The microwave land surface emissivity(MLSE)is a very important parameter for describing the characteristics of the lands, and it is also a key factor for retrieving the parameters of land and atmosphere. Space-borne passive microwave radiometers provide direct retrieved land surface emissivity spectra with larger temporal and spatial scales compared with physical modeling simulation in that the physical modeling simulation needs plenty of parameters, but quite a few of these parameters, such as parameters of land surface and vegetation, are not available from traditional measurements. This paper systematically reviews MLSE retrieving algorithms for passive microwave remote sensing data, which include statistical approach, atmospheric radiation transfer model approach, index analysis approach, neural network approach and one-dimensionally variational analysis approach. The main advantages and limitations of these five methods are also discussed. Finally, the development tendencies of estimating MLSE by remote sensing are pointed out, such as developing algorithms of Radio Frequency Interference (RFI) detection and correction, improving algorithms of detection of clouds and rain-affected radiances, and intensive research on microwave atmospheric radiation transfer process.

Impervious surface, as an important indicator to measure the urbanization degree and environmental quality, has attracted more and more attention. The magnitude, location, geometry, spatial pattern of impervious surfaces and the ratio of perviousness-imperviousness significantly affect regional eco-environment changes. Extracting and mapping impervious surface by means of multiple remote sensing data and analytical methods have constituted a hot topic in these research directions. In this paper, impervious surface extraction methods are summarized from traditional method of remote sensing, extraction based on spectrum and geometrical features and artificial intelligence algorithms, then the principles, characteristics, application fields are described, and finally the future prospects are pointed out.

Being simple and effective, the split-window algorithm based on thermal infrared remote sensing is widely used to retrieve surface temperature. The method mainly uses thermal infrared bands in 10~13.3 μm(1 000~750 cm^-1) range, neglecting bands in 8~9.09 μm (1 250~1 100 cm^-1) range. This paper analyzes the process of deriving the formula of the split-window algorithm, summarizes the problems associated with the channel setting and makes numerical simulation analysis in the 10~13.3 μm (1 000~750 cm^-1) and 8~9.09 μm (1 250~1 100 cm^-1) ranges to solve the problems. The results show that split-window algorithm derived on the basis of this approach has similar performance in both 10~13.3 μm (1 000~750 cm^-1) and 8~9.09 μm (1 250~1 100 cm^-1) spectral ranges. Therefore, it can be concluded that the spectral range in 8~9.09 μm (1 250~1 100 cm^-1) range can also be used to derive split-window algorithm for thermal remote sensing.

A rapid sub-pixel corners detection method based on image block for UAV (unmanned aerial vehicle) image is proposed with the purpose of improving the pixel level precision of corners location and the efficiency in Harris algorithm. With this method, we can screen corners by similar pixels in nearest and diagonal neighborhood direction, carry out Harris corners detection by auto-adaptive threshold based on image block, and then refine the initial corner by traditional Harris algorithm from the Euclidean distance between corners cluster and ideal corner by the least square method with weight. Tests show that the method is effective and practical for UAV image corners detection, and can improve Harris corners detection process speed greatly due to the reduction of the computation and also make corners well distributed.

The primary filtering of the pre-matching road sections in the map matching algorithm, in which GPS point finds the closest approximation road for driving, is the key factor of the time efficiency. There is a great deficiency of redundant searching range in the existing primary filtering methods. To reduce the searching range, this paper proposes a novel four-level interlaced grid filtering method, which can efficiently narrow the searching range. It is indicated that the method proposed in this paper is superior to the existing methods on the premise of guaranteeing the stability of the filtering range, as shown by experimental comparisons with many traditional methods based on GPS data. This method has high application values in many fields such as the vehicle navigations and large-scale GPS data processing because it can further reduce the searching time by controlling the searching range effectively.

