On the basis of a detailed discussion on the principle of GB InSAR, the main data processing and analysis stages for estimating deformations starting with the GB InSAR observations are described. This paper gives a review of the main types and development trend of ground-based radar system, the main application domain and some existent problems of GB InSAR, and then summarizes the pros and cons of ground-based and space-borne InSAR for deformation monitoring.
Relative radiometric normalization of multi temporal remote sensing images is the prerequisite of dynamic monitoring of remote sensing. The effect of pseudo-invariant feature(PIF) relative radiation correction is largely determined by the selection of PIF points. The method of ordinary artificial point selection has a lot of shortcomings, such as strong subjectivity and the fact that the points' quality and quantity cannot be controlled. The following operations proposed in this paper will solve these problems. First, the accurate geometric correction and registration of the two TM images were carried out. Then the PIF was refined in stepwise way by combining the band ratio operation and quality control to improve the quality of PIF and reduce the subjectivity of its selection. The quality of selected points was evaluated by using the correlation coefficient of corresponding sample points of each band. The method of least square regression analysis was used to get the gain and offset based on the qualified PIF points selected. Then the relative radiometric correction of the two TM images was carried out. The spectrum curves of typical features, root mean square error, NDVI statistics between corrected image and original image were analyzed. The results show that the correction effect is satisfactory.
In order to improve the accuracy of hyperspectral pixel un-mixing,the authors proposed a kernel based pixel un-mixing method in this paper. By adopting orthogonal subspace projection(OSP) operator, least squares OSP(LSOSP) operator, nonnegative constrained least squares(NCLS) operator and fully constrained least squares(FCLS) operator respectively, the authors established kernel OSP(KOSP),kernel LSOSP(KLSOSP),kernel NCLS(KNCLS) and kernel FCLS(KFCLS) for hyperspectral imagery pixel un-mixing. The comparative experiments of abundance inversion by applying KLSOSP, KNCLS, KFCLS and LSOSP, NCLS, FCLS to CUPRITE AVIRIS data were carried out,and the results show that, for heavily mixed hyperspectral images, the pixel un-mixing accuracy of kernels based KLSOSP,KNCLS and KFCLS is higher than that of LSOSP, NCLS and FCLS. Meanwhile,the constraint conditions can improve the accuracy of abundance estimates.
Due to the highly detailed information and noise in the high resolution panchromatic images, the results of traditional residential area extraction algorithms based on texture features are not satisfactory. To tackle this problem, the authors propose a method based on wavelet texture and primitive merging in this paper. For obtaining the initial primitives, the image was firstly segmented by fractal net evolution approach modified by the wavelet transform, and then the multi-scale wavelet texture features extraction method was directly applied to the irregular image primitives. Based on the artificially provided seed primitives, the algorithm merged the primitives with similar texture features and then applied morphological methods to the result of primitive merging. In the experiment, Mapping Satellite-1(TH-1) panchromatic images were used to validate the proposed method. The comparative analysis with other texture features-based methods shows that the proposed method could extract the street-block residential area from high resolution panchromatic images with a higher extraction accuracy and computational efficiency.
High-precision information extraction of mountainous rivers is a key technology for development and utilization of water resources in arid areas of China. Nevertheless, the utilization of remote sensing images cannot distinguish water form mountain shadows. In this paper, the authors used GF-1 satellite images with resolution of 2 m and 8 m as the data source, selected Baka Luck reservoirs as the study area, and put forward an improved method(modified shadow water index, MSWI) for water information extraction. At the same time, the authors used the single-band threshold method, the NDWI method, the single band method combined with the SWI decision tree classification(SWI) and the single band method combined with the MSWI decision tree classification (MSWI) respectively to extract water information in the study area. The results show that, compared with the SWI and the MSWI method, the first two methods have relatively poor performance. The SWI and MSWI classification effect is good and the total classification accuracy of MSWI is increased by 1.22% relative to the SWI method. It can provide technical support for the domestic high series satellite image information extraction in water resources in arid regions.
