bands combination of LAI retrieval. And finally the optimal spectral index were validate with the in-situ LAI observations, which performed satisfied estimated accuracy.

The PROSAIL (Prospect+SAIL) canopy radiation transfer model is the most widely used physical model in vegetation parameter inversion, however, it is hard to achieve a balance between the accuracy and efficiency of the retrieval. In this paper, a PSO-coupled (particle swarm optimization) bi-LUT (look-up table) was used to retrieve LAI (leaf area index) and LCC (leaf chlorophyll content) from Sentinel-2 MSI (multi-spectral instrument) images, which largely reduced the time consumption of the inversion. The results were validated with ground measurements. The RMSE of retrieved LAI and LCC were 0.79 m2/m2and 7.1 μg/cm2, separately.

Timely and accurate knowledge of crop yield information is of great significance for guiding agricultural production, formulating agricultural policies and controlling the balance of food supply-demand. In the process of yield formation, spectral analysis technology, as a fast and non-destructive method, has been widely applied to detect the photosynthetic capacity indexes, which is highly correlative with the final crop yield. In this study, the spectral information of winter wheat leaves was obtained in three important phenological phases. The two-dimensional (2D) correlation spectrum analysis modified by the mutual information (MI) was then brought in, and the dynamic spectra were got by using crop yields as the perturbation quantity. After the analysis of the photosynthesis mechanism and 2D synchronous correlation spectra in three periods, three groups of wavebands were selected as the sensitive spectral information to the final yields. The principal component analysis (PCA) was conducted among the acquired wavebands. The crop yield was forecasted by the principal components from the selected wavelengths in different phenological phases. PLS model, the model based on neural network and the neural network prediction model optimized by the genetic algorithm were established separately. After the comparison, the BP Neural network model optimized by the genetic algorithm got a significant improvement in yield estimation accuracy. The results offered the rapid, convenient and valuable guidance for the agricultural production.

Diverse storage formats, archive dispersal, and inconsistent naming make it difficult for researchers and the general public to find and access remote sensing data. To facilitate the use of remote sensing data, this paper provides an integrated framework for direct reading remote sensing data in a widely compatible and analysis-ready format, NumPy ndarray. The framework is composed of two main components. One is the raster data processing and storage model. All the operational gridded remote sensing data are split into tiles, and reorganized in n-dimensional array. Then the N-Dimensional data array is serialized into netCDF and stored into distributed file system. The other is the spatiotemporal filter to achieve parallel query, and it has been encapsulated into Internet-accessible application programming interfaces (APIs). The scenario of calculating NDVI of a specified spatiotemporal range given at last illustrate the efficiency and convenience of our platform provided for remote sensing data analysis.

Urban thermal environment plays a crucial part in urban disaster prevention, urban planning, and environmental protection. Urban shadow distributions and land surface components are considered as the most influential factors in the thermal radiation of urban environment. This paper proposes a new method to determine the two factors from high-spatial-resolution digital surface models (DSM) and remote sensing multispectral images respectively, and then urban thermal radiation can be determined by combining temperature measurement on the ground level. Finally, The proposed method is applied to simulate the urban thermal radiation in Beijing as an example, using a three-meter DSM and Landsat 8 images.

This letter presents a novel approach for extracting accurate outlines of individual buildings from very high-resolution (0.1-0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph G(V,E) is constructed based on the endpoints of those extracted line segments. The building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of building roofs and outperforms the state-of-the-art method.

Leaf area index (LAI), an important parameter describing a crop canopy structure and its growth status, can be estimated from remote sensing data by statistical methods involving vegetation indices (VIs). This letter reports the development of a new VI, the inverted difference vegetation index (IDVI), for crop LAI retrieval. The IDVI can overcome the saturation issue of the normalized difference vegetation index (NDVI) at high LAI values and exhibits robust insensitivity to crop leaf water and chlorophyll content. By combining the IDVI and NDVI with a scaling factor, we constructed a novel statistical regression model with parameters that can be calibrated to a specific region to estimate the LAI. Validations on simulated data and in situ observations show that the proposed retrieval method with the IDVI is stable for low and high LAIs and obtains better results than the empirical method involving the NDVI at the regional scale. Findings in this letter will benefit future agricultural applications.

Land surface temperature (LST) is a key surface feature parameter. Temperature and emissivity separation (TES) and split-window (SW) algorithms are two typical LST estimation algorithms that have been applied to a variety of sensors to generate LST products. The TES algorithm can synchronously obtain LST and emissivity, but it requires high accuracy for atmospheric correction of the thermal infrared (TIR) data and does not perform well for surfaces with low spectral emissivity contrast. On the contrary, the SW algorithm can retrieve LST without detailed atmospheric data because the linear or nonlinear combination of brightness temperatures in the two adjacent TIR channels can reduce the atmospheric effect; however, this algorithm requires prior accurate pixel emissivity. Combining the two algorithms can improve the accuracy of LST estimation because the emissivity calculated from the TES algorithm can be used in the SW algorithm, and the LST from the SW algorithm can then be applied to the TES algorithm as an initial value to refine emissivity and LST. This paper investigates the aforementioned hybrid algorithm using Chinese Gaofen-5 satellite data, which will provide four-channel data for TIR at 40 m for synchronously retrieving LST and emissivity. The results showed that the hybrid algorithm was less sensitive to instrument noise and atmospheric data error, and can obtain LST and emissivity with an error less than 1 K and 0.015, respectively, which is better than those obtained with the single TES or SW algorithm. Finally, the hybrid algorithm was tested in simulated image and ground-measured data, and obtained accurate results.

Leaf Area Index (LAI) is an important parameter in describing leaf density and canopy structure of plants, which could be estimated by remote sensing data conveniently by empirical methods with vegetation indexes. Due to the saturation of Normalized Difference Vegetation Index (NDVI) in high LAI value, Inverted Difference Vegetation Index (IDVI), which possessed a robust insensitivity on leaf water content and chlorophyll content, was proposed in this paper. Then we combined the statistical regression model base on NDVI and IDVI with a dynamic scale factor to estimate LAI. Our result demonstrated that this new retrieval method was quite stable in both low and high level of LAI, which indicated that it would be a promising way to retrieval LAI from remote sensing data in the future.

Fractional Vegetation Cover (FVC) describes the distribution and growing condition of vegetation on land surface ecosystem, and it could be estimated in regional and global scale with remote sensing techniques conveniently. The multispectral sensor equipped on China's GaoFen-4 (GF-4) geostationary satellite which was launched in December 2015 has a high spatial resolution of 50 m, thus makes it a powerful data source for vegetation monitoring and FVC estimation in large spatial scale timely. In this paper, we conducted a FVC estimation experiment in Northeast Inner Mongolian, China with GF-4 data acquired in August 2016 based on 2 most widely used method, and the estimation results were validated by a simultaneous Unmanned Aerial Vehicle (UAV) measurement afterwards. Our results showed that the vegetation index method with pure endmember pixel of soil and vegetation NDVI (Normalized Difference Vegetation Index) value extracted in a 2-dimensional spectral space was most accurate with the near ground UAV measurement results, and it would be a promising method in GF-4 FVC estimation in northern temperate semi-arid area of China in the future.

In order to improve the low spatial resolution of remote sensed LST, two methods based on multiple scale factors are proposed. Considering the optimal scale factor is usually not unique under environment of different land cover types, a stratified linear regression model is built, which shows a higher accuracy than global linear regression with one scale factor. In view of the relationship between scale factor and LST showing fluctuant under different conditions, such as season, size of scale and land cover type, where the errors of linear regression model often come from, a method of downscaling based on BP neural network is proposed. Multiple scale factors as input, this method shows as almost precise as the first method globally, especially for mountainous areas and mixed areas, which shows a stronger robustness.

