Forests are the most extensive terrestrial ecosystems in the world. Their vertical structure information not only reflects the spatial structure characteristics of the forest but also the physiological and ecological processes. The existing studies on forest vertical structure through remote sensing often encounter challenges such as high costs, difficulties in acquiring effective data, or complexities in data processing. In this study, a new technology for detecting the vertical structure of vegetation is proposed and validated, which can obtain the vertical structure of vegetation by single flight. In order to adopt the new technology, a radar system was developed, incorporating a modular design scheme that emphasizes high integration and lightweight characteristics, and it was deployed on an unmanned aerial vehicle (UAV) flight platform. It consists of a main control unit, a signal processing unit, and a data recording unit, which weighs only 0.92 kg in total. Meanwhile, a novel algorithm is proposed to reconstruct the vertical structure of targets, effectively addressing critical challenges in UAV radar signal processing, such as strong system coupled signal, significant noise in radar signals, and high sidelobes in images. In order to validate the capability of the new technology, UAV flight experiments, as well as in situ observation, were carried out in typical vegetation areas. The proposed algorithm was used to process the acquired radar echoes, yielding a root-mean-square error (RMSE) of 1.33 m for the vegetation height compared to the ground synchronous measurement.

Quad-pol synthetic aperture radar (SAR) is one of the most effective approaches for dry and wet snow identification data, but its applicability is limited by the high cost of quad-pol SAR data. Dual-pol SAR such as Sentinel-1 has larger spatial coverage, longer time sequences, and freely accessible data, but there is still a highly uncertainty in dual-pol SAR to distinguish dry and wet snow due to limited polarimetric information. In this study, a pixel neighborhood-based snow identification algorithm was developed and verified using dual-pol C-band SAR data in Northern Xinjiang, China. A total of six decomposed parameters were obtained to characterize the polarimetric information of dual-pol SAR data by modifying the H-$\alpha $decomposition applicable to dual-pol SAR data. In the case of limited training samples, polarimetric features that were most sensitive to snow identification were selected as the optimal features for support vector machine (SVM), and the result derived from SVM was employed as the initial labels of Markov random field (MRF) model to separate dry and wet snow using iterative conditional mode (ICM). Then, the proposed algorithm, dual-pol SVM-MRF (DSVM-MRF), was validated and compared with previously published methods. The results show that the DSVM-MRF acquires the superior snow recognition with the overall accuracy (OA) and Kappa coefficient of 84.5% and 0.58%, respectively.

Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.

Snow density is an important variable in snowpack research. The comprehensive applicability evaluation of the snow density datasets is a prerequisite of these datasets for their applications in hydrology processes and climate change, as well as in snow equivalent water retrieval algorithms. In this letter, the applicability of three snow density datasets, including European ReAnalysis (ERA)-Interim, ERA5, and the newly released ERA5-Land datasets, was first assessed using two ground evaluation datasets with different land covers from seven snow survey courses and four densely sampled networks in China. The results show that the ERA-Interim dataset significantly overestimates snow density during the entire snow season, with an overall root mean square error (RMSE) larger than 112 kg/m3, and lacks temporal dynamics. The ERA5 and ERA5-Land datasets are generally in good agreement with the ground measurements in China. The averaged RMSEs of the ERA5 dataset are 56.2 kg/m3 against snow course sites and 28.3 kg/m3 versus the densely sampled measurements, and those of the ERA5-Land dataset are 56.6 and 28.4 kg/m3, respectively. However, the ERA5 and ERA5-Land datasets still underestimate snow density over time, especially for the middle and late snow seasons. These new findings are expected to provide valuable feedback to model developers to further enhance the accuracy of snow density datasets.

Sensitivity analysis (SA) of model parameters is of great importance for understanding, development, and application of models. However, the influence of snow microstructure variability on snow water equivalent retrieval from passive microwave measurements is still unclear. This article explores the parameter sensitivity of the microwave emission model of layered snowpacks (MEMLS) with improved born approximation (IBA) by using a quantitative global SA method, the extended Fourier amplitude sensitivity test (EFAST) algorithm. A deep analysis is conducted, including the sensitivity of passive microwave emission to snow parameters, the sensitivity variation analysis for different snow conditions, and the temporal properties of the parameter sensitivity. The results show the exponential correlation length, snow depth, and snow density are the three most sensitive parameters for snow without salt in the MEMLS model for the brightness temperature gradient at 18.7 and 36.5 GHz. For snow with a small salt content, the exponential correlation length, snow depth, snow temperature, and snow density are the four most sensitive parameters. Second, snow parameter variability highly affects the microwave radiation. The sensitivity values of microwave brightness temperature to snow depth gradually increase when the exponential correlation length is less than 0.25 mm and then slightly decreases with the increase of exponential correlation length and decreases along with the increase of snow density. Finally, our analysis highlights the importance to include the snow density, especially for deep snow depth, in the combination of sensitive factors in future multiparameter retrievals.

As the second generation of Chinese polar-orbiting meteorological satellite missions, the Fengyun (FY)-3D satellite provides the latest multi-frequency brightness temperature (TB) of FY-3 series satellites. The microwave radiation imager (MWRI) boarded on FY-3D has similar sensor configuration as Advanced Microwave Scanning Radiometer 2 (AMSR2), and thus the intercalibration of these two sensors can make their TB data more consistent and continuous to facilitate their joint applications. In this study, the FY-3D H-pol and V-pol TB at five frequencies from 10.7 to 89 GHz during 2019 to 2020 were calibrated against AMSR2 TB over land. Two categories of intercalibration methods were compared, including global intercalibration method, i.e., global linear regression, and per-pixel-based intercalibration methods, i.e., per-pixel linear regression joint global linear regression, per-pixel linear regression joint inverse distance interpolation, per-pixel linear regression joint nearest neighbor interpolation, and global per-pixel linear regression. Furthermore, the effects of diverse environmental variables (i.e., land cover and its heterogeneity, climate types, water body fraction, terrain and its complexity, soil texture, and vegetation coverage) on FY-3D calibration accuracy were fully investigated. The results indicate that all five approaches can reduce the bias between FY-3D and AMSR2 TB, and the root-mean-square difference (RMSD) also reduces accordingly. Among them, the global per-pixel linear regression method performs the best with the lowest averaged RMSD of 2.93 K (at ascending overpass) and 2.34 K (at descending overpass), followed by the per-pixel linear regression joint inverse distance interpolation. The global linear regression method performs the worst with the largest RMSD of 4.69 and 3.82 K at ascending and descending overpass, respectively. The RMSD is relatively larger in temperate and polar climate zones, as well as in grasslands and croplands than in other climate and land cover types. The calibration errors generally decrease as the altitude increases, while they increase with the increase in land cover heterogeneity. The water body fraction exerts the greatest impact on the calibration accuracy, and the RMSD reaches 3 K when the water body fraction is greater than 15%. Soil texture, terrain complexity, and vegetation coverage generally have little influence on the calibration accuracy. These findings can provide a good reference for the intercalibration of satellites with similar configuration to generate long-term climate data records.