Based on the spectral first order differential techniques and equivalent MODIS vegetation index according to spectral response function using the field sampling survey, this paper built the hyperspectral remote sensing estimation models for total biomass and LAI of wheat named Jingdong 12. The research shows that: 1the largest correlation between total dry biomass and canopy spectra lies at 552 nm and 721 nm, and the relationship between LAI and canopy spectral is significant in the band range of 400~1 100 nm; 2the relationship between red edge position (REP) and wheat biomass is most significant, with R being 0.818; 3in the 6 vegetation indices similarly to MODIS-VI, EVI is most sensitive to total dry biomass; 4the linear model using REP estimation biomass is the best,with R² being 0.669 4. Exponential model between LAI and EVI has the strongest fitting degree, with R² being 0.94. Using field spectral data and band response function to retrieve wheat parameters can provide important research methods for making use of satellite remote sensing data characterized by large area, non-destruction and acquisition of timely ground vegetation information.

On the basis of the existing preliminary accuracy validations of ZY-3 satellite imagery, a further in-depth analysis and verification of the geopositioning accuracy were performed at a typical site with both flat and mountainous terrains. The rational polynomial coefficients (RPC) were refined through block adjustment with high accuracy GPS points as ground control points (GCP) and check points. Digital surface model (DSM) and digital ortho map (DOM) were automatically generated with the refined RPC, the planimetric and stereo geopositioning accuracies were evaluated, and the influences of terrain, number and distribution of GCPs were analyzed. The results show that the horizontal and vertical geopositioning accuracies of ZY-3 imagery of the test site are 1.476 m and 2.213 m respectively in the flat area, and 1.506 m and 2.895 m respectively in the mountainous area, all meeting the requirements of 1∶50 000 DEM and DOM production both in the flat area and mountainous area. The ZY-3 imagery can also be used for revision of the 1∶25 000 topographic maps.

Gaussian mixture model (GMM) is used to describe the probability density function of remote sensing data. According to the process of parameter estimation and the calculation of posterior probability,remote sensing information extraction can be realized. For the purpose of improving the accuracy of the extraction by GMM, Markov Random Field (MRF) is applied to calculate the prior probability of each feature in the pixel’s neighborhood to replace the mixing probability of the feature, and spatial correlation is reflected by this way. Then simulated annealing (SA) is utilized for the acquisition of overall optimum estimation of parameters. With the parameters, posteriori probability for every feature of each pixel is computed and the distribution of features is obtained. Extracting information from the images obtained from the remote sensing test site reveals that the new method has a better performance, thus proving the effectiveness of the above-mentioned improvements.

In the dual-aspect compensation procedure, the best compensated result occurs under the condition of optimal incidence angle pair, which varies with terrain. Nevettheless, the problem as to how to obtain this pair remains unsolved. To solve this problem, this paper proposes a new method in search for the optimal incidence angle pair based on simulation. Firstly, DEM data are used to produce the layover and shadow mask images at different incidence angles from two different aspect directions respectively. Then from these images, the optimal incidence angle pair was searched out to obtain the best compensating result. On such a basis, the best incidence angle pairs in three areas of different topographic condition for the "dual-aspect compensation" method were given by experiment and the effects were analyzed. The results show that, with this method, the incidence angle pair could be obtained easily and effectively. The method can therefore guide the users to order the best SAR data when they use the dual-aspect compensation in mountainous areas, and this is the essential step in dual-aspect compensation.

A new cloud removal algorithm based on spectral and geometric information is proposed for generating cloud free and cloud-shadow free mosaic image from multi-temporal TM images. At first,single image and multi-temporal images of thick cloud and cloud-shadow multi-spectral detection models are built based on TM spectral characteristics analysis. Secondly, cloud is detected according to spectral characteristics,and then a technique is applied based on coupled geometrical relationship between the cloud and its shadow by using sun azimuth angle, sun elevation angle and statistical distance between cloud and its shadow so as to automatically predict the approximate location of cloud-shadow. After that, erosion filtering and dilation filtering are used sequentially in the cloud and cloud-shadow fraction image to eliminate the small bits and generate the exact zones contaminated by cloud and cloud-shadow. At last, the cloud and cloud-shadow zones of the target image are replaced by the same-location cloud-free zones on reference images whose spectral information is matched with the target image by the linear regression method. The results show that this algorithm is capable of eliminating the cloud influence from TM image significantly. Moreover, it can effectively eliminate the influence of water body and hill shadow on cloud-shadow. This method can therefore support producing cloud removal images in a quick, simple and applicable way.