In hyperspectral thermal infrared remote sensing, iterative spectrally smooth temperature / emissivity separation (ISSTES) is currently the most popular method to retrieve land surface temperature (LST) and emissivities (LSEs) at the same time. However, a serious problem may occur when noise reaches certain intensities, which causes ISSTES to fall into a false minimum, and thus the errors of LST and LSEs are far beyond tolerance. In this paper, both simulated and measured data were used to show how the problem would occur, and the ISSTES-Extreme (ISSTES-E) method was proposed to fix the problem. The results reveal that the new method is able to prevent the false minimum when the original method fails to come to a valid answer.

Leaf area index inversion using remote sensing is crucial for obtain vegetation information and monitoring global climate change. For the invalid uniform continuous canopy hypothesis of SAIL model, fractional canopy cover (FCC) is introduced and a simultaneous retrieval method of LAI and FCC is developed. SAIL model, PSO algorithm, and linear spectral mixture theory are combined in the novel method. Different fitness functions are designed and tested with field measurement data and Landsat-8 OLI data. Results show that the underestimation of LAI of canopy caused by the invalid hypothesis of SAIL model is well restrained when appropriate fitness function is adopted. The RMSE of the new method is only 0.489, which indicates the satisfactory retrieval performance. Therefore, the novel method is suggested as an effective LAI inversion technique.

The objective of this letter is to extend the alpha approximation method, a method proposed by Balenzano et al., for soil moisture retrieval from multitemporal synthetic aperture radar (SAR) data. The original alpha approach requires an initial estimate of the upper and lower bound soil moisture values to constrain the soil moisture retrieval. This letter demonstrates an extension of the alpha approach by employing the juxtaposition method to adaptively set the soil moisture bounds using the absolute radar backscatter values. This extended alpha method was tested using an airborne time series of L-band SAR data and coincident ground measurements acquired during the SMAPEx-3 experiment over bare agricultural fields. The agreement between estimated and measured soil moisture values was within a root-mean-square error of 0.07 cm3/cm3for each of the three polarization combinations used (i.e., HH, VV, and HH and VV). Moreover, inclusion of the two-polarization combination (HH and VV) slightly improved the retrieval performance. The proposed extension to the alpha method makes the most of the information contained in the SAR data time series by using dynamic, spatially explicit soil moisture bounds retrieved from the SAR data themselves.

Damaged building detection from high spatial resolution remote sensing image helps to rapid disaster losses assessment. However, the majority of traditional methods relies on only a single category feature of the damaged building. This letter presents a new strategy for detecting damaged buildings from postquake remote sensing image by multiple-feature analysis, in which the integrity of the building edge and the interior roof was both considered. The intactness of the building edge was assessed by proposing a new feature parameter, edge significance (ES), ES using significance test to quantify the difference between the gradient values on the edge and in the edge buffer. In addition, the gradient orientation inside the building was analyzed and local gradient orientation entropy (LOE) parameter was adopted to determine whether the interior roof was damaged. In general, damaged buildings have lower ES values because of broken edges and higher LOE values owing to debris, final decision was made on the basis of both feature parameters. A Quickbird image of Yushu, China, was used in the experiment and, among a total of 327 buildings, 266 were detected correctly. The overall accuracy was 84.10%, which is better than traditional methods.

The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data.

The aim of this paper was to estimate soil moisture in agricultural crop fields from fully polarimetric L-band synthetic aperture radar (SAR) data through the polarimetric decomposition of the SAR coherency matrix. A nonnegative-eigenvalue-decomposition scheme, together with an adaptive volume scattering model, is extended to an adaptive model-based decomposition (MBD) (Adaptive MBD) model for soil moisture retrieval. The Adaptive MBD can ensure nonnegative decomposed scattering components and allows two parameters (i.e., the mean orientation angle and a degree of randomness) to be determined to characterize the volume scattering. Its performance was tested using airborne SAR data and coincident ground measurements collected over agricultural fields in southeastern Australia and compared with previous MBD methods (i.e., the Freeman three-component decomposition using the extended Bragg model, the Yamaguchi three-component decomposition, and an iterative generalized hybrid decomposition). The results obtained with the newly proposed decomposition scheme agreed well with expectations based on observed plant structure and biomass levels. The new method was superior in tracking soil moisture dynamics with respect to previous decomposition methods in our study area, with root-mean-square error of soil moisture estimations being 0.10 and 0.14 m3/m3, respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface.

The urbanization process changed the urban ecological land and consequently affected the quality of urban residents' environment, and it was very important to obtain urban ecological land cover information. In this paper, an object-oriented method was proposed to extract urban ecological land cover from the multiple-channel images acquired by Chinese Gaofen-1 (GF-1) satellite. Taking Beijing City as an example, five ecological land covers, including water, vegetation, road, building land and bare land, were classified using new classification rules based on the spectral, geometry and texture information in the GF-1 image. The result showed that the urban land covers were accurately identified and its validation accuracy was up to 90%.

This paper presents a novel deep hierarchical representation and segmentation approach for high resolution remote sensing image understanding. An information extraction approach using deep hierarchical exploitation for remote sensing image is presented. The key idea is that we adopt a fast scanning image segmentation within a deep hierarchical feature representation framework, using a deep learning technique to split and merge over-segmented regions until they form meaningful objects. The contribution is to develop an effective procedure for multi-scale image representation to address the issue of information uncertainty in practical applications. We test our method on two optical high resolution remote sensing image datasets and produce promising experimental results in the form of multiple layer outputs, which confirm the effectiveness and robustness of the proposed procedure.

Road damage detection from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pair of pre-disaster and post-disaster road data for change detection are difficult to obtain due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e. remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, aspect ratio are selected form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads were detected by applying the knowledge model. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake in May 15, 2008. The results show that the producer's accuracy (PA) and user's accuracy (UA) reached about 90% and 85% respectively, indicating that the proposed method is effective for road damage detection. This approach also significantly reduces the need for pre-disaster remote sensing data.

This paper presents a new method to detect damaged buildings caused by earthquake from high spatial resolution remote sensing image. We found that the probability of multiple gradient orientations is greater in a local area within a damaged building than that in a local area within an intact building. Therefore, a new feature (Local Gradient Orientation Entropy, LGOE) was put forward to determine whether a building was damaged. First, gradient information was obtained by Prewitt gradient operator. Second, the gradient orientation entropy of one pixel was calculated in a local 3 ×3 window. Last, average LGOE value within a building boundary was counted. In general, damaged buildings have higher LGOE values because of their irregular texture. Therefore, an optimum LGOE threshold value (LGOET) was set to detect damaged buildings. The experiment results of Yushu earthquake using a Quickbird image demonstrated that our method was effective. Of the total 101 buildings, 87 were detected correctly, the overall accuracy was 86.14%, and the overall kappa coefficient is 72.25%.

Super Low Frequency electromagnetic prospecting methods, based on natural source, have seen an increasingly trend in geophysical applications. It is known that natural source electromagnetic signal is weak, and how to extract useful information has drew great attention wordwidely. In this paper, we designed a signal processing method, an adaptive filter, to filter out the strong power frequency interference at 50Hz and its harmonics mixed in the output signal from induction magnetic sensors. As the output could change with the input, the adaptive filter system is releated to its input signal closely and specificly. Because of its stronger adaptability and better filtering performance, the adaptive filter showed good effectiveness.

Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.

This letter presents a new approach for rapid automatic building extraction from very high resolution (VHR) optical satellite imagery. The proposed method conducts building extraction based on distinctive image primitives such as lines and line intersections. The optimized framework consists of three stages: First, a developed edge-preserving bilateral filter is adopted to reduce noise and enhance building edge contrast for preprocessing. Second, a state-of-the-art line segment detector called EDLines is introduced for the real-time accurate extraction of building line segments. Finally, we present a graph search-based perceptual grouping approach to hierarchically group previously detected line segments into candidate rectangular buildings. The recursive process was improved through the efficient examination of geometrical information with line linking and closed contour search, in order to obtain more reasonable omission and commission rate in building contour grouping. Extensive experiments performed on VHR optical QuickBird imageries justify the effectiveness and robustness of the proposed linear-time procedure with an overall accuracy of 80.9% and completeness of 87.3%. This method does not require user intervention and thereby has the potential to be adopted in online applications and industrial use in the near future.