This paper estimated Arctic monthly sea ice thickness (SIT) using the ICESat-2 gridded monthly sea ice freeboard product (ATL20) with the assumption of hydrostatic equilibrium. Different snow and ice parameters are applied to illustrate the effects of auxiliary data on the SIT retrievals. Snow depth data are from three sources including climatology observation, microwave remote sensing and reanalysis data, while six snow/ice density combination schemes were adopted. The results show the averaged monthly sea ice freeboard from ICESat-2 ATL20 product well captures the seasonal variation of sea ice freeboard and sea ice growth in Arctic winter, which can be further used to estimate SIT. The difference of monthly SIT from different snow and ice density schemes is smaller than that caused by snow depth. Overall, the largest difference caused by snow depth in mean SIT is ~0.50 m, while that caused by the snow and ice density is ~0. 15 m. The best parameter scheme of SIT inversion using ICESat-2 needs to be further investigated using field measurements in the future.

As an important element of the earth's surface, snow cover plays an important role in the global terrestrial ecosystem, climate change, water cycle and energy cycle. Snow depth (SD) provides information on the spatial distribution of snow cover and material energy information. It is also used to study the climatic effects of snow cover, water balance in the basin, snowmelt runoff simulation, and monitoring and evaluation of snow disasters. Snow depth data has become an indispensable basic supporting data in multi-disciplinary research. However, the current snow depth data is relatively poor in completeness and consistency and cannot meet the needs of related scientific research and industry applications, which will bring great confusion to users. This study attempts to use machine learning methods to effectively integrate snow depth data products from multiple sources to obtain a snow depth data set with high consistency in China. This paper chooses the passive microwave remote sensing data (WESTDC), ground-based data (Canadian Meteorological Centre, CMC) and Land surface models (Global Land Data Assimilation System, GLDAS; the NASA Modem-Era Retrospective Analysis for Research and Applications MERRA2; the European Centre for Medium-Range Weather Forecasts Interim Reanalysis, ERA-Interim) as the main SD data source of random forest model, and considering theinfluencing factors (e.g., land cover, snow class, forest cover fraction, surface roughness). The results show that the random forest fusion model method can effectively gather the advantages of each data source, improve the accuracy of snow depth estimation, and reduce the inconsistency between snow depth data from multiple sources. The correlation coefficient between the fused snow depth dataset and the observed snow depth can reach 0.87, and the root mean square error is 5.1 cm. Therefore, the multi-source snow depth fusion using random forest model can improve the accuracy of snow depth estimation.

This paper assessed the accuracy of four passive microwave (PM) sea ice concentration (SIC) products in polar regions by using twelve scenes MODIS images under clear-sky conditions. The SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2). An adapted optimal threshold method (i.e., the Otsu algorithm) was adopted to automatically classify the MODIS images into sea ice and water which were then aggregated to compare with the PM SIC. The results show that the averaged bias of PM SIC (PM SIC minus MODIS) is less than 6% in Arctic and ranges from -8% to 1 % in Antarctic, and the averaged root mean square error (RMSE) is less than 16% in the whole polar regions. Overall, the SSMIS/ASI product has better performance in Arctic, while the FY3/NT2 has lower bias and RMSE in Antarctic. Meanwhile, it is observed that the error metrics of PM SIC vary noticeably when compared to different MODIS images which may be caused by the diverse surface characteristics of different sea ice types.

This paper evaluated the consistency of four microwave remotely sensed sea ice concentration (SIC) products with respect to a reanalysis SIC dataset in polar regions during the period of 2015-2017. The remotely sensed SIC products include the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), the Chinese Feng Yun-3C with NT2 (FY3C/NT2), the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), and the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT). The OISSTV2 (NOAA Optimum Interpolation 1/4 Degree Daily Temperature Analysis Version 2) dataset was adopted as a reference to compare and evaluate the performance of four satellite-based SIC products. The results show that remotely sensed SIC values are generally in good consistency with OISSTV2. Meanwhile, it is observed that different products have different bias in polar regions. Overall, the SSMIS/ASI product has better performance during the whole period, demonstrating that ASI algorithm may be more potential in SIC estimation. Our results also illustrate the spatial and temporal distribution characteristic of discrepancy between microwave remotely sensed SIC products and reanalysis dataset for the whole Arctic and Antarctic regions. The large difference for all the four SIC products mostly occurs in summer and marginal ice zone, indicating a great deal of uncertainty of satellite SIC products in this period and areas. The results will be useful to find possible errors in the satellite SIC products for further algorithm improvement.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LA!) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the activeonly model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for σHHo, σVVo, TBH, and TBV, respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.

Accurate monitoring of the global snow cover is important in understanding the impact of the climate on snow cover. Google Earth Engine (GEE) is a cloud computing platform dedicated to satellite imagery and other Earth observation data. Taking the Landsat TM data as true data, this paper evaluated and analyzed MODIS snow cover products by GEE in snow seasons. Using GEE JavaScript program, we obtain that error rate of MODIS snow cover product, the missing rate during the snow season with finally 88.94% of overall average accuracy in the test sites. The results showed that the GEE platform can be used to assess accurately the snow products with high efficiency.