In order to overcome the shortcomings of the traditional image classification based on spectral and texture, the authors propose an image classification method considering temporal features in this paper. Land use vector map in historical period was used as auxiliary data. The objects were extracted by image segmentation under the constraint of land use vector map. The land cover transition probability which represents temporal feature was calculated by iterative statistic method. The joint probability of object based on temporal feature was built after integrating the land cover transition probability into the traditional maximum posteriori probability. The image classification map was obtained by the maximum posteriori probability theory. The experimental results based on the QuickBird image show that the proposed method can improve the accuracy of the image classification result. Compared with things of the traditional classifier using spectral and texture features, the overall classification accuracy and kappa coefficient of the proposed method are increased by 9.8% and 17.9% respectively.
The purpose of this study is to explore a new effective method to conduct quantified calculation of the lower threshold. Based on ASTER image, the authors used the fractal model and the change-point analysis model in study areas named Xinjinchang and Laojinchang. The experimental results show that the model could quickly calculate the lower threshold for the alteration anomaly with fractal characteristics, and the model verification results also show that the threshold values are accurate and effective. In addition, field geological survey also indicates that the alteration anomalies delineated by the authors are well in accord with the known orebodies and the spectra of the alteration geological bodies. The authors thus hold that the nonlinear analysis method is a reliable means for extracting alternation anomalies and is also useful for mineral exploitation.
In consideration of the fact that multi-source remote sensing image fusion is restricted by the existing resolution, the authors propose a super-resolution remote sensing image fusion method based on dictionary learning with sparse representation theory in this paper. The spatial resolution of multispectral images can be promoted to 1 or 2 times higher than the spatial resolution of panchromatic image. Under the framework of the method in remote sensing image fusion, a learning dictionary was established, the redundant dictionary on image sparse representation was used to conduct super-resolution reconstruction implementation. Then the Gram-Schmidt(GS) spectrum sharpening method was used as a fusion rule to obtain super resolution multispectral image fusion. Three experiments were carried out using QuickBird data. The results show that the proposed method is suitable for remote sensing image super-resolution fusion with some advantages in comparison with traditional fusion method, traditional super-resolution method and the other dictionary learning strategy. This paper provides a feasible solution for multi-source remote sensing image fusion, and has referential significance for other fusion methods.
In order to obtain a better understanding of the spatial distribution of permafrost in Zhada area of the Tibetan Plateau, the authors used different remote sensing models to delineate the depth of permafrost and employed elevation model and temperature model to compare the results. According to the result, the study area covers 17 148.93 km2. The authors summarized the interpretation signs for the depth of permafrost in this area. A comparative study of the 3 models can improve the mapping accuracy for the large scale permafrost, greatly reduce related project preceding work and improve efficiency. It is concluded that the application value of the remote sensing technology delineation method is high. Therefore, the remote sensing technology delineation method is very useful in such fields as regional hydrogeology, engineering geology and climate change in the Tibetan Plateau.
To tackle the problem of the large quantities and serious deformation of L0 level airborne three-line-array images, this paper proposes a tie point matching method based on SIFT algorithm and correlation coefficient. Firstly, pyramid image is generated and SIFT algorithm is used to match initial corresponding points on the top-level of pyramid image. Then tie points are propagated through pyramid images via correlation coefficient matching method. Finally, possible match error is removed based on geometric constraint of POS data, and distribution of tie points are optimized. Three stripes of ADS40 images were used for experiments. Compared with the conventional image tie point transfer method, the proposed method can improve the ratio of correct matches by more than 6% and the tie points are well-distributed.