This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R2of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R2of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., khRMS<; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP.

On the basis of the thermal infrared radiative transfer theory, this paper addressed the retrieval of Land Surface Temperature (LST) from Landsat 8-the latest satellite in the Landsat Data Continuity Mission (LDCM) project in two thermal infrared channels, using the Generalized Split-Window (GSW) algorithm. Meanwhile, a linear bidirectional reflectance distribution function (BRDF) models were used to estimate the emissivity according to different surface classification. A series of ranging of typical surface emissivity and the atmospheric water vapor content (WV) were used into an accurate atmospheric radiative transfer model MODTRAN 4.3 to derive the coefficients in the algorithm. The simulation result showed the LST estimated by the algorithm with the Root Mean Square Error (RMSE) is 1.26K for the all ranges of the atmospheric WV and the results could be better in lower atmospheric WV condition.

In the paper, we developed a novel method of soil moisture estimation in vegetated area base on the simulation result of Cupid model[1], and it was found that LAI and land surface temperture (Ts) appeared in logarithmic relation rather than linear traditionally TVDI (Temperature Vegetation Dryness Index) assumed. Then the soil moisture in vegetated area was calculated through a look-up table. The validation result shown that R2 of the novel method was better than TVDI.

Different ecological and environmental information can be revealed at different scales. This method reveals the temporal and spatial patterns of the ecological capacity of the Li River Basin (LRB) at three different scales including perpixel, county and regional levels over past 20 years. Although the total ecological capacity at regional level doesn't exhibit any sign of ecological deficit, the pixel-based results reveal that the ecological deficit concentrated in part of Guilin County, Xing'an County, Yangshuo County and the surroundings of Li River. The deficit had been expanding from these regions to their surroundings during the period of 1991 through 2013. The results offer detailed and unique information to assist with decisions and strategies associated with further regional development and ecological conservations.

This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45 g/cm2for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm2) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.

Soil salinization is one of the most devastating land degradation process causing agricultural yields reduction. This paper presents a hyperspectral prediction model of soil salinity using partial least squares regression (PLSR) in Tianjin costal area. Soil spectral reflectance of soil samples varying in salinity was measured using an ASD Field Spec spectrometer. The treated continuum-removed (CR) reflectance and first-order derivative reflectance (FDR) were used and compared to explore the more preferable predicting model of soil salinity, which could detect subtle differences in spectral absorption features compared with original reflectance. The results showed that the soil spectra reflectance got distinct absorption feature with peaks centred at 411 nm, 475 nm, 663 nm, 868 nm, 1100 nm ~ 1250 nm, 1400 nm, 690 nm, 1911 nm, 2206 nm and 2338 nm, representing key bands for soil salt content estimation. Through established Partial Least-Square Regression model based on treated soil spectra, the first derived-continuum-removed reflectance was the optimal spectra indexes, prediction accuracy of the optimal PLSR model was 94.4%.

Coal-bed methane (CBM), as an increasingly promising resource for the energy supply, deserves further exploration and accurate reservoir evaluation. It is also required to dynamically monitor the reservoirs (>200 m). Remote sensing methods in regular wavebands may fail in the depth sounding, with only imaging geo-objects shallower than 100 m. In contrast, the Super-Low Frequency (SLF) remote sensing technique has outstanding traits over others, including lower attenuation, all-weather and deeper penetration. In this paper, we have developed a non-imaging remote sensor to acquire electromagnetic signals in the Super-Low Frequency bands (i.e. SLF signals), which also enables us to fast and efficiently pre-process signals in a real-time display. In order to accurately identify producing CBM reservoirs, we mainly extract electromagnetic radiation (EMR) anomalies from processed SLF signals, and then dynamic analysis can be achieved. This technique has been validated by field experiments in Qin shui Basin, China.

This paper presents a novel hierarchical feature representation for geospatial objects detection from optical very high resolution (VHR) satellite imagery. An information extraction approach using multi-scale and hierarchical exploitation for remote sensing image is presented. The key idea is that we adopt a morphological pyramid-based framework for geospatial objects detection in VHR imagery, with a combination of morphological pyramid exploitation and a novel hierarchical feature representation metric, in order to develop an efficient procedure for multi-scale analysis of geospatial object detection to address the issue of information uncertainty in practical applications. We test our method on optical VHR QuickBird satellite imagery and obtain promising experimental results, which confirm the effectiveness and robustness of the proposed procedure.

Road is a kind of very typical artificial object. Road extraction from multi-scale remote sensing images is significant both in military field and in people's daily lives. With the development of remote sensing technology, the scale of remote sensing images that can be obtained becomes various. Therefore, the research of multi-scale remote sensing images is getting more and more attention and it is really a challenging task in the field of image processing. In this paper, a method of road extraction from multi-scale remote sensing images is proposed. Firstly, the textures are extracted and added to the bands of the original image. The filtering, resampling and segmentation operations are then implemented. Next, the spectral characteristics and textures of roads on the remote sensing images are statistically analyzed, and the changes of those on multi-scale remote sensing images are obtained. Then, considering the road characteristics displayed on remote sensing images, some parameters of spectral characteristics and textures are selected to extract roads using the object-oriented method. Finally, the results of road extraction are post-processed based on the opening and closing operation of mathematical morphology. This study has great significance in areas such as features optimization, target recognition, building feature database and improving the utilization of remote sensing data.

Lijiang River Basin (LRB) has witnessed intensive human activities in the past 20 years, due to its unique karst landform, which has attracted a large amount of tourists. During this period, land cover changes were impacted by rapid expansion of road network substantially. This paper analyzes the interactions between road network and land cover change in the LRB by statistical analysis on both random samples and buffer samples. The results demonstrate that road network has a positive correlation with built-up land area and a negative correlation with woodland area. Built-up land is more affected by road network as compared with woodland. Impact strength of surface distance to road on built-up land is greater than that on woodland. Random samples and buffer samples in combination help us uncover the interactions between road networks and land cover change quantitatively and thoroughly.

The paper realizes façade 3D reconstruction using recently promising oblique photogrammetry data. the point-to-point problems in traffic network. We make full use of the multi-level image features to extract interest regions of façade, and then present a backwards coarse-to-fine matching, which makes the auxiliary data unnecessary. The experiment shows the efficiency and robustness of the proposed method, and the vectors describing façade 3D information are also verify the high precision.

The paper presents an approach for building damage detection from high resolution remote sensing image using multi-feature analysis and the fuzzy reasoning procedure. The selected area of our study is in Yushu, which was strongly hit by 7.1-magnitude earthquake. The study area contains 101 buildings, of which 46 are collapsed and 55 are un-collapsed. First, the buildings were selected one-by-one from the GIS data and remote sensing image. Second, three categories of features were analyzed to describe the differences between the collapsed buildings and un-collapsed ones, including spectral feature, texture feature and gradient feature. Last, a final decision was made through considering the variety of feature parameters utilizing fuzzy reasoning. The overall accuracy of building damage detection was 91.09%, of the total 46 collapsed buildings, 42 were detected correctly by the proposed approach, giving 91.30% producer's accuracy.

Based on the theory of coal-bed methane(CBM) geology, the micro-seeps of hydrocarbon cause geochemical alterations in rocks and soil. In this study, the hyperspectral instrument, Hyperion, was used to detect the alterations and hydrocarbons on the land surface of CBM reservoirs. Our study area is in the Qinshui Basin, China. Utilizing Hyperion datasets, the endmember spectra of specific minerals were extracted and the carbonate was mapped by the Spectral Angle Mapper (SAM) algorithm and the hydrocarbons in soil were detected by the Normalized Hydrocarbon Index (NHI). Because the vegetation endmembers in this study can produce similar absorption feature at 1730nm, the distribution of the vegetation was obtained by SAM. The results show that the carbonate and hydrocarbons concentrated in Jincheng Coal Mining Area. This approach, using the hyperspectral datasets, is advantageous for CBM exploration.