The first evaluation of GaoFen-3 SAR radiation characteristics and its potential for quantitative parameter estimation in land surface is presented in this letter. Based on two triangle corner reflectors, five impulse response property parameters are calculated to assess image quality of GaoFen-3 SAR, and results show that the peak sidelobe ratio is superior to system specification and the integrated sidelobe ratio cannot be assessed by misconduct in the field campaign mainly for strong background scattering (not low enough). Five distributed target parameters are extracted from three categories and compared with Radarsat-2 and Sentinel-1A quasi-synchronous images in two days. The results indicate good radiometric resolution (RR) of 3 dB and good equivalent number of looks closed to 1 for GaoFen-3 single-look complex product, which proves GaoFen-3 SAR image is at the same quality level with the other two C-band SAR images and its RR meets system design of 3.5 dB. Then, the backscatter coefficient of these three SAR images is compared at bare soil area after GaoFen-3's incidence angle being normalized to 43° by Oh2004 empirical model, and the results reveal the problem of several decibels lower for GaoFen-3 in absolute radiometric calibration. Finally, the capability of surface parameter estimation is justified by statistical relation of GaoFen-3 co-polarization backscatter coefficient and bare soil moisture content, indicating its good potential for quantitative applications in land after solving the problem in calibration.

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m3· m-3, respectively, which generally meet their mission requirement of 0.04 m3· m-3. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m3· m-3. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m3· m-3though it underestimates in situ measurements with a bias of 0.059 m3· m-3, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles.

For supporting the applications of GF-3 and GEOSAR satellite projects, which have been approved by Chinese government, a multi surface parameter inversion algorithm using alternative Radarsat-2 polarimetric images is tested and improved in this study. Result shows that, though the algorithm can retrieve vegetation height and water content with relatively good accuracies, soil moisture and surface roughness inversed results are poor when the above two vegetation parameters retrieved values as inputs to inverse two soil parameters. Analyzing the reason, scattering model could amplify the errors caused by the two retrieved vegetation parameters, which are inputted as “true value” in the model. The conclusion of this study is that, surface parameters can not be inversed step by step as the idea of engineering, scientists should focus on certain parameter(s), and take other parameters as disturbance factors to eliminate or suppress.

SAR knowledge base system is a comprehensive application platform for typical objects interpretation, which includes the database of microwave scattering models, backscattering measurement data and SAR image. The paper introduces the design of the whole system and gives a detail description for each database in the system. The comprehensive knowledge rules for SAR application is developed based on the system, and a case of SAR classification is shown using the expert rules. The result of the classification illustrates the advantages of SAR knowledge base system.

Over the permafrost region of the Qinghai-Tibet Plateau, serious decorrelation has restricted the inteferometric synthetic aperture radar (InSAR) techniques in monitoring ground deformation and thaw-melt hazards. Improved understanding, quantification and prediction of the coherence evolution with time are key prerequirements for choosing the optimal observation time and band in such interferometric applications. In this letter, considering the main processes of degradation and the freeze-thaw cycle, a temporal decorrelation model including both the long-term and seasonal coherence factors is proposed. Multitemporal L-band PALSAR, C-band ASAR, and X-band TerraSAR were collected to validate the model's applicability. Coherence matrices of several typical features were estimated for the parameter inversion. Root-mean-square errors ranging from 0.048 to 0.152 showed good fitting results between the model predictions and the estimated interferometric coherences. The temporal decorrelation characteristics of the permafrost region at the three frequencies were successfully revealed. This letter could also facilitate applications of InSAR techniques in similar areas in other parts of the world.

The Improved TD-RVoG model was combined with the classical forest inversion algorithm, three-stage inversion method, for the estimation of forest height by using single baseline spaceborne PolInSAR data. Previous research [6-7] has shown temporal decorrelation reduces the interferometric coherence and increases the deviation of the interferometric phase. The Improved TD-RVoG model considers the both sides of the temporal decorrelation and summarizes the complex temporal decorrelation as the ground and volume motion per day and introduces a temporal decorrelation function as an additional part for the RVoG model.

Validation of the remotely sensed soil moisture products is very important for data application and the refinement of the retrieval algorithms. In the study, we evaluated three newest soil moisture products that are the essential climate variable (ECV) soil moisture products (active-only, passive-only, and merged active-passive) which are the first multi-decadal satellite-based soil moisture data sets released by the European Space Agency recently. In-situ measurements used for validation are from three networks established in the Tibetan Plateau. The results show that all the ECV products can capture the soil moisture dynamics well. The active product performs better than the passive product when adding the Advanced Scatterometer (ASCAT) data set and is less sensitive to vegetation cover, but both of them overestimate the ground measurements and exhibit higher variation than in-situ data. Overall, the combined product outperforms other two products. It can preserve the relative dynamics of the active and passive products very well and the number of observations is also improved, indicating a positive effect by merging both active and passive data sets in the Tibetan Plateau.

A method for soil moisture and surface temperature estimation in the Tibetan Plateau (TP) using spaceborne radiometer observations was presented. Based on the physical basis that the 36.5-GHz (Ka-band) vertical brightness temperature is highly sensitive to the topsoil temperature, a new surface temperature model was developed using all ground measurements available from three networks named CAMP/Tibet, Maqu, and Naqu, established in the TP, which can significantly improve the accuracy of surface temperature derived from the land parameter retrieval model (LPRM). Then, the new surface temperature model, which was calibrated with in situ data, was integrated into the soil moisture retrieval algorithm proposed in this letter using Advanced Microwave Scanning Radiometer (AMSR-E) observations. The algorithm combines the vegetation optical depth and roughness into an integrated factor to avoid making unreliable assumptions and using auxiliary data to get these two parameters. Finally, the algorithm was validated by ground measurements from the dense Naqu network and was compared with NASA AMSR-E and Soil Moisture and Ocean Salinity (SMOS) official algorithms. The results show that the proposed algorithm can provide much more accurate soil moisture retrievals than the other two satellite algorithms in the Naqu network region. The algorithm can be applied to the areas with spare vegetation but may not be very suitable for densely vegetated surfaces.