In this paper, the authors used GF-2 remote sensing data to extract Nitaria tangutorum dune in Minqin County. The authors made full use of the minimum/maximum difference texture information and length-width ratio as well as area of geometric features of Nitaria tangutorum dune, by using multiresolution segmentation, object-oriented classification and threshold extraction methods, made quantitative extraction of the Nitaria tangutorum dune. The results show that the spatial position accuracy of Nitaria tangutorum dune can reach 94%, which could completely meet the requirement of the research. Compared with the actual area of Nitaria tangutorum dune, the linear regression R2 can reach 0.77, the standard deviation is 5.77, but the area extraction accuracy remains unsatisfactory, which needs further improvement.
In this paper,the strategy to extract accurate building boundary from LiDAR data and images was explored. The workflow is as follows:first LiDAR data and images feature are used to extract building blobs. Then contour extraction candidate regions are established, and gradient and direction information of the candidate points are calculated to build the classic energy function. Finally energy function is computed with GCBAC algorithm, and the building boundary will be generated after the iterative optimized approach. The three experiments show that the strategy proposed in this paper is an effective method.
Low-altitude UAV remote sensing technology has become an important means of remote sensing technology. With the continuous development of the technology, its sensors have also changed from the visible ones to the multi/hyperspectral ones. However, due to the limitations of a small UAV payload on the sensors, the data quality of these new types of sensors is poor and hence it is difficult to deal with existing methods directly. Therefore, the authors studied the data obtained by UAV infrared sensors and then optimized parameters and removed gross errors based on the SIFT matching algorithm. This method has made robust matching results and can solve the key technology of the late mapping application of multi/hyperspectral data. The authors used a set of UAV infrared data to test and verify this method. The experimental results show that this method is capable of obtaining robust matching results and has a great value in improving applications of UAV multi/hyperspectral sensors.
Remote sensing data, as important information for flood disaster monitoring and loss assessment, can timely obtain the spatial-temporal distribution characteristics of flood. However, as it is restricted by weather conditions, it cannot form a dynamic and continuous process data. In this study, multi-temporal GF-1 satellite remote sensing clear images were used to extract the flood extent area based on bacha breach on the Heilong River in 2013. The flood inundation process was transformed into a numerical problem of partially differential equations by level set function. Finite difference method both in space and time was used to simulate the results of daily flood inundation area from August 24 to October 8. The results show that, compared with remote sensing data, the spatial-temporal consistency and the Kappa coefficients are 0.921 2 and 0.893 2; Compared with statistic data,the relatively error is less than 10%. This method has provided a scientific basis for the decision of flood disaster emergency response without prior information.
Height information created by LiDAR data is generally used for building extraction. LiDAR data can produce highly accurate, reliable 3D point clouds of ground objects. However, LiDAR data is expensive. In view of such a situation, this study aims to extract buildings solely using UAV imagery data. The height information used is created by point clouds derived from UAV stereo pairs through dense matching algorithm. In this study, UAV imagery was used as a single remote sensing data source and building extraction was carried out by the integration of objected-based method and SVM classification. In the preprocessing period, Pix4D Mapper was used for aerial triangulation and photogrammetric point clouds generation. Then, an objected-based method that utilized spectral information and geometric features was developed, the object height was derived from photogrammetric point clouds to assist in the detection of the building. Finally, the building boundaries were extracted through SVM classifier. In the post-processing procedure, morphological operations were applied to remove small objects from building images. To validate the photogrammetric point cloud usefulness, experiments were conducted on UAV imagery data, covering the selected test areas in Hanwang Town of Sichuan Province and Linpa Town of Henan Province. The building extraction accuracy was accessed on the test areas, and building detection completeness of Hanwang test area is 85.5%, detection correction is 83.9%; building detection completeness of Linpa test area is 92.5%, detection correction is 78.6%. The results show that nDSM derived from photogrammetric point clouds can be used for building extraction, and can improve the detection accuracy of the building.