Natural source Super Low Frequency electromagnetic prospecting methods have seen an increasingly promising potential in geophysical applications. As the natural source electromagnetic signal is weak, the electromagnetic signal acquisition and processing hardware technology aiming at getting electromagnetic signals with high signal to noise ratio and data inversion accuracy is in great need. In this paper, we designeda signal processing and acquisition hardware system based on ARM platform, i.e., an embedded system, which was specific to the output signal from induction magnetic field. The FFT computation is conducted in our processor to convert time domain in to frequency domain. The relevant information is display on a LCD screen and the acquired data can be stored in storage module for further processing. The software system is based on UCOSII, and FAT32 FS, to improve operability of the system.

Abundant field experiments have showed that the super low frequency (SLF) electromagnetic detector is sensitive to Coalbed Methane(CBM). The signal curves collected by the SLF electromagnetic detector show high amplitude anomalies in the CBM enrichment areas. Based on this finding, we choose the Qinshui basin as study area, and take advantage of the field SLF electromagnetic data to make quantitative prediction of CBM gas content.The results show that the average error between the estimated value and the measured value is 7.56%.

Abundant field experiments have showed that the super low frequency (SLF) electromagnetic detector is sensitive to Coalbed Methane. The signal curves collected by the SLF electromagnetic detector show high amplitude anomalies in the Coalbed Methane enrichment areas. Based on this finding, we choose the Qinshui basin as study area, and take advantage of the field data to seeking the coupleing relationship between the SLF electromagnetic data and Coalbed Methane gas content. The results show that the passive super-low frequency electromagnetic detection technology can effectively monitor the longer time span dynamic of Coalbed Methane gas content.

The buried faults are widespread in the coal-bed, which result in great difficulties in the construction work. In this paper, an integrated method is used in coal-bed in order to detect and analyze the characteristics of buried faults. Firstly, the lineaments are interpreted by visual interpretation from the ETM+ image, and several lineaments enriched areas are picked up. Secondly Super-Low Frequency (SLF) electromagnetic detection is conducted in these areas. Finally, combined with the geology information, lineament distribution and the SLF data, the characteristics of the buried faults are delineated. The results show that the near EW trending normal faults exist widely in the study area by this method, and the depth of the buried faults are presumed in 450-600 m that are coherent with the available drilling data.

Remote sensing monitoring and inversion research of vegetation water content is one of the most important developments of quantitative remote sensing theory and application. The common features of spectral reflectance from vegetation foliage upon leaf dehydration are decreasing water absorption in the near-infrared (NIR) and short-wave-infrared (SWIR). We studied that leaf water indexes in the NIR and SWIR were the most suitable for the assessment of leaf water content and developed a new model TWI (triangle vegetation water index) to estimate both canopy and single leaf water content. From the results of validation perspective, this model has better estimation accuracy at both canopy and single leaf water content comparing to WI, PWI, and NDWI. Validation was established on the basis of PROSPECT+SAIL estimation and ground measured data. So the model had good reliability and great potential to monitor vegetation water content.

Soil moisture not only is an important parameter in precision agriculture, but is the main parameter in crop condition monitoring. We developed the method of soil moisture monitoring and evaluation with the new remote sensing platform, modified the existing indexes of PDI and MPDI, established inversion model and invert the soil moisture.

In this paper, the feasibility of building seismic damage assessment using oblique images is discussed. Firstly, some geometric features of buildings are selected and analyzed according to the universal geometric characteristics of most buildings. Secondly, the parameters of each geometric features are calculated by the method of photogrammetry. Finally, the results of parameters calculation are analyzed and discussed. The results show that more information of building can be obtained from oblique airborne images than from vertical images. Furthermore, these information can reflect the conditions of buildings correctly and effectively in three dimensional (3D) space. The conclusion is drawn that it is feasible and potential to conduct building seismic damage assessment based on oblique airborne images.

The objective of the study is to utilize the local spatial statistics in multi-source remote sensing to analyze and extract surface anomalies in coal mining areas. We illustrated the equations and characteristics of three local spatial statistics, and then calculated the textual bands of them. In contrast with the selected optimal bands, the local spatial analysis improved the classification accuracy from 93% up to 98% based on Supporting Vector Machine (SVM) Classification. In addition, a few Ground Truth Region of Interests (ROIs) were also derived in the multi-spectral image. By means of the hyper-spectral remotely sensed image covering the ROIs, we directly identified six different surface objects or anomalies and inferred that a clustering of minerals and sandy soil with dense vegetation was a developing coalfield, which should be verified in the ground survey.

Soil Salinization is one of the most devastating land degradation in agriculture and environment all around the world. The salinization classification map composed of four stages of salinity can be generated using remote sensing technology. In this paper we propose a semi-supervised procedure for dynamic evolution monitoring of soil salinization in Tianjin costal area based on the use of Landsat data. The trends and extents of soil salinization for a period from 1989 to 2009 are investigated every 10 years. Compared with traditional 2-D spectral space of NIR-R bands, the results reveals that highly saline soils can be easily differentiated in NDVI-Brightness 2-D spectral space, which is a powerful tool for exploring salinization information in Landsat data. Mapping and monitoring soil salinization changes in arid and semi-arid regions by means of remote sensing is effective for producing detailed and accurate land degradation information.

This paper presents an automatic procedure for rapid building extraction from optical very high resolution (VHR) satellite imagery. Classical extraction models are always complex and time-consuming. The optimized process of building extraction consists of three main rapid stages: edge-preserving and smoothing bilateral filter, line segment detection, perceptual grouping polygonal building boundary. Firstly, we use bilateral filter to smooth original image with edge-preserving. Secondly, a state-of-the-art line segment detector (LSD) algorithm gives highly accurate building contour segments. Finally, we apply the perceptual grouping approach based on graph search to organize detected contour line segments of interested buildings. We test our method on optical VHR QuickBird satellite imagery and obtain promising experimental results with overall accuracy of 79.1%, which confirm the effectiveness and robustness of this linear-time procedure.

The goal of this paper is to develop and analyze the natural source Super-Low Frequency (SLF) remote sensing using the BD-6 detector and its data processing and interpretation system to help with Coal-bed Methane (CBM) reservoir information extraction. We delineated the diagram of the SLF remote sensing technique, and especially illustrated the integrated method of the Independent Component Analysis (ICA) and Wavelet-Lifting Wavelet Transform to suppress time-varying 150Hz and 250Hz power frequency electromagnetic interference (EMI). In the application of interpreting enrichment layers of (CBM), we obtained the SLF interpretation signs to identify CBM reservoirs and features. The result demonstrates that the SLF remote sensing provides a prosperous perspective on the detection and demarcation of underground geo-objects.

Highway traffic estimation based on floating vehicle data is one of the major methods in measuring traffic conditions now. Under normal circumstance, average vehicle speed data, which is used to determine traffic conditions, is mainly obtained from taxis and other small vehicles which travel at a speed similar to the road's free traffic flow speed. But for intercity highways and suburban roads that are less traveled by taxis, coach buses and hazmat cargo trucks, which are traveling at a much slower speed compared to the road's free traffic flow speed, are required to compute traffic conditions. This paper introduced a new method in which the traffic condition is estimated using acceleration noise algorithm and a speed classification system, along with the above mentioned low-speed floating vehicle data and verified it by calculating the traffic condition of expressway.

Nowadays, Geographic Information Systems (GIS) has been widely applied in traffic noise prediction to manage the spatial data and to realize the visualization of noise levels. But the common method we use to calculate the noise level of traffic has to sum up the noise levels of all the road segments logarithmically and that is really time-consuming. In order to accelerate the calculation process, a method of filtering road segments using GIS is proposed in this paper. This method focuses on selecting out the road segments which contribute the most to the noise level at the sensitive point. This approach can increase the computational efficiency in a large degree. Finally, this method is validated and proven to be accurate and efficient.