Currently, application-oriented researches on the data of Medium Resolution Spectral Imager (MERSI), which is on board China’s new generation polar orbit meteorological satellite FY-3, are very insufficient, due to the reason that the data as a new source have been delivered only since 2008. With the normal operation of FY-3 satellite system, it is necessary to develop an operational module for FY-3/MERSI regional land surface temperature (LST) retrieval and its post-processing, since LST is required for a wide variety of scientific studies but FY-3/MERSI’s operational LST products have not yet been provided by National Satellite Meteorological Center (NSMC). Based on an analysis of FY-3/MERSI L1 data’s HDF5 format and its channel characteristics, the authors selected the generalized single-channel algorithm developed by Jiménez-Muñoz & Sobrino to directly realize the LST retrieval at 250 m spatial resolution with MATLAB programming and the thematic mapping of LST derivative products. This paper describes the parametric processes of LST retrieval algorithm in detail, which include radiometric calibration, cloud detection, estimation of two intermediate parameters-surface emissivity and atmospheric water vapor,and calculation of thermal indexes from LST. On these bases, an automatic flowchart for FY-3/MERSI LST retrieval and thematic mapping was established. Experimental results of this flowchart applied in Shanghai thermal environmental monitoring show that it can process FY-3/MERSI L1 data in a fast, real-time and automatic way, thus suitable for operational products producing and sharing, with the saving of human resources. It is also proved that FY-3/MERSI data and various forms of LST products can reveal the spatial pattern of Shanghai thermal field and the urban heat island effect more finely and intuitively.

Impact crater is the major geomorphologic unit of the lunar surface. Its shape and distribution characteristics can provide a large amount of information about the impact history and the lunar evolution process. In this paper,based on the real shape of craters, the authors proposed an automatic extraction method of elliptical craters. The method consists of three steps: image preprocessing, edge detection and ellipse fitting. The last step uses principal component analysis (PCA) of local interesting parameters and Hough transform to derive multiple ellipses. The total accuracy based on a comparison with manually-derived result is over 76%, with the result of larger craters even in excess of 83%. Statistics of the size-frequency distributions of the craters show that, when the length of the long half axis (D) is smaller than 160 meters, the crater number N and D^-3.8 has a significant linear relationship.

High resolution remote sensing image can provide a lot of spectral and textural information,and both of the two kinds of information can help effectively detect the changed information. However,the traditional methods of change-detection based on medium or low spatial resolution remote sensing images only use the spectral information to extract the changed information,with the ignorance of the textural information. In this paper,both the spectral and textural features are integrated in one change-detection method to extract the changed information from high resolution remote sensing image,and the method is called difference principal component transformation. The advantages of the proposed method can be concluded in two aspects. One is that it will be easy to get the internal changed details in the large changed areas according to textural information,which can compensate for the deficiency of spectral information in high resolution images. The other is that some post-processing procedures such as connecting narrow gaps and filling holes can make the change-detection result more complete. The experimental results show that some changes that are spectrally similar but texturally different can be effectively detected after adding textural information in this change-detection method.

K-means clustering algorithm is a kind of hard classification based on the Euclidean distance, with each data point assigned to a single cluster. Due to the uncertainty and mixed pixels in remote sensing image,it is difficult for the traditional K-means clustering algorithm to obtain satisfactory classification results. To overcome this drawback,the authors applied the SPA(set pair analysis)theory to the clustering algorithm of remote sensing image. The IDC(identical discrepancy contrary)connection degree model,which can descript unitarily the identity,discrepancy and opposition,was employed to improve K-means clustering algorithm. The improved algorithm has overcome the limitation of K-means clustering algorithm to certain extent. Clustering analysis experiments of Landsat TM image show that the improved K-means clustering algorithm is superior to K-means in classification accuracy of ground cover class components of mixed pixels.