For multitemporal hyperspectral images, the spectral characteristics of the same land cover object may vary significantly. Therefore, manifold alignment algorithm was employed to find a feature space in which data distributions of both images become the same. The method includes three steps. Firstly, a standard linear or nonlinear dimension reduction method is used to reduce the dimensionality of hyperspectral images. Secondly, the Procrustes analysis method is utilized to remove the translational, rotational and scaling components from one set so that the optimal alignment between the two data sets can be achieved. Finally, the nearest neighbor algorithm is applied for classification. Experimental results using multitemporal hyperion images demonstrate that the proposed approach can obtain performances which are superior to those of several popular manifold alignment methods.
In view of the study status of traditional speeded-up robust features (SURF)algorithm, an improved image registration algorithm based on SURF was proposed in combination with the image blocking strategies and the relative distance theory. The proposed algorithm can improve image uniformity of the feature distribution by image blocking strategy and increase the matching accuracy of the feature point through relative distance theory. With the quantitative indicators of correct feature point matching rate and RMSE, the authors selected the QuickBird satellite data of Shapingba District in Chongqing as the test area to verify the image registration results by using the improved algorithm based on SURF. The results show that the correct feature point matching rate of improved SURF algorithm reached 88%, higher than that of the traditional SURF algorithm (the rate is 76%). Excluding the mismatching points by relative distance, the RMSE of the final registration results reached 2.69 pixels. It meets the basic need of high-precision image registration(the RMSE is 2 pixels around), achieves the automation of remote sensing image registration and thus has some promotional value.
Based on the MODIS near infrared atmospheric precipitable water products with the resolution of 1km×1km and elevation data, using GIS spatial analyst and mathematical statistics method, this paper analyzed the spatial distribution and spatial correlation of atmospheric precipitable water in the Tianshan mountains during the period from 2003 to 2013.The results show that the atmospheric precipitable water in western mountain area is higher than the eastern mountain area. The atmospheric precipitable water in the Tianshan mountains has significantly positively correlated and its global spatial autocorrelation index is 0.899 8. The atmospheric precipitable water in the Tianshan mountains tends to be spatially clustered. The cluster of high values (HH) accounts for 35.94% of the total and are mostly distributed in elevation 2 000 m in surrounding area of Tianshan mountains. The cluster of low values (LL) accounts for 38.79% of the total and concentrated in the central and eastern region of the Tianshan mountains with elevation 3 000 m. The spatial outliers in which a low value is surrounded primarily by high values (LH) are scattered in the Tianshan Mountain. The spatial correlation coefficient between atmospheric precipitable water and elevation is -0.831 3. Elevation is the main reason for the distribution and difference of spatial clustering pattern.
Retrieving fog physical parameters becomes one of the major hot spots of study in recent years based on remote sensing data. The visibility, top height of fog, effective particle radius, and liquid water path (LWP) of fog are the fundamental physical parameters for fog monitoring. In this study, the authors retrieved fog physical parameters from southwest Jiangsu Province according to the path model of fog radioactive phenomena and SBDART based on the MODIS images. The authors verified the visibility and top height of fog according to the data from the Nanjing Information Engineering University and analyzed the influencing factors for the changes of physical parameters. The results showed that the correlation coefficient of visibility and top height of fog was 0.908 3 and 0.980 7, indicating that the retrieval of remote sensing data was feasible. The study also found positive correlations between the fog physical parameters,the surface elevation and vegetation index. The vegetation index was negatively correlated with the radius and optical depth and positively correlated with the liquid water. There was a positively correlation between the visibility and the surface elevation.
Solar radiation is the most important energy source in the Earth. The Yangxin's research shows that the effect of DEM scale causes great uncertainty to the simulation of solar radiation, and the impacts of DEM resolution on the simulation of the solar radiation are much greater in hilly area than in the mountainous area. To estimate the solar radiation model (SRAD), the authors measured the micro terrains with the help of real-time kinematic (RTK) and achieved the 0.1 m×0.1 m high-resolution DEM by TGO and ArcGIS10.0 software. Then the authors analyzed the correlation between the solar radiation and the land surface temperature. It is found that the solar radiation is differently distributed on the micro-landform. Groove ridge, sunny and gentle slopes accept more solar radiation than groove bottom, shady and steep slopes. The radiation is in descending order of summer(2 149.96 MJ/m2), spring(1 903.97 MJ/m2), autumn(1 461.86 MJ/m2) and winter(1 093.11 MJ/m2), and solar radiation is reduced gradually with the increase of the grade of slope. The results show that the land surface temperature is significantly correlated to solar radiation (0.622).