Based on today's most widely used surface scattering model, the advanced integral equation model (AIEM), this study proposes a novel soil moisture inversion model that estimates bare surface soil moisture using double-incidence angle and dual-polarized L-band radar data. Compared with previous studies at L-/C-band, the proposed method provides the estimation of soil moisture without referring to the measured soil roughness and eliminates the requirement of an initial dry season condition. The root-mean-square error (rmse) of volumetric soil moisture varies from 0.8% to 3.2% at different incidence-angle combinations validated by simulated solving and from 4.0% to 7.9% by field measurements when the paired incidence angles are not both large. In case the paired angels are both large, not particularly suitable for soil moisture estimation, the rmse increases to 10.3%. Therefore, this method is applicable to bare surface soil moisture retrieval when at least one of the incidence angles is not large.

Remote sensing technology is considered a fast and effective method to prospect ore. Now, this method is used in Gejiu tin deposit of YunNan in order to extract more accurate mineralization abnormal information. In this study, first through the band math method and principal component analysis method, the mineralization alternation can be extracted in ETM data. Then using ASTER data the limonitization, the chloritization and the dolomitization are extracted by the spectral angle method. At last, the trace elements of the vegetation are statistically analyzed and the vegetation mineralization alteration information is extracted by two different methods in ASTER data. The result shows that the alternation information distributions are consistent in the east-south study area and match with the field exploration. Consequently the extracted results are effective.

The purpose of this paper is to assess the quality of the Global Land Surface Satellite (GLASS) albedo products, which are being generated in China under the 863 key project entitled “Generation and Applications of Global Products of Essential Land Variables”. Ground measurements at twelve sites over three land cover types were collected from AmeriFlux and used to calibrate and validate the albedo products derived from Landsat TM images, and then the validated TM albedo products were aggregated to 1 km resolution to evaluate the accuracy of GLASS albedo products. The validation results demonstrated that GLASS albedo products have a high accuracy, with an overall root mean square error (RMSE) of 0.026 and bias of -0.009. In order to better understand the accuracy of GLASS albedo products, further validations need to be performed over more land cover types and at more ground sites.

This study summarizes 12 spectral indices for chlorophyll inversion, including traditional multi-spectral indices and newly published indices constructed based on “Red Edge” (680-750nm) and “Double-Peak” in red edge region. Among them, some are good candidates to resist Leaf Area Index (LAI) variations, but some are not. By using forward model simulations and in-situ measurements data, we systematically tested these indices and picked out the best one which (1) has the highest sensitivity to chlorophyll; (2) has the best resistance to LAI variation. Then predictive equations were developed from simulated data and validated using winter wheat ground measurement data collected in 2011, Yucheng Station at Chinese Academy of Sciences.

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.

This work focuses on using remote sensing and geographic information systems (GIS) to identify promising geothermal areas in southwest Tengchong, China. Thematic information, including surface temperature, urban area, and surface slope are derived from Enhanced Thematic Mapper Plus (ETM+) data and digital elevation models (DEM). GIS is applied as a decision-support tool to integrate the thematic information for suitability analysis. The results indicate that combining remote sensing with GIS is an overall effective and accurate method for geothermal exploration. Three developed geothermal fields are successfully extracted in Tengchong, and promising areas are found to the north of study area and warrant further exploration.

Real-time traffic information is important in terms of easing highway congestion, while map matching is the basis for calculating real-time traffic information. This paper starts with the three factors of currently used algorithms in floating car map matching, namely distance, speed-direction, and connectivity. From there, the paper gives analysis on the characteristics of highway network and the efficiency problems of currently used algorithms, and proposes a new map matching model based on the gradual-removal of candidate roads. Based on this model, the paper gives the corresponding algorithm process and testing results of processing actual GPS data. It has been validated that this algorithm is of fine accuracy and is capable of meeting the efficiency requirements of map matching in large-scale floating car data on highway, therefore providing a feasible approach for highway map matching at present.

Soil spectral reflectance is affected by soil physicochemical characteristics and the physical basis of the soil remote sensing. Generally, the impact factors of the soil spectral features include water content, organic matter content, iron oxides content, physical composition and the parent material. In this study, a portable ASD FieldSpec Pro FR was used to collect the spectra of soil samples. The sensitive bands were selected by analyzing the relationship between nutrients and soil spectral features. Then the inversion models of soil nitrogen and organic were established by linear regression separately. The result showed that the content of soil nitrogen and soil organic can be well retrieved from remote sensing.

In this paper, an analysis of the spatial and temporal landscape evolution of northwest Sichuan Province, China, is carried out based on the landscape theory, RS and GIS. Fucheng District in Mianyang City, Sichuan Province, is chosen as our study area, and the driving forces of its landscape evolution are discussed based on the land use maps, the Landsat TM/ETM+ and CBERS 2B remote sensing images and other related geographic data. The result indicates that: (1) The area of cultivated land in northwest Sichuan declined sharply during the past 13 years with a much higher dynamicity than the average landcover change rate in study area. (2) The stability of the spatial pattern of cultivated land gradually decreased, while the annual terrain evolution presented a significant temporal and spatial variation. (3) The spatio-temporal evolution of cultivated land is the joint result of the negative driving forces including land slope, elevation, increased construction sites, population density, urbanization and GDP, and the positive driving forces, such as irrigation conditions, highways grades and the intensity of land consolidation.

There are two buffer representation methods at present: vector buffer and raster buffer. When dealing with a large geographic dataset, both of them are unsatisfactory either in time efficiency or in space efficiency. This paper presents a new buffer representation method-equational buffer, which is not represented by physical entities like vector buffer and raster buffer but by a mathematical equation. Such a representation method frees equational buffer from complicated geometric calculations and makes it suitable for dealing with massive geographic data and dynamic geographic data. We also discuss the characteristics and potential application of equational buffer.

Drought index plays an important role in the assessment of drought severity as one sensitive indicator of land drought status. A simple and accurate method of expressing ground drought from remote sensing data is in urgent. Firstly, a brand-new frame has been developed by coordinate system transformation, with (x', y') expressing (soil water content, vegetation coverage), which made the meaning more clearly. Secondly, a new drought index - the modified Shortwave Infrared Perpendicular Water Stress Index (MSPSI) has been proposed, with additive of vegetation fraction f, (expressed as a distance from LAI contour to soil baseline) as a vegetation coverage adjusting factor, which makes ground drought status with vegetation coverage variance be considered as belonging to the new coordinate system. To evaluate the new index, the correlation analysis between the in-situ observation data from the meteorological sites and simulated values from MOD09A1 has been performed on a regional scale. We found them to be in good agreement. To further confirm that the drought index is feasible with higher precision than original drought indices (DIs), comparison experiment has been done.

Aimed to solve the efficiency and timeliness problems in the traditional road updating method, the authors propose a new solution based on floating car data. By the procedures of map matching, point density analysis and automatic vectorization, the authors successfully extract plenty of new roads. The validation result shows that the method proposed here is a more efficient and timely way to update the road network. With its advantages, the long updating cycle, large manpower and material resources cost of the traditional method will be overcome. Also, the path planning and the traffic analysis applications will benefit greatly from the timely and reliable new method.

This paper focuses on assessing the effectiveness of applying orientation angle calibration to polarimetric synthetic aperture radar (PolSAR) data for soil moisture estimation. We employ Cloude-decomposition-based method to estimate the orientation angle because it can relate a scatter-distributed pixel to its major component of an equivalent "pure target," use the Jet Propulsion Laboratory/Airborne Synthetic Aperture Radar L-band fully polarimetric data to validate the proposed method, and observe results in good agreement after orientation angle compensation is employed. Specifically, root mean square errors of measured radar backscattering coefficients σhh0and σvv0and copolarization ratio versus advanced integral equation model predictions are reduced significantly from 1.95, 1.33, and 2.03 dB to 1.30, 1.15, and 1.43 dB, respectively. The compensated copolarized backscattering coefficients are also used as inputs to a novel inversion model to estimate the dielectric factor Rhhand volumetric soil moisture mv. The results show that the estimation errors are reduced significantly from 0.075 to 0.054 and 0.056 to 0.041 for Rhhand mv, respectively. This paper demonstrates the advantage of orientation angle calibration as a preprocessing for estimating bare soil moisture, particularly in agricultural areas, and the preponderance of fully PolSAR data on soil moisture estimation over dual and single polarizations.