In this paper, part of the loess plateau was chosen as the study area. A set of automatic information extraction methods for water-erosion desertification was proposed by using the ETM⁺ images obtained in this area and on the basis of remote sensing data and geographic information system. NDVI (normalized difference vegetation index), KT3 (KT transform,humidity), slope, DEM (elevation) and typical feature spectral data were used to establish the characteristic bands of the study area, and then a decision tree classification rule could be constructed, which could exclude the non-water erosion desertificatin information effectively in the study area. The object-oriented muti-scale segmentation technology was adopted, and the slope, gully density and vegetation coverage were taken as the characteristic bands of the water-erosion desertification classification. With the building of the multi-characters space, the weight value was determined by the analytic hierarchy process, which also served as the classification index of the water-erosion desertification. The consistency of the evaluation between the automatic extraction results and the visual interpretation results shows a good linear relationship, with the overall consistency reaching 82.8%.

ASD Field Spec Pro FRTM spectroradiometer was used to measure the spectral response of the vegetable and grass at different vegetation coverage levels. The data were applied to calculate six vegetation indices, i.e., NDVI (normalized difference vegetation index), DVI (difference vegetation index), RVI (ratio vegetation index), MVI (modified vegetation index), MSAVI (modified soil adjusted vegetation index) and GEMI (global environment monitoring index). Then the best combination of spectral bands was analyzed. Furthermore, the performance of different vegetation indices was investigated when they were used to estimate the vegetation coverage by using the dimidiate pixel model. The results show that, for the green vegetable, the best combinations of bands in the spectral region from 620 to 740 nm and from 780 to 900 nm have the best correlation with the vegetation index, whereas for the grass, the best combinations of bands are from 620 to 750 nm and from 760 to 900 nm, with the correlation coefficients of the two cases being all larger than 0.8. The bands of Landsat7 and HJ-1A CCD1 simulated according to the spectral response function were employed to calculate the six vegetation indices. The average overall accuracy for estimating the vegetation fraction by DVI and MSAVI is 83.7% and 79.5% respectively, indicating that they are superior to the other four vegetation indices as the input of vegetation index for the dimidiate pixel model.

With the coal fire area of the Shuixigou mine in Xinjiang as an example and on the basis of observation data of infrared radiometer at the same time of passing aviation of Landsat 5 on June 31, 2011, the authors calculated surface temperature at pixel scale using several schemes, comparatively studied the surface temperature of the coal fire area inversed by mono-window algorithm, and generalized single-channel algorithm and Weng algorithm with TM data. The results show that all the three algorithms show a consistent distribution of surface temperature of the Shuixigou underground coal fire area, and the mono-window algorithm and generalized single-channel algorithm have the smallest difference in the average surface temperature of the whole study area, which is about 1.60℃. Through a comparison with the ground measurements, a lower difference value is obtained by all the three algorithms, and the retrieved data by generalized single-channel algorithm are highly close to the data retrieved by mono-window algorithm, wih the regression coefficient and RMSE being 0.886 and 1.48℃ respectively. The retrieval results of generalized single-channel algorithm are in line with the spatial distribution law of the temperature of the underground coal fire area, and the high-temperature anomaly district is obvious. The result of the retrieved data of surface temperature is acceptable and the generalized single-channel algorithm is somewhat effective in acquisition of the LST of the underground coal fire area, thus providing a reference for the dynamic monitoring and evaluation of underground coal fire areas.

Using the 2nd national land survey data and focusing on the semantic generalization and graphics generalization, the authors reduced the dimension of the area-patches to achieve the area proportion of different kinds of land-use. At the same time, the authors proposed the minimum area principle. Experiments have proved that the generalization results guided by the principle can more accurately reflect the geographical attributes of the original map.