Compared with conventional single-polarization synthetic aperture radar (SAR), quad-polarimetric SAR observations provide more information and show potential for land cover classification. This paper evaluates the wheat identifying potential of quad-polarimetric SAR data. The fully polarimetric Radarsat-2 image for the Hebei province, China, is selected to evaluate the identification accuracy. Experimental results indicate that HH with HH-HV is the optimal linear polarization combination, with an accuracy of up to 82.3%. The Freeman-Durden decomposition can discriminate wheat more effectively, with an accuracy of about 86.8%. The combination of the Freeman-Durden decomposition and the parameter H is the best method for wheat identification, with an accuracy of up to 90.5%.

A new model for land surface temperature (LST) estimation in the Tibetan Plateau using passive microwave observations was presented. The new LST retrieval model was developed based on the strong linear relationship between the 36.5 GHz vertical polarized brightness temperature observations from Advanced Microwave Scanning Radiometer (AMSR-E) and the topsoil measurements from three networks named CAMP/Tibet, Maqu, and Naqu, established in the Tibetan Plateau. These networks were located in regions of different climatic conditions and vegetation cover. Then the new LST retrieval model was validated by ground measurements from the three networks and was also compared with the well-know land parameter retrieval model (LPRM). The results show that the new model can significantly improve the estimation accuracy of LST compared with LPRM in the Tibetan Plateau, and better results are achieved at the AMSR-E descending pass. The LPRM underestimates the LST at AMSR-E descending pass while it overestimates the LST at AMSR-E ascending pass in the Tibetan Plateau.

This letter presents a novel method for high-precision estimation of mountain glacier thickness change from the conventional interferometric synthetic aperture radar (InSAR) interferograms and multiaperture InSAR measurements. The method exploits the two components of displacement along the line of sight of radar beam and along track for deriving the glacier thickness change. To demonstrate the method, we estimate the glacier thickness change for the Dongkemadi Glacier in Tibet Plateau, China. The performance of this method is validated by field survey data. The results obtained with three InSAR pairs covering the Dongkemadi Glacier in the same seasons of three years show considerable spatial variability. The results in this study are, to our knowledge, the first ones with such a method, and they demonstrate the feasibility of the approach to obtain and analyze the mountain glacier thickness change.

In this paper, a new way of integration of SAR geometric correction and radiometric rectification named radar-coding method has been put forward for the improvement of accuracy and efficiency in SAR image classification in glacierized district. A radar-coding look up table has been constructed based on geometric correction, which can be used for generating the simulated SAR. Then radiometric rectification can be completed based on simulated SAR. The study in Dongkemadi Glacier Basin located in the middle of the Thang-la Mountain Range of Tibetan Plateau of China shows that: (1)This new method has advantages of eliminate the geometric and radiometric distortions in glacierized district; (2)Accuracy of classification is at least 80% due to the confusion matrix.

Snow water equivalent have been retrieved from passive microwave instruments for many decades. Current SWE products from NSIDC and GlobSnow are achievements of traditional spectral difference method and newly developed assimilation algorithm combining model estimates with ground-based weather station data. In this paper, validation analysis show that GlobSnow SWE possess less RSM errors in each month and higher stability through the whole winter for all Sturm's snow classes. In term of snow changes, both NSIDC and GlobSnow products mainly shows similar temporal trend. Even although the total snow amount in northern hemisphere decreased from 1978 to 2010, snow mass is increasing in higher latitude and decreasing in lower latitude in the first 9 years, appearing kind of antiphase oscillation between higher and lower latitude; and in the next 15 years, 1988-2002, this phenomenon still exists but gradually disappears. Until resent 8 years, 2003-2010, all kinds of snow in north hemisphere decreased with relatively drastic tendency.

In Qinghai-Tibet Plateau, permafrost is usually full of ice underground and sensitive to the temperature. So due to the global warming and seasonal change of temperature, great changes of surface in physical characteristics would occur in the temporal dimension. This could lead to the decorrelation between two SAR images, and hinder the application of Interferometric Synthetic Aperture Radar (InSAR) technique in this area. Additionally, distributed scatterers (DS) spread all over the tundra. In this paper, we first introduce DS analytical method, and use 45 ENVISAT-ASAR images to analyze the coherence of several typical ground features. The result shows that DS analytical method could improve the accuracy of coherence estimation, mainly railway and road in our study area.

This paper proposes an improved subspace method (ISM) for fully polarimetric synthetic aperture radar (PolSAR) image classification, which is a combination of the leaning subspace method (LSM), the averaged learning subspace method (ALSM), and the multiple similarity method (MSM). The fully polarimetric Radarsat-2 image for the Yellow River Delta of northern Shandong Province is selected to evaluate the recognition accuracy. The supervised Wishart method is also performed for comparison. Experimental results validate the proposed method yielded better classification results. Therefore, the ISM is a feasible method for fully polarimetric SAR image classification.

A physically-based algorithm for surface soil moisture retrieval in the Tibetan Plateau using passive microwave remote sensing was presented. The algorithm is based on a radiative transfer model and the assumption that the vegetation optical depth is polarization independent. It combines the effects of vegetation and roughness as a single parameter and uses the microwave polarization difference index (MPDI) to eliminate the effects of surface temperature and obtain soil moisture through a nonlinear iterative procedure. The advantage of this algorithm is that it needs only one frequency brightness temperature observations, and requires no field observations of roughness, soil moisture or vegetation data sets during the whole retrieval process. Finally, the algorithm was tested with the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) and compared with NASA official algorithm using in situ soil moisture from 20 stations in the Tibetan Plateau. The results show that the soil moisture retrieved by the algorithm is more consistent with ground measurements than the NASA soil moisture products.

A technique combined CFAR and EF detection method is presented to improve the power transmission tower detection in synthetic aperture radar (SAR) image. CFAR can detect power transmission tower as the point like targets used the intensity information. EF features is sensitive to target geometric feature, which can consider the shape of power transmission tower. The paper takes advantage of intensity information and geometric feature of targets to detect the power transmission tower, which has the regular shape. The test results using real SAR images show good performance in multitarget situation and heterogeneous environment.

Mountain glaciers and ice caps are often excellent indicators of regional climate change. The surface of a glacier can be roughly divided into accumulation and ablation areas, separated by the equilibrium line where accumulation is exactly equal to ablation. However, it is not easy to be detected on SAR image in glacial areas. The boundary between wet snow and ice is defined as the transient snow line (TSL). At the end of the ablation period, the transient snow line is often interpreted as an approximation of the equilibrium line, which is of special interest for the monitoring of climatic variations and glacier mass balance studies. In this paper, three methods to calculate the snow line altitude in late summer are introduced, and their accuracy and characteristics are analyzed.