Leaf Area Index (LAI) is a very important parameter in the area of vegetation quantitative remote sensing. Large range of LAI can reflect the change of eco-system. This article has discussed whether the crop type is a factor to impact the leaf area index retrieval. We choose four types of crops in our research and Hyperspectral Data and leaf area index of these crops were measured. Then the LAI retrieval models were established, which demonstrate the relationships between SVI and LAI. Finally the conclusion can be made that the type of crop is a factor impacting the LAI retrieval. For different crops, the best models are not the same. But the little difference of R2can be omitted. The SR is the best spectral vegetation index for LAI retrieval.

On September 6, 2008, the satellite HJ-1B was launched into a sun-synchronous, near-polar orbit. In order to determine temporal changes of the absolute radiometric calibration of the HJ-1B satellite in flight, a program was carried out at DunHuang calibration field, Gansu province of China, from August 19 to 30, 2009. In this work, reflectance -based calibration method was employed to simulate the absolute calibration coefficients of the HJ-1B CCD sensors. Then the cotton field, cement court, water pool and test site was selected to validate the calibration coefficient. The validation results indicated that there had a good agreement between the imaged-based reflectance and the ground measurement of the type of cement court and test site. On the other hand, there had a disagreement of the type of cotton field and water because of the effect of mixed pixel.

After the occurrence of natural disasters, objective and accurate assessment of road damage is the key to emergency rescue. However, in the traditional road damage assessment, the assessment factor and the form of assessment result are single. It can only give an overview of damaged roads, but can't further provide their traffic capacities, which are essential for the disaster relief department. A new approach of road damage assessment is presented in this paper. The damage levels are graded based on the magnitude of actual traffic capacities of post-disaster roads, and the detailed damage information is output at three levels - damage block level, single-road level and region level. The proposed method is applied in the damage assessment of roads in Wenchuan County, China, which was hit by an 8.0-magnitude earthquake. The experimental result shows the validity of this method.

Drought indices based on spectral space have a great potential in remote sensing drought monitoring. With the discussion of two-dimensional spectral space, we introduce a model named Distance Drought Index (DDI) as an improvement of the well recognized method Perpendicular Drought Index (PDI) for drought monitoring. By constructing the NIR-Red spectral space, we found that the scatter diagram characterized with a triangle shape in the two-dimensional space. The point distribution in the scatter diagram varies with its moisture content and vegetation conditions. The higher moisture the target possesses, the nearer it is located to the coordinate origin. Thereby, the distance between the random point and the origin point can be used as a drought monitoring indicator, where DDI is developed. Larger DDI value indicates a more severe drought condition. To validate the drought indices proposed in this paper, MODIS images are used to calculate the DDI and PDI over ground measuring points in Ningxia Hui Autonomous Region of China in March, April, June and August, 2008. Finally, we come to the conclusion that DDI is an effective drought monitoring index and achieves better result than PDI.

In the field of drought monitoring using remote sensing, the software has been lagging behind the applications. For the operational needs of monitoring and evaluating drought, a professional remote sensing application system (named RSDMS) is designed and developed with the concept of component-based technology. Starting from the architecture design, this article details the component-based design patterns of this system, and then discusses some key techniques during the development process. After achieving the general-purpose functionalities, this system also integrates a variety of drought monitoring models in the form of component, showing great flexibility. Meanwhile, the results of preliminary application in Ningxia region of northwestern China are presented to indicate that the system has a good practice effect.

This study wants to demonstrate that two different polarimetric target decomposition methods can improve SAR data accuracy for estimating the parameters of bare soil surface. To achieve this goal, two experiments are conducted: (1) both Freeman and Cloude decomposition methods are performed on JPL/AIRSAR L-band fully polarimetric data; and (2) Advanced Integral Equation Model (AIEM) is used to simulate backscatting coefficients. The root mean square errors (RMSEs) of σ0hh, σ0vvbetween original data and AIEM simulated data are 1.96 and 1.25 dB. However, if Cloude method is used to decompose original data, the RMSEs will be reduced to 1.45 and 1.14dB, respectively; for Freeman method, the RMSEs are 1.64 and 1.35 dB. Therefore, polarimetric target decomposition compensation, especially Cloude method, can help to improve the accuracy of SAR data for estimating the parameters of bare soil surface.

The passive Super Low Frequency (SLF) detection is a new technology with wide application prospect, but faces some problems. The paper focuses on the problems of passive SLF detection data processing, and then introduces current data-processing method in detail. Based on above problems, the specific requirement analysis is made, and a set of passive SLF electromagnetic data post-processing software is designed. The paper displays the system framework and the function modules of software. The system, mixed programmed with the VC++ and OpenGL, contains the data-processing, data-display and data-transform modules. The key technologies and algorithms such as de-noising method, envelope filtering and OpenGL are introduced.

The paper presents a new modification method of chlorophyll content index based on a new LAI index-sLAIDI to solve the problem of chlorophyll content index influenced by LAI. Based on the data stimulated by PROSPECT-SAIL modal and measured data, seven chlorophyll content indices are chosen and divided by sLAIDIkto reduce the influence of LAI. Based on the stimulated dataset, the normalized standard deviation of chlorophyll content indices are used to evaluate effect of modification to determine the parameter k. Measured dataset are used and to verify result of modification. The result shows that chlorophyll content index divided by sLAIDIkcan improve accuracy of estimation, and parameter k is different for different chlorophyll content indices.

A comparative research of snow cover is conducted, which is aimed to investigate the effect of spatial scale variation and the differences of different spatial resolution remote sensing images, by using moderate-resolution imaging spectroradiometer (MODIS) data and the satellite-B (HJ-1B) data of the small satellite constellation for environment and disaster monitoring and forecasting of China. Results show that: (1) the scaling-change within certain limits has little impact on snow cover mapping; (2) high spatial resolution image gives a better description of the detailed information of snow cover area; (3) the differences of snow cover between low spatial resolution and high resolution images are due to the different snow cover type.

It is found that the abandoned mine goaves, because of the different fillers, exist in below types: goaves filled with air, filled with water, and congested by falling coal bed roof after investigation. According to the goaf types and characteristics of different fillings in goaves by super low frequency (SLF) electromagnetic exploring, the SLF electromagnetic exploring curves may be classified into three major categories: the characteristic curve of air filled goaf, the characteristic curve of water filled goaf, and the characteristic curve of coal bed roof backfilled goaf. Based on the analysis above, we established the recognition characterization of passive super low frequency electromagnetic exploring spectrum curves with respect to goaves, and accordingly obtained the information of goaves.

Drought is a harmful and headachy natural disaster in the world, which has caused considerable loss to agricultural production and economy per year with high frequency. It is therefore very important and necessary for drought monitoring over large scales by remotely sensed techniques. In order to establish physically meaningful water stress index, the SWIR bands with strong water absorption features are used in this study, then the shortwave infrared water stress indices (SIWSI) are constructed using NIR and SWIR bands. A representative arid and semi-arid region over northwestern China, Ningxia region, where droughts occur frequently, is selected to assess the drought status using two indices of SIWSI6, 2and SIWSI7, 2derived from NIR channel 2 and SWIR channel 6 or 7 of Moderateresolution Imaging Spectroradiometer (MODIS) sensor, combining with ground truth data of soil moisture observed by the meteorological stations across the whole study area. The fitted regressions of negative exponential curves indicate that both indices have significantly correlated with the in-situ measurements (P6, 2performs slightly better than the SIWSI7, 2(r2=3D0.56 and 0.48).