In order to solve the problem of restoring missing information of mountain shadow on remote sensing images in peak cluster karst area,this paper took Guohua Ecological Experimental Area as the study area,which lies in Pingguo County of Guangxi. The spatial variability of digital number (DN) was analyzed by applying geostatistic method to the three bands,i.e., B3,B4 and B5. Then the DN values of the mountain shadow areas on the three bands were predicted and verified. The results indicate that the DN values of the these bands show strong spatial variability and spatial autocorrelation mainly from the impact of intrinsic factors,and their ranges are above 360 m. The correlation of values between validated values and restored values are extremely significant for each band, and the average relative errors are all lower than 0.2% with very high precision. The restoration of missing information of the mountain shadow on the remote sensing images in peak cluster depression areas provides a new idea and method for improving the accuracy of remote sensing interpretation of ecological environment monitoring of karst rocky desertification assessment in same areas.

High resolution satellite remote sensing technology is an important new technology and method in the field of geological exploration,with which many application results have been made. However, the typical applications to identifying and analyzing the mineralization alteration information are very insufficient. In this paper,the typical application of high resolution satellite remote sensing technology in identification and analysis of the uranium mineralization bleached alteration was illustrated from the angle of uranium exploration,including the identification of bleached alteration information and the analysis of its spatial distribution in the Bashibulake uranium ore district on the northern margin of Tarim Basin in Xinjiang,the discovery of grayish white bleached alteration and the analysis of its spatial distribution in the eastern part of Keping up-lift on the northern margin of Tarim Basin in Xinjiang. These two typical applications show that high resolution satellite remote sensing technology can achieve good application effects in identifying mineralization alteration information,and the further excavation of its application potential is very valuable in the future.

Research on using remote sensing technology to extract the ductile structure information was conducted in this paper. The tectonic deformation history in Wudang area is complex, represented by ductile shear deformations. The thrust and gliding nappe ductile shear zones in Wudang area were analyzed by using ETM remote sensing image, and the results show that, although there is no significant abnormal interface of the shear zones in the remote sensing image, the tones of the image on each side of the ductile shear zone are obviously different because of the different geological components. Thus good identification results of the image characteristics and the direction changes of the foliation lineation combination can also be obtained by the wavelet analysis and the extraction of the foliations. Using the methods mentioned above, the authors interpreted and analyzed six typical examples including the Yunxi-Yunxian gliding nappe shear zone and the Fangxian-Zhushan thrust nappe shear zone, and the result is consistent with the practical investigation, thus providing a useful reference for the remote sensing interpretation of the ductile shear zone.

In order to enrich the information and broaden the research field of the geochemical evaluation,the authors carried out multi-purpose regional geochemical survey (MPRGS)by using the remote sensing (RS) technology based on the regular geochemical survey. The authors compared the advantages between the RS technology and the traditional geochemical investigation methods in such aspects as sampling point arrangement,sample acquisition and quality evaluation. The theoretical basis of the RS application in geochemical evaluation was discussed. With several examples,the technical procedures of the RS application in MPRGS work were summarized,which was a useful attempt to enrich the means of the MPRGS work. The application results of the RS technology in the MPRGS work show that using the RS technology in geochemical survey can help us expand the field of the RS application research and widen the field of geochemical research,and therefore the RS technology will surely become an important tool for the MPRGS work in the future.

From the point of view of spectral curve and spectral feature space and through the analysis of the spectral data of three main ground objects measured in the field, i.e., rocks , soil and vegetation, in the two study areas of Tuquan in Inner Mongolia and Tongren in Qinghai, the authors found that the feature distribution forms are basically the same in the two study areas in both spectral curve space and spectral feature space, although there exist a bit difference caused by the geological environment, ecological environment and climate factors in different areas. Through the analysis and experiment, it is found that several new band combinations can better distinguish the three main ground objects in the spectral feature space and can also guide the classification of the three main ground objects based on the scatter plots of the remote sensing image. This research is helpful to improving the method for extraction of alteration information.