Forest parameters can be estimated using the single baseline polarimetric synthetic aperture radar interferometry (PolInSAR) data using the Random Volume over ground (RVoG) model. For a long-term temporal baseline, temporal decorrelation was present in all the polarimetric channels for the entire range of the synthetic aperture radar (SAR) data due to the complex changes process of the two temporal acquisitions. To overcome this problem, the Improved Temporal Decorrelation Random Volume over Ground (Improved TD-RVoG) model, based on the RVoG model is developed to minimise the effect of temporal decorrelation. This model introduces a new temporal decorrelation function requiring only one more additional parameter than the RVoG model. The new model is appropriate for use with the `Three stage inversion' procedure. Finally, the new model is validated by using single-baseline Advanced Land Observing Satellite (ALOS) SAR data.

Snow water equivalent (SWE) is a critical parameter for climatological and hydrological studies over northern high-latitude areas. Based on the long time observation and monitoring of SWE, we can discover the climate changes tendency. Most conventional SWE retrieval algorithms, well known as NASA algorithms, depend on the difference between brightness temperatures near 19 (or 18) and 37 GHz. Until now, there are as many as 6 PMRs' data to derive hemisphere-scale snowpack since 1978. In this paper, we collected the microwave radiometry's SWE products in the Equal-Area Scalable Earth Grid (EASE-GRID) to do time series analysis in the northern hemisphere during the period 1978–2010, including the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I) and The Advanced Microwave Scanning Radiometer-EOS (AMSRE). After adjusting the systematical difference of these three snow products in winter, the snow cover extent and SWE changes for 1978–2010 are available. We found in the past 33 years, both SWE/snow mass and snow cover decreased. Besides, in order to gain the information of spatial variations of snow cover extent and SWE in the northern hemisphere, several interesting area such as China, North America, Eurasia, Siberia, and Arctic Circle are illustrated.

The paper develops a new technique enhancing synthetic aperture radar (SAR) resolution as well as suppressing sidelobes based on adaptive weighting technique. Spatially Variant Apodization (SVA) is a nonlinear sidelobe reduction method without lose the resolution of mainlobe. The paper's method applies 2D SVA on the SAR image. And then it is detailed analyzed about the effect of extrapolated signal bandwidth after the processing. An inverse weight function is used to equalize the spectrum to obtain the extrapolated signal bandwidth. A modified noninteger Nyquist SVA formulation is used to suppress sidelobes after extrapolation. Examples of 1D case and 2D case demonstrate enhanced image resolution with sidelobe reduction.

The movement parameter of glacier is the very important factor of the glacier changes. It is an indicator of the local effects of global climate change. Meanwhile, changes in climate are affecting temperature and precipitation at the earth's surface and therefore the accumulation and melt along the surface of glaciers with a direct effect on the mass balance. Monitoring of the glacier movement changes is important in climate change studies, with accelerated or decreasing motion indicating an alteration of the equilibrium of glacier masses. In this paper, we used the two combined methods (InSAR and Offsettracking) for estimating the motion of Muztag Ata glaciers from ALOS SAR data. The two methods yield similar results and agree with each other. Eleven glaciers were identified in Muztag Ata and gave a detailed analysis of the different types, different sizes and different orientations glaciers' motion features and changes in Muztag Ata according to the motion results of the 2008 and 2010.

Synthetic aperture radar (SAR) can be used to distinguish areas of contrasting backscatter on glaciers and relate these areas to glacier facies. In the ablation season, there are two typical facies on temperate mountain glaciers: wet snow and ice. The boundary of wet snow and ice is defined as the transient snow line (TSL), which is an important concept in glaciology. In this letter, a new TSL detection method is proposed, in which the polarimetric SAR image is classified into three classes (wet snow, ice, and others) using support vector machines, and the boundary between wet snow and ice on the classification map is considered the TSL. The method is efficient and accurate and enables large-scale TSL observation. In our work, the TSL is extracted and analyzed on the long-observed Dongkemadi glacier using the proposed method. In addition, the method is applied on other neighboring glaciers to estimate their snow line altitude. On a regional scale, the TSL of the glacier group shows interesting phenomenon in the study area: The TSL altitude correlates closely with the orientation of the glaciers.

As we know, SRTM (Shuttle Radar Topography Mission) DEM has been widely used to evaluate the multi-decadal elevation changes of mountain glaciers. Recently, a new global elevation dataset known as GDEM, based on the ASTER satellite imagery has also been released. In this paper various kinds of data on SDG(Small Dongkemadi Glacier) were collected to evaluate the potential of GDEM used in this field, including the GDEM, SRTM DEM, topographic map for 1969 and Landsat ETM+ image for 2000. The elevation change from GPS survey data (2007) and DEM(1969) on SDG is used to validate the results here. The comparison analysis shows the thickness change detected by SRTM DEM is consistent with the GPS measurements, however, the result from ASTER GDEM is far away from the field work on SDG, although the overall distribution is right. The experiment results will cause our serious attentions to the usage of GDEM in glacial field.

The paper provides an efficient extrapolation algorithm to enhance resolution as well as reduce sidelobes, which is based on ASR. The processing of algorithm is simple to operate. Simulation experiments show the validity of the algorithm. Comparing to the Fourier method, the proposed algorithm obtains better results.

Synthetic aperture radar interferometry (InSAR) has been proven to provide very useful information for the glacier movement. Although the main potential of InSAR for glacier movement estimation has been shown in several case studies, its successful application is often limited by decorrelation. Due to the back scattering characteristic change of the individual scatters on glacier surface, the coherence of glacier surface will be entirely missing for the long temporal baseline (such as 35 days or more). The corner reflectors, duo to their design, provide a very clear and stable target response (both amplitude and phase) to the radar at any acquisition time. They don't suffer from decorrelation effects of conventional DInSAR [1-3]. In this paper, we first attempt to use the differential InSAR technique with corner reflectors (CR-DInSAR) to monitor and detect the motion of mountain glacier in Qinghai-Tibet Plateau. This method will be more effective than the conventional D-InSAR, as well as nature persistent scatters method.