In virtue of the property of light, portable, and less affected by complex terrain, the natural source magnetotelluric method and explorer is superior in complicate terrain exploration in the field. Goaf, the waste part after mining, is a ruinous geologic hidden danger. Due to the act of gravity, the wall rocks of goaves probably sink and caving to cause ground falls and other geologic disasters. This paper introduces the theory and exploring method of using natural source super low frequency (SLF) electromagnetic wave to detect goaves. The effect of exploration was proved by comparing experiment results with well logging.

Atmospheric corrected Landsat Enhanced Thematic Mapper Plus (ETM+) near-infrared (NIR) and shortwave infrared (SWIR) band reflectances are used to develop a new index to monitor vegetation water content (VWC) in terms of equivalent water thickness (EWT, cm). This paper outlines the first part of a research program to investigate the potential and physical basis of wavelengths in the optical domain to assess the VWC. Then, a method called vegetation water content index (VWCI) were developed using SWIR, and NIR wavelengths of ETM+ data. The relationship between the EWT at canopy level is explored through linking leaf reflectance data obtained from PROSPECT with canopy reflectance from SailH and in-situ measurements. Significant correlations are found between canopy EWT and the developed index for both modeled and ground measured data.

In high spatial resolution satellite images, shadows are usually cast by elevated objects such as buildings, bridges, and towers, especially in urban region. Shadows may cause loss of feature information, false color tone and shape distortion of objects, which seriously affect the quality of images. Hence, it is important to segment shadow regions and restore their information for image interpretation. This paper presents an effective and robust approach for shadow segmentation and compensation in color satellite images with high spatial resolution. The approach uses normalized saturation-value difference index (NSVDI) in hue-saturation-value (HSV) color space to detect shadows and exploits histogram matching to recover the information under shadows. Experimental results by applying the proposed approach in the IKONOS color images of urban area demonstrate the effectiveness and feasibility of the proposed approach.

Common methods for DEM generation are based on collinearity equation, and rational function. Either of them require registered stereo pair as input. In order to register each point precisely, this paper proposes a corresponding image object constrained registration method based on object oriented image segmentation. Compared with current methods, the proposed method provides one geometric image transform (GIT) function for each pair of building roof because one GIT could fit well for just one roof. Object oriented segmentation not only helps us set the effective area for each GIT, but also provide us enough feature points from the boundary of a segmented building points to fit the coefficients of its GIT. The result by applying the proposed method on IKONOS stereo pair demonstrates the feasibility of generating building DSM with acceptable accuracy.

In this paper, we develop a new method called distance drought index (DDI) to monitor land surface dryness condition, which is based on an extensive analysis of spatial distribution features of soil moisture in NIR-Red spectral space. The DDI can be considered as an improvement of the model perpendicular drought index (PDI). To validate the drought indices proposed in this paper, MODIS images are used to calculate the DDI and PDI over ground measuring points in NingXia of China. The DDI and PDI are then compared to an in-situ drought index obtained from field measurements made synchronously with the satellite overpass. Results show that DDI and PDI have significant correlation with 0-20 cm averaged soil moisture. The highest correlation of R2=0.59 for the DDI and R2=0.57 for the PDI is obtained when compared with average soil moisture from 0 to 20 cm soil depth. It is evident from the results showing that the DDI has better performance than the PDI and the potential to provide a simple and real-time drought monitoring method in the remote estimation of drought phenomena.

The objective of this paper is to analyze the albedo-NDVI spectral feature space to develope drought estimation methods using Landsat ETM+ imagery. A simple ratio and differences of surface albedo and normalized difference vegetation index (NDVI) are examined. The methods are compared against field measured soil moisture at Shunyi test site in Beijing, China. Results indicate that there are close correlations between soil moisture and both albedo-NDVI difference index (AVDI) and albedo-vegetation ratio index (VARI). The coefficient of determination (R2) between AVDI and 5 cm soil moisture is 0.45. The strongest correlation between the VARI versus soil moisture R2= 0.4103.

In the paper, The DT classifier adopted was CART(Classification and Regression Trees) to obtain a habitat of interest to Paeonia sinjiangensis, existing as fragments on the southern coteau of the Altay mountain located Xinjiang autonomous region of China. In the paper, decision tree classifier allows for the integration of remotely sensed data with other sources of georeferenced information such as land use data, spatial texture, and digital elevation models (DEMs) to obtain greater classification accuracy. TM reflectance data acquired in 2001 were required to completely cover Altay mountain area. Several ancillary datasets were used as inputs into the decision tree classification. These datasets include land use, city boundaries, vegetation types and digital elevation models. Logical decision rules, discovered from samples through CART integrating spectral textural and the spatial distribution character, then are used with the above various datasets to assign class values to each pixel. Finally, In contrast with CART, a standard maximum likelihood decision rule implemented by a discriminant analysis. Results on study of Paeonia sinjiangensis ground extraction from ETM imagery show that the classification results of CART was significantly better than that of Common DT classification. And this organizational methodology for classification is feasible and reliable if taking advantage of ancillary data and image analyst for classification.

Process-based terrestrial ecosystem models inevitably need model initialization and parameters specification. In this study, remotely sensed surface soil moisture derived from near infrared and shortwave infrared bands was assimilated in BEPS (Boreal Ecosystem Production Simulator) to initialize soil moisture in BEPS and fine-tune BEPS key parameters which are closely related to soil moisture estimation including maximum stomotal conductance, leaf area index (LAI) and root density. An Ensemble Kalman Filter is used to perform data assimilation and parameter adjustment. The result shows that using the optimized parameters, the performance of model predictions of 0-10 cm soil moisture was greatly improved compared with the surface soil moisture fields derived from remote sensing data. It is demonstrated that the method of assimilating remotely sensed soil moisture in the BEPS model can help improve the soil moisture results of the BEPS model in the arid and semiarid area and provide a feasible way to monitor drought and to assess its influence on agriculture.

In the agriculture research field, the main object of drought monitoring is to gain the soil moisture, furthermore to confirm the loss of drought. Soil moisture is one of the most important factors affecting the crop especially for the drought area. Besides, the terrain of farmland also affects the soil moisture and the variety of crop. This paper will indicate the effects of the two factors above and the relationship between them. Agriculture drought research group of PKU has used TVDI (Temperature Vegetation Dryness Index) to evaluate the soil moisture in order to establish a model to predict crop yield, which has been used in the real task of meteorology and agriculture department. Using a special software system established by the research group, the TVDI could be conveniently calculated and the result accords with the real situation very well when we use it to compare with the local observation data.

Research on road extraction from digital imagery is motivated by the need for data acquisition and update for geographic information systems (GIS). Roads usually have parallelism of road sides, and on the images, especially the edge map, there are dual edges for each road. In this paper, we propose an approach for automatically extracting road from ETM panchromatic image with a resolution of 15 meters based on Dual-Edge Following. Our approach uses the edge detector with embedded confidence (EDEC, Peter Meer, 2001) to detect road edge, then traces road to generate road candidates by Dual-Edge Following, next exploits the perceptual organization based on probability to link the road segments. Dual-Ddge Following use edge information of both road sides to search for road segments which can improve the precision of road segments. The experiment with ETM panchromatic image in Xinjiang, China shows the validity of the approach.

Coal-bed methane (CBM) is composed primarily of gas and it is a clean and efficient energy of high value. Based on the theory of gas geology, usually there will be an anomaly of land surface temperature at the area where the density of CBM under the coalmine is high. Land surface temperature at coal mine district is retrieved by using ASTER band 13 (10.7¼m) and band 14 (11.3µm) combined with split-window algorithm. The atmosphere transmittance is simulated through MODTRAN 4 and land surface emissivity is calculated from NDVI. The temperature anomaly areas in the result image are suspect high CBM density areas and the causes of the temperature anomaly are discussed at the end.