Mixed pixels are abundant in medium-low resolution images,but the traditional methods for image classification could only assign pixels to one class,with the ignorance of the mixed pixels. To tackle this problem,the authors selected the typical area in Tianshan Glacier of Xinjiang as an experimental area. Based on the theory of mixed pixel decomposition and the principle of the linear model and taking into account the spectral characteristics of TM/ETM⁺ image as well as the land cover characteristics of Tianshan area, the authors developed an end-member composition model suitable for the glacier area,i.e., Snow-Vegetation-Rock-Shade model. After the appropriate end-members were selected and the reflectance values were substituted into the improved linear mixed pixel decomposition model,which satisfied the constraints,the abundance image of individual end-member was calculated and the snow cover information was easily and precisely extracted. The extraction results of snow cover in 1989 and 2000 demonstrate that the mixed pixel decomposition and the linear model could be used to monitor the snow cover changes in the glacier area.

In order to build a more reasonable and accurate remote retrieval model than the models used before, the authors measured water spectral reflectance by field spectrometer in sea area of Hebei Province. Based on analyzing the relationship between spectral reflectance and actual measured value of chlorophyll-a concentration, the authors conducted correlation analysis between the reflectance of each band or band combination of MODIS data and measured value of chlorophyll-a concentration. The result shows that the best inversion band is B1. Then the remote sensing monitoring model of chlorophyll-a concentration was built by using B1 of MODIS data. The retrieval results and corresponding actual measured results were compared with each other at last. The results show that the correlation coefficient of this model is 0.66 and the root mean square error of inversion result is 0.48 mg/m³. The precision of this retrieval model is better than OC3 standard empirical algorithm of SeaDAS. Therefore, the model could be employed to retrieve chlorophyll-a concentration of surface water of Hebei sea area effectively by using MODIS data.

In order to predict the soil potassium content more quickly and accurately,this paper studied the relationship between soil spectrum and soil potassium content based on soil hyper-spectrum and chemical element analytical results. Based on preprocessing, the authors extracted the original soil spectrum and four parameters of spectra, i.e., reflectance spectra, first derivative reflectance spectra, inverse-log spectra and band depth, so as to establish the prediction model for potassium content by PLSR. The model was tested, and the results indicate that band depth is the optimum parameter for inverting soil potassium content, with a minimum modeling correlation coefficient of 0.85 and maximum RMSE of 0.1. This research shows that, as a non-destructive method, the soil spectrum with high spectral resolution in the whole range has the potential for the rapid simultaneous prediction of potassium concentration.

With the support of RS and GIS technology,this paper aims to analyze spatial distribution characteristics of the landscape patterns of Houma City as a case study. The results show that the landscape index can well reflect the changing characteristics of landscape patterns of each buffer zone in the study area. The method of making the buffer zone to extract samples has certain advantages in the analysis of gradient change on circle landscape patterns in plain cities of northern China. This study has certain significance and theoretical reference value for reasonably guiding urban expansion and reducing local ecological degradation caused by urban expansion.

With Zitong County in Sichuan Province, an old revolutionary base area, as the study area, the authors classified the factors affecting urbanization spatial development into three types, i.e., traffic, regional dynamics and landform, and, on the basis of ASTER GDEM,ETM and basic geographic information data, extracted nine factors, namely national highway, provincial road, county road, vegetable orchid area, ecological tourism area, ecological industrial area, stream system, slope and degree of topographic relief. The weights of these factors were calculated in combination with Analytical Hierarchy Process (AHP), and the spatial pattern adaptability gradation and zoning charts were compiled by means of spatial superimposition analysis in ArcGIS 9.3. The realization of this method has provided a entirely new workable means for scientific quantitative evaluation of urbanization spatial pattern model in the lower mountain hilly old revolutionary base area.