Based on large quantities of remote sensing data and topographic data, the authors studied the evolution of the shoreline and mangrove wetland in Lingdingyang Estuary since 1978. The results show that the evolution of the shoreline in the east bank and in the west bank was different from 1978 to 2014, and the shoreline has been mainly man-made since 1978. In 2014, the artificial shoreline accounted for 73.3% of the total length of the shoreline. Compared with the west bank, the east bank was developed faster. The mangrove wetlands were largely lost because of intensified human activities in the study area from 1978 to 2014. The analytical results clearly show the fluctuations for the areas of mangrove wetland in the past four decades. Many natural mangrove forests have disappeared because of reclamation projects. Only those in the reserves, such as the Qi'ao Island, Futian mangrove forests, have been well protected under strict conservation policies.
The coastal reclamation is an important way for people to access marine resources. Monitoring the coastal reclamation changes is an important task in coastal zone management and coastal zone evolution study. However, the coastal reclamation feature is complex, and it is difficult for remote sensing techniques to efficiently monitor reclamation. In this paper, the authors propose an ensemble classification algorithm for identifying four categories of reclamation using GF-1 imagery. The ensemble classification is constructed based on minimum distance algorithm and 10 features from manually extracted image objects. The 10 features include four mean features of each object in the four bands of GF-1 imagery respectively, mean value of the four mean features, object size, object perimeter, external rectangular area, ratio of object area, external rectangular area, ratio of object perimeter and object area. The proposed method was extensively tested by using two GF-1 images from 2013 and 2014. The results show that the highest accuracy of single feature model is up to 82.03%, and the accuracy of spectral features based ensemble model and that of the spatial features based ensemble model are 63.28% and 87.50% respectively, and the accuracy of full feature based ensemble model is 80.47%. This study provides a useful solution for monitoring the coastal reclamation.
With the popularization and application of high resolution remote sensing data of domestic satellite image, the imported satellite data will be partially substituted as the preferred data source in the updating of remote sensing background data. Using the ZY-1 02C and GF-1 images, the authors proposed a practical technical scheme of remote sensing image background data update and, taking remote sensing image data update in geological hazard prone areas as an example, verified the applicability of the method for updating the background data. The accuracy can meet the plane accuracy requirements of the remote sensing image background data at the 1:50 000 scale. The technical solutions are reasonably practicable. and can provide important technical support for the development of remote sensing image background data update as well as expansion of the domestic satellite data in the field of geological disaster survey and monitoring and other fields. It has certain popularization value.
Geochemical compositions have significant implications for rock classification,identification of the petrogenesis and evolution of the rocks. The utilization of remote sensing method to estimate the geochemical compositions of the rocks is a new subject, and is also a difficult point in remote sensing related researches due to its relatively immature applications. In this study, he Permian basalts were chosen as the study object. Based on systematical sampling, spectral analysis and geochemical test, the authors constructed a mathematical model between field measured spectra data (2 150 bands) and available data of six representative major elements by using partial least squares regression (PLSR). It is essential to initially choose proper preprocessing method to optimize the spectra data, and then search for the optimal number of principal components with minimum root-mean-square error through k-fold cross-validation. The results show that the PLSR model yields higher stability and precision,and plays a significant role in applications of geochemical composition inversion using remote sensing data.