Super low frequency remote sensing technique introduced here is, with the use of natural electromagnetic waves, used to explore the apparent resistivity of strata on the surface of the Earth, and then to reduce the degree of enrichment of gas underground and indicate the depth of the gas-bearing stratum. The simple theory, exploration method and device are presented in the text. Contrasts are made between the results obtained from experiments and those extracted by drilling at the same location for validating this technique.

Aerial Remote Sensing (Aerial RS) image compression & transmission on-board system, not only is the core of Aerial RS supervising, but also the key technology about the security of data obtainment. The requirement of Aerial RS is stricter on the data quality and security. If we transmit the images through some special channels while the images are obtained during the task of Aerial RS, the control center on the ground could acquire the status of the whole Aerial RS system. Besides, it could also backup the images immediately. Image compression & transmission system is the important bridge between the Aerial RS system on-board and the control center. The research group of Aerial RS data processing in Peking University, integrates all the necessary technologies including compression algorithm & hardware, integration of function modules, protocol of data packing & unpacking. The research group has developed an efficient compression method that has been optimized both in software structures and hardware architecture. Considering with the narrow space on the airplane, the research group has designed the compression & transmission function module which is installed on the motherboard of the Aerial RS control system. The compression program is compiled in the Code Composer Studio (CCS) development environment, and then the result file compiled is burned into the DSP chip on the module. This paper also introduces an efficient protocol designed by the research group, which can ensure the accuracy of the transmission. It ensures the control center can rebuild & unpack the packages correctly, and avoid fatal errors caused by some false frames and packages during the transmission. This data compression & transmission system, which has been tested in certain different places in China, has achieved the purpose expected.

The economic value of natural resources and natural environment can be described by natural capital. Based on the example of the change of water and green space within the fourth ring road of Beijing from 1983 to 2002, this paper presents the quantitative research on the change of natural capital by the means of remote sensing. And this paper explores the dynamic change of water and green space as natural capital, and assesses the economic loss from the viewpoint of natural capital. Then this paper analyzes by each (City proper, Haidian, Fengtai and Chaoyang). Finally, conclusions are drawn that there is a decrease of 18.72% of water and a decrease of 23.33% of green space. The largest change happens in Haidian. Also the distribution, types and functions of natural capital have changed at different levels. Finally the factors which cause the change are presented. Also some solutions are put forward.

Normalized difference vegetation index (NDVI) plays an important role in the detection of drought and desertification. However, being calculated by direction limited spectral reflectance, it is almost ignorant to anisotropy of target reflection. In this paper, on the basis of atmospheric correction of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data using 6S (Second Simulation of Satellite Signal in the Solar Spectrum) code, bidirectional reflectance distribution function (BRDF) of surface coverage is obtained to retrieve spectral albedos of certain wavelengths. Narrowband spectral albedo to broadband albedo conversion is accomplished via measured band data and simulation of unknown spectral wavelengths which are not available by Landsat-7 ETM+ imagery. Albedo based ecological safety monitoring index (ESMI) is developed and sensitivity analysis of ESMI in surface application is conducted. Application results show that ESMI has potential use in quantitative monitoring of eco-environmental problems

The founding of building pattern database is the key technique of building recognition of high resolution remote sensing imagery. This paper founded a simple pattern database by extracting the characteristics of building in remote sensing imagery and summing up several building patterns. Next, on the basis of patterns from the pattern database, this paper used the wavelet descriptor to describe the characteristics of buildings and utilized its affine invariant to recognize them. Then an image of Peking University was taken as an example to do the experiment. The result proved the method of founding pattern database was feasible.

In this paper, we present a novel semi-fragile watermarking scheme based on wavelet packet. The method in the paper includes four parts: first, to produce watermark; second, to scramble watermarking image; third, to embed watermark; last, to inspect and locate tampered marked image. To inspect whether including watermark in an image with the key attained from process of embedding watermarking. If it is a marked image, then extracting watermarking. At last to validate the degree of robustness by compression and noise, to locate tamper by cutting and altering.

This paper discusses some key techniques in designing and implementing such software. The paper starts with the design of the main functions of the system and discusses the system's data structure and data flow. On the basis of this, the authors present the detailed project implementation strategy. And the advanced component-based development techniques is introduced and used. Then the authors expound some key technologies such as resolution unification, projection unification and mass data fast visualization to solve major problems during the development process. Via these means, the prototype of oceanic remote sensing multidimensional dynamic visualization system is developed successfully

In the paper, the status of flood disaster in China and the progress of disaster prevention and reduction in the field of property insurance were analyzed. The characteristics and application fields of 3S (GIS, RS and GPS) were also introduced. According to the current need and future development of property insurance company, which were based on the investigation to the work of disaster prevention and reduction in property insurance and casualty company (abbreviated as PICC) China, the authors decided to apply 3S technologies to the field of property insurance and used the successful methods in foreign property insurance companies for reference to develop a decision support system of flood disaster for property insurance. The system linked well with the operational system of insurance company and realized seamless integration between different data sources. The work of disaster prevention and reduction in property insurance company was better organized both in theory ways and in key technologies. As a result, the economic benefit of property insurance company was really improved. The system was applied in Shenzhen, China and the result was satisfying

]Studies on character and variation of surface moisture become more and more important in land surface process and hydrological modeling. Several methods have been proposed to estimate dry/wet conditions of land surface. However, there are some limitations in these methods. We developed an empirical method of detecting surface moisture condition using surface temperature and vegetation index derived from NOAA/AVHRR data. The results show that vegetation index and surface temperature are important parameters to describe characters of dry/wet condition of land surface. It is obvious that the use of remotely sensed data is potentially of great interest in such context. Therefore, to monitor surface moisture is an important subject of remote sensing. Using this method the surface moisture is detected in the Northwestern Loess plateau of China. The distribution characters of the surface moisture is analyzed as well

Sustainable development of Northwestern Loess Plateau of China (102deg-108degE, 34deg-38degN) will largely depend on the availability of water resources. Evapotranspiration (ET) is one of important items in water resources assessment. Current remote sensing can not directly measure ET which need to be estimate using the land surface physical parameters that can be derived from remote sensing data (NOAA/AVHRR) in combination with meteorological information. But various land surface parameters, such as albedo, emissivity, land surface temperature (LST), and NDVI, are directly or indirectly derived from satellite remote sensing data and therefore associated with uncertainty. The effects of the land surface parameters on the daily ET obtained by applying SEBS (Zhan, 2003) are analysed in this paper. In the sensitivity analysis of the daily ET to surface parameters that are different with seasonal change, it was appointed that the sensitivity will be different not only in different area, but also in different season

NDVI is a land surface parameter which plays an important role in ecology. It is also sensitive in the circulation process between land surface and atmosphere. The earlier research indicated that NDVI has closely relationship with some main biophysics parameters such as photosynthetically active radiation, leaf area index, biomass of vegetation, and so on. So NDVI is widely used in the study of global vegetation. In this article, it was presented that ecological indexes have been derived from NDVI using NOAA/AVHRR data by the method of principle component analysis (PCA). After the PCA of NDVI which calculated form the NOAA/AVHRR data, the relationship analysis aimed at the four ecological parameters which derived from NDVI. Then the results show that the PCA can compress the key information into four foremost principal components which would be called the ecological indices (EI). And the first principal component reflects the basic situation of vegetation overlay (EI_all), the second (EI_ss), the third (EI_ws) and the fourth (EI_aw) principal components indicated the vegetation change in seasons respectively. According to the direction curves of seasonal vegetation change, the four principal components seem to be their biological significance

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.

We study in this paper the relationship among seismic fault, strong earthquake movement, satellite orbits, etc., and propose the formula for range being separated into horizontal and vertical components under different circumstances.

This paper discusses several processing algorithms of D-InSAR based on experience accumulated by our predecessors, and puts forward a new method, "new" 4 pass D-InSAR, with its formula.