The glacier volume changes on Qinghai-Tibetan Plateau are believed to currently provide significant responses to global climate change and strongly influence human welfare in this arid or semi-arid region, where water supplies are predominantly from glacier melt. So the elevation changes of the mountain glaciers play an important role in compute the volume change. Previous researches mainly used two temporal digital elevation models (DEM) to monitor the glacier elevation changes. However, these methods may introduce more errors caused by the DEMs. This paper presents a novel method to monitor the thickness changes of mountain glacier based on the deformation extracted by D-InSAR of the glacier's surface. Using this method, we can monitor the thickness changes in cm-level accuracy. In order to demonstrate this method a practical example, the monitoring thickness changes of the Kangwure Glacier in the middle part of the Himalayas of China, is given.

In the field of agriculture, robust harvests and crop yields are challenged by the dynamic nature of soil and crop conditions that fluctuate throughout the growing season. Satellite SAR imagery is an efficient method for mapping crop and underground soil characteristics over large spatial areas and tracking temporal changes in soil and crop conditions. Compared with conventional one (ERS-1/2,Radarsat-1) or two-polarization (Envisat /ASAR) space-borne SAR sensors, RADARSAT-2 powerful new features in terms of polarization can benefit the agricultural sector. In this paper, soil moisture and LAI from ground measurements is compared with power information of two Radarsat-2 polarimetric images, result shows soil moisture does has some influence when vegetation is short, but LAI doesn't influence backscattering at any stage of this paper.

Image registration is the process of overlaying two or more images of the same scene taken at different times, from different view points, and /or by different sensors. A novel feature-based multi-sensor image registration system is developed. The system consists of two new points: first, edge features are extracted from images, and the features are dilated to suppress some certain kinds of noise arising from groves; second, the preprocessed features are matched using the improved shape context. The shape context has been found to be robust in hand written digit and object recognition, and now it is introduced into remote sensing image matching after some adjustments. The developed system is successfully applied to register airborne optical and C-band SAR images in our experiments, and the results demonstrate its robustness and accuracy.

Electromagnetic models are fundamental for understanding of the interaction processing between electromagnetic waves and matter and further to retrieve geophysical parameters from observation data. The MEMLS and DMRT are two most widely used models and these corresponding multilayer versions include the effects of stratification due to ice-layer and depth hoar. In order to extend the results Tedesco has obtained, following the Durand's work, sufficiently comparisons of multilayer model are made in this paper to definitively determine which the better approach is meaningful. At First, we compare the accuracy of MEMLS with multilayer QCA/DMRT using the same multilayer snowpit inputs. Secondly, in order to the stability of model, we test the sensitivity of models.

In this paper we measured the long-term thickness change of the Dongkemadi Glacier(DG) on Tanggula Mountain, Qinghai-Tibetan Plateau, using the Shuttle Radar Topography Mission (SRTM) C-band data(2000) and a digital elevation model(DEM) generated from topographic map(1969). First we check the accuracy of SRTM DEM by comparison at 16 random independent points in surrounding non-glacier area below 5500m a.s.l., then we focus on an analysis of the glacier's surface thickness change features and a validation of the results using GPS survey data (2007) and DEM(1969) in Xiao Dongkemadi Glacier(XDG). The result shows XDG decreased by an average of 6.15m, or 0.20 m a-1 between 1969 and 2000. We estimate the error of annual thickness change rate to be on the order of 5% compared to the result of field measurement while Da Dongkemadi Glacier(DDG) decreased by an average of 20.74m or 0.67m a-1(1969-2000).

One of the driving forces of the development of SAR image formation has been to obtain better and better image resolution. Conventional radar imaging methods based on Fourier transform provide good resolution as long as the backscattered data is available over a large bandwidth and a sufficient aspect region. The paper proposes a 2D Unitary ESPRIT superresolution SAR imaging method exploiting that the SAR image in phase history domain is a band-pass function with a main frequency support domain. Thus, the problem of superresolution SAR imaging is transformed to solve sinusoid harmonic estimation, which can be solved by 2D Unitary ESPRIT. From the experiments using simulation and measured data, we can see better resolution obtained by the method of the paper than the FFT method.

Monitoring temperate glacier activity has become more and more necessary for economical and security reasons and as an indicator of the local effects of global climate. The most studied variable in ice dynamics in the literature is ice velocity. From remotely sensed images, mainly two types of methods have been used for the estimation of glacier flow velocities: feature tracking and differential interferometry (DInSAR). In this paper velocities of the Keqikaer glacier are acquired from ALOS (Advanced Land Observing Satellite) optical and SAR data respectively with feature tracking. We show that different window size in correlation calculation of feature tracking leads to different flow field. We also developed a new method to determine the best window size, and the method is testified by the two kinds of data.

The glacier is important factor in climatological and hydrological investigations, especially in the western China. Glacier changes are among the clearest signals of on-going warming trends existing in nature. In view of environmental changes, combined with the high thermal sensitivity of earth's mountain glaciers, especially in Qinghai-Tibetan Plateau, is of growing interest. SAR systems have an important ability to observe the earth's surface, independent of cloud conditions. Particularly, the SAR interferometry provides a useful tool for monitoring the velocity of glacier movement. For accurate measurement of glacier movement with InSAR data, the suitable method of retrieving velocity of glacier motion need be considered for the different type of glaciers in case of the difference of the characteristics of surface and movement velocity. In this paper, we use the SAR interferometry to derive the movement of several types of glaciers and analyze the characteristics of different glaciers type, such as continental glacier, sub-continental glacier and maritime glacier, and demonstrate the method and results for the glacier motion in the Qinghai-Tibetan plateau.

HJ-1C will be launched at the end of 2008, it is a component of HJ satellites which are developed in China. HJ-1C SAR has a frequency of S-band (3.2 GHz), VV single polarization, and incident angle range from 25deg-47deg. Soil moisture retrieval using L- and C-band radar has been widely studied, but little attention was paid to S-band data. For the application of HJ-1C SAR, in this paper, the available data of S-band radar data by PALS (Passive and Active L- and S-band sensor) from SGP99 and SMEX02 experiments was used to study the potential of soil moisture change retrieval using S-band single polarization radar.