In China, iron tailings dumps have been accumulated up to about 5 billion tons. The tailings have led to extremely serious dust pollution. Therefore, dust effects on leaf spectra were studied on the basis of the observation of real experiments with Anshan mine tailings dust and by means of artificial simulated dust and spectral measurements. The dust samples of iron tailings were taken from the Anshan mining area. The quantitative inversion of foliar dustfall was realized by using the band of the best correlation between the dustfall and the vegetation leaf spectrum and the characteristics of absorption spectra of iron respectively. The results show that, when the dustfall of iron tailings on leaf increased, the differences of spectral curve between leaf and dust decreased. In the two inversion methods, dustfall and vegetation leaf spectral variables were significantly related to each other. Furthermore, the precision of the inversion modeling according to spectral characteristics of iron is higher than that of the one according to best correlation band. The results could provide basic model and technical basis for quantifying the amount of mining dust monitoring with hyperspectral remote sensing.
In this paper, the authors reconstructed MOD13Q1 time-series NDVI data from 2001 to 2013 using Savitzky-Golay filter and Chebyshev Polynomial methods for classifying vegetation types in the six coalfields in Shanxi Province. The key phenological parameters were extracted from the reconstructed NDVI data, such as the beginning dates of the growing season, length of the growing season, the ending dates of the growing season, the maximum NDVI value and the responding dates. The results show that different vegetation types of the six major coalfields in Shanxi have different phenological features. Cropland has distinguishable differences from grass and forest. Similarly, forest is distinguished from grass and cropland by integration of total growth. It is shown that the classification of vegetation types can achieve better results by extracting and analyzing the phonological parameters compared with multi-temporal unsupervised classification. The overall classification accuracy reaches 89.67%. This study provides a robust method for assessing long-term ecological conditions and monitoring vegetation coverage changes of the six major coalfields in Shanxi Province.
In this paper, the authors selected July 21, 2012, the biggest rainfall day since the founding of People's Republic China in Beijing, as the study target. The rainfall data from both Tropical Rainfall Measuring Mission (TRMM) and meteorological observations and MODIS LST products were mainly used to study the spatiotemporal distribution of rainstorm and the relationship between urban heat island (UHI) and urban rain island (URI). The spatial interpolation, spatial downscaling, accuracy assessment and correlation analysis were used in the study. Some conclusions have been reached. Firstly, the heavy rainfall area was located mainly in southern Beijing. The rainfall process moved from west to east, as shown by tracking the rainfall maxima of 3 h TRMM data. Secondly, the accuracy of TRMM data was improved by downscaling, as evidenced by the fact that the correlation between TRMM data and observational data was improved and RMSE decreased simultaneously. Finally, the spatial distribution of URI is consistent with UHI and the correlation between the two can produce optimal result in the maximum rainfall periods.
Hapke photometric model is a useful tool for studying the spectra of mixed minerals. However, there are still some improvable things, and domestic research still lags far behind that of foreign countries. This paper focuses on the characteristics of surface minerals through 4 groups of spectroscopic tests in laboratory, and then discusses and points out the accuracy of the Hapke photometric model when simulating the spectra of mixed minerals. The mean of root mean square errors (RMSE) of the 4 groups by using IMSA model is 0.014 4, and the mean of correlation coefficients (R) is 0.994 7. The mean of RMSE of the 4 groups by using AMSA model is 0.008 4, and the mean of R is 0.994 4. These data suggest that IMSA model and AMSA model have a very high precision and can be a good means to simulate spectral mixture of mixed minerals. Nevertheless, the experiment results show that, when the mixed minerals contain biotite, the accuracy is not satisfactory, but the accuracy of simulation can be improved by adjusting the weight of biotite. Spectral shape of mixed minerals needs a specific analysis of compositions of the mixed mineral, for instance, a particular mineral which possesses a higher mass fraction in the mixed minerals may not play the leading role in the spectral shape, while the mineral of low reflectivity may play a more important role.
To explore the changes of time and space of the Loess Plateau vegetation status is of great significance for vegetation restoration in this region. Two indexes were established on the basis of the MODIS-EVI2 sequence data in the Loess Plateau region from 2001 to 2014 to reflect vegetation cover state from different sights. The first one is density of vegetation cover(DVC), and the second one is time of vegetation cover(TVC). Then these two indexes were analyzed with trend analysis method and breakpoint analysis method. Finally the spatial and temporal evolution of vegetation cover in the Loess Plateau in the past 14 years was obtained. Some results have been obtained:① Vegetation cover in the Loess Plateau increased from northwest to southeast, and the vegetation cover in the whole region grew faster than from 1986 to 2006. ② The rising trends of TVC and DVC areas were 43.04% and 32.57% of the whole study area, and the most prominent change happened during 2006-2007 and 2011-2012. The declining trends of TVC and DVC areas were 2.92% and 5.68% of the whole study area. The changed farming areas were mainly affected by the early stage of returning farmland to forest. The results show that the policy of returning farmland to forest and windbreak in recent years has made an obvious effect.
Lithological interpretation is a very important part in remote sensing geological interpretation. First, interpretation keys must be established according to the lithology and its combination types of the geological body in the area to be interpreted. The stratigraphic division in Bayan Hara Mountain Group is relatively difficult and controversial, resulting in the existence of a large number of division schemes. That is because of single lithology, multilayer formation thickness, single top-bottom and rare fossils of flysch formation in Bayan Har Mountains Group. Aimed at tackling this situation, using remote sensing method, and starting with the lithological interpretation based on the SPOT5 and ETM data, the authors obtained information to the greatest degree. The lithologic interpretation and classification was based on detailed elaboration from four aspects in this study. A systematical description was made on the process of establishment of interpretation keys based on visual image. And then the stratigraphic image was redelimited through the established interpretation keys. The rock section and lithological association were subdivided for each stratum. Some new understanding of formation lithology was obtained in the study area. This would provide new data for stratigraphic correlation as well as regional geological survey in Bayan Har mountains.
In order to deal with the multi-source and multi-scale spatial data content integration and ensure the plotting correctness of the content, the authors have handled the multi-dynamic plotting information in which there exist such cases as the plotting marked with the same thing, different standards for foreign body, synonymous standard, and the same standard for the foreign body. The key problem is to analyze the characteristics of plotting information for themselves, perform source data analysis, extraction and conversion, and build data integration standards and quality control system. In addition, the authors have broken through the restriction that data are always from different sources or with outlines. As a result, the authors have achieved the application-oriented multi-scale spatial data integration.
Soil moisture content estimation is one of the important research fields in the GNSS-R (Global Navigation Satellite System Reflectometry, GNSS-R) land surface remote sensing. In recent years, many experts have done a lot of research on the theories of soil moisture estimation, receiving and processing of GNSS reflected signals, ground-based/air-borne experiment, estimation model and accuracy evaluation, which has greatly promoted the development of GNSS-R land surface remote sensing technique. Based on the previous research results, the authors built the framework of soil moisture estimation using GNSS-R and carried out the initial software implementation by integrating different estimation models. By verifying the models and functions of the software using public datasets for GNSS-R research, it is demonstrated that the software can provide effective technical support for GNSS-R data processing and model validation in soil moisture estimation.
With the increasing access to remote sensing data and the rapid development of network technology, remote sensing data on-line automatic integration of the service demand is growing, and there has been no satisfying online visual and automated computing platform for remote sensing data based on services chain so far. This research is based on B/S architecture and services chain as well as the workflow technology. The authors propose an integrated visual platform to store data, design model, compute model, distribute and display result information all in one stop. And Users can integrate the function from data selection, the model design to achieve model on demand in a friendly visual Web interface. Actually, this research is based on the reusability of remote sensing processing module, the goal is to quickly build and implement the process of remote sensing information models (RSIM),and it is an effective attempt to achieve online automated service for remote sensing image.