For climatological and hydrological investigations, the areas covered by glacier and their spatial variability are important parameters, particularly in Qinghai-Tibetan Plateau. A interferometric SAR technique not only can produce a high-resolution digital elevation models but also can identify the surface object with coherence coefficients. This property of SAR polarimetry is particularly useful in classification. In this paper we analyze to demonstrate the method and result for the glacier identification integrated intensity of backscattering from Envisat/ASAR images, coherence coefficients of repeat pass interferometry from ASAR and ALOS/PalSAR, and full polarimetric SAR from PalSAR.

Gonradsen et al. developed a change detection method with multilook fully polarimetric Synthetic Aperture Radar (SAR) data. The method is based on complex Wishart distribution supposition limits the applicable scope of test statistic, especially when only polarimetric diversity (the texture and speckle statistics depend on polarization) and not radiometric diversity. On the other hand, the polarization signature has the capability of representation of the polarimetric diversity. A new test statistic for equality of two polarization signature is proposed in this paper. Without statistical distribution hypothesis, it can give the associated asymptotic probability measure as the Conradsen's method just by non-parameter correlation analysis. Some experiments have been done in this paper with ALOS-PALSAR data by two different algorithms. It has been shown that the new method is effective.

The surface displacement by seasonally freezing bulge and thawing subsidence are main hazards for engineering construction in permafrost regions, especially for the Qinghai-Tibet railway. One of the main problems is how to monitor the frozen ground's displacement in the process of construction and protection of the Qinghai-Tibetan railway. The technology of PS (Permanent Scatters) has been successfully use to detecting the long time subsidence at urban area. For detecting the subsidence of the frozen earth on Qinghai-Tibet Plateau, this paper extended the capability of the technology of Permanent Scatterers to investigate deformation phenomena in vegetated area. The paper analyzes interferometric phase model, and presents an improved PS-InSAR algorithms for separating different components in interferometric phase. The proposed technique is implemented by using ENVISAT ASAR images to detect the deformation over permafrost region of Qinghai-Tibet Plateau. The results are in concordance with results provided by a traditional ground levelling, which encourages future development for use permanent scatterers method to analyze deformation of the frozen earth on Qinghai-Tibet Plateau.

The movement parameter of glacier is the very important factor of the glacier changes. And now, mainly two types of methods have been used for the estimation of glacier movement: differential synthetic aperture radar interferometry (D-InSAR) and Image matching (mainly used for optical data). Although the main potential of D-InSAR for glacier movement estimation has been shown in several case studies, its successful application is often limited by decorrelation. Due to the backscattering characteristic change of the individual scatters on glacier surface, the coherence of glacier surface will be entirely missing for the long temporal baseline (such as 35 days or more). In this paper, we present a novel method to monitor the movement of the whole glacier. According to glacier dynamics theory the glacier's movement is mainly affected by two factors, its gravity and pressures of the firn. The direct consequence of the whole glacier movement is that caused the deformation of the earth surface around the glacier. So we use the motion result extracted by D-InSAR of the earth's surface near the terminus of glacier to deduce the movement of the whole glacier.

The ERS-1/2 Wind Scatterometer (WSC) operates at a frequency of 5.3 GHz (C-Band) with vertically like-polarised antennas both in transmission and reception (W-polarization). It continuously provides global measurement of radar cross-section from August 1991 to December 2000. WSC has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. The method presented in this paper combines a theoretical model and an empirical algorithm developed by previous authors to estimate land-surface parameters. The method is implemented using 15 days WSC data in China, a good agreement is observed between retrieved fractional vegetation and NDVI, and the temporal-spatial distribution of soil moisture is reasonable, too.

For more than two decades, SAR images have been evaluated as a means to ocean wave spectrum in oceanographic remote sensing. To fulfill the role, Hasselmann and Hasselmann's SAR imaging theory of ocean surface and Lyzenga and Monaldo's method to evaluate ocean spectrum from SAR image are jointly the attractive ways. In this present paper, a two-dimensional ARMA (auto regressive moving average) model is addressed to extract ocean spectrum information from ENVISAT-ASAR image of South-China Seas. The relation between the parameters of a 2-D ARMA model and ENVISAT-ASAR image is investigated and a new algorithm is proposed based on this relation. Example is given with using ENVISAT-ASAR image. As a result of this example, it is shown that the ARMA parameters and the corresponding power spectrum estimated by the proposed model are practical way to gain the ocean spectrum.

From the frequency characteristic of L and C band, in this paper, a new phase based method of vegetation height estimation using dual frequency SIR-C polarimetric SAR interferometry data is presented. Compared the preliminary result with field measurement data, it indicates that the inversion algorithm can obtain the height of vegetation with an acceptable accuracy

High resolution data of the ASAR sensor aboard ESA's Envisat satellite offers the opportunity for monitoring surface soil moisture at high spatial resolution with multi-polarization and multi-incidence capabilities. Using Integral Equation Model (IEM), we simulated the backscattering coefficients of C band SAR backscatter at different incidence angles and the alternating polarization mode, and analyzed the sensitivity to soil moisture. The algorithm of inferring surface soil moisture from radar backscatter is developed according to semi-empirical model from the results of simulation and sensitivity analysis. The soil moisture map are retrieved from ASAR backscatter measurements in Hetian, Xinjinag (37/spl deg/15'N, 79/spl deg/48'E) area, where are the arid and semi-arid area of west China. Accuracy in the retrieval of the surface moisture content from ASAR is compared to the spatially distributed ground truth. The results demonstrate the effects of soil moisture monitoring using multi-polarization multi-incidence ASAR data.

The effects of vegetation are very important in the process of retrieving soil moisture with space borne radiometer. We develop an experimental model, at C-band (about 6.6 GHz), to estimate the effects of vegetation. The developed model is more efficient than the original model and the estimate of the vegetation opacity with the developed model is approximate to the original model. Sets of AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) data, during the SMEX02 period in the watershed region, are used to retrieve soil dielectric constant and the land surface temperature only with the two channels (V- and H-polarization) at 6.92 GHz. The retrieved soil dielectric constant k and land surface temperature Te with the developed model are more approximate to the k and Te measured at WC13/14, and the process takes about 80% CPU-time cost with the developed model than that with the original model. The retrieved results without considering the effects of vegetation vary too significant during the time-sequence, and are not approximate to the synchronic measured data at WC13/14.

The interferometric SAR technique has demonstrated its capability to measure ground deformation in wide range of application. The seasonal freeze/thaw transition will cause the deformation of ground surface, which is the main factor for engineering construction in permafrost region. China begins construction of Qinghai-Tibet railway in 2002. About 550 km of the railway will pass through permafrost areas. Thawing and temperature rising has a great influence on railway stability. In this study, five sciences SAR SLC images, including Tandem data, are used to produce multiple interferograms. Two typical methods for deformation detection are discussed. One is the InSAR measure with the high accurate DEM used to reduce the terrain effect, the other is differential InSAR. The data of deformation in experiment site are compared quantitatively with precise and accurate geodetic data derived from field measurement respectively. The analysis of precision and reliability of two methods showed that the InSAR measure with the high accurate DEM is the suitable technique for deformation monitoring using ERS-1/2 SAR data in Qinghai-Tibet Plateau. Additionally, an InSAR monitoring system of frozen ground deformation is proposed for the Qinghai-Tibet railway building.

Land vegetation plays a major role in the global climate change through the carbon cycle, and climate change in turn affects vegetation growth and its photosynthetic activity. In arid and semi-arid areas, sparse vegetation cover characterizes environments. Thus quantitative temporal series analysis of vegetation distribution and its variations enables observing annual trends, and helps to find out the reason for environment variability. There are serious environmental problems, such as deforestation, soil erosion, salinization, and desert encroachment in northwestern China, its natural conditions are very delicate. In this paper, we build a time series of vegetation change by the NDVI (normalized difference vegetation index) covered northwest regions over 19 years (1982-2000), and analyze the time serial NDVI variability using three methods, which are simple differencing, slope map of NDVI, slope map of biomass. The correlation analysis between NDVI with the temperature and precipitation in northwestern China was carried out. The results showed that there was significant positive correlation between NDVI and precipitation but that temperature was not strongly correlated with NDVI in northwest China.

Discrimination of blue-ice domain is very important for meteorolite searching and Antarctic inland ice sheet evolvement research. Application of SAR interferometric coherence to blue-ice domain target discrimination is studied over polar inland ice. For the region of Grove Mountains in East Antarctica, interferometric coherence analysis is carried out for a pair of tandem ERS- 1/2 SLC full scenes acquired in February 1996. The investigation indicates significant differences in coherence between blue-ice and other ice sheet features, providing possibilities for target discrimination. The generated blue-ice domain map is compared to a reference blue-ice domain map extracted from TM image acquired in 1990. The final results indicates good consistency.

Digital elevation information of Antarctic in-land ice sheet is very important in research of ice flow. ASTER (Advanced Spaceborne Thermal Emission and Reflectance Radiometer) is an imaging instrument on board Terra-the first Earth Observing System (EOS) satellite. The ASTER Earth Observation Satellite offers nearly simultaneous capture of stereo images, minimizing temporal changes and sensor modeling errors. Band 3 of the VNIR sensor includes two channels, a nadir looking scene and a backward looking scene. This provides stereo coverage from which a DEM can be automatically extracted. For the research region of Antarctic Grove Mountains, a pair of ASTER L1A stereo scene product acquired in December 27/sup th/ 2001 is used to extract digital elevation model. Accuracy of the DEM is highly dependent on the source of the ground control points used. Several field ground control points in this region are selected. The final DEM result is compared to DEM surveyed in 2000 using GPS by Chinese surveyors, which indicates good accuracy and powerful usability of ASTER DEM construction in tough Antarctic in-land environment.

Most application of polarimetric SAR imagery require more efficient processing technique which achieves two fundamental goals. The first one is to detect and classify the constituent remotely sensed scenes from pixels to pixels in the imagery. The second one is to reduce the data dimensions without loss of critical information because the fully polarimetric SAR data can be processed just as multi-dimensional images, which could like multi-bands spectrometer images. It is the increased use of spatial data in remotely sensed images that we can get the valuable extraction. But there is composing complexity among the polarimetric imagery texture comparing some hyperspectral images. Some strong backscatters may exist under arid surfaces and contaminate some adjacent pixels. The contamination of imagery decreases the precision of classification. To determine the pixel distance in spatial coordinate is a key to debar some difficulty in front of polarimetric SAR data. The approach to link the nature and cause of spatial variation in image is to use the variograms and subsequent Kringing theory. The data results from the fully polarimetric images prove that the variograms are the tool used to link models of radar backscattering scenes. Some directional variograms aim to indicate the backscattering anisotropy, the height of the sills of the variograms are related to the kinds of the remotely sensed objects and the range of the influence is related to the acting distance on pixels of scenes.

This paper develops a classification way using polarimetric synthetic aperture radar (SAR) data. Polarization of radar electromagnetic wave is a very important factor of backscattering theory and geosciences applications. However, polarimetric SAR images are still difficult to interpret and therefore there is a need to improve the classifier that can make use of the polarimetric information. Under the certain circumstance the present paper attempts to classify remotely sensed scenes by all the complete polarization response parameters, which are presented as data dimensions for classification arithmetic. The test site is located in Hetian of Xinjiang, China. SIR-C data were acquired in the test site in 1994. Fully polarimetric SAR data can be processed as multi-dimensions images. But the polarimetric information is related greatly with variable targets. Firstly, we decompose the backscattering matrix and get polarimetric ratios, polarimetric degree, and polarimetric entropy. These polarimetric parameters are considered as data dimensions in which elements change in terms of probability functions with variable targets. Training samples were then generated from the outputs of the unsupervised classification of K-means, to be used in subsequent supervised classifications of two frequencies (C- and L-band for SIR-C data) and various polarization combinations. The estimation of the classifier tallies with the local municipal statistics. The result shows that classification precision can be improved finely with the polarimetric technique.

An inversion scheme of vegetation parameters based on a simulated annealing algorithm using polarimetric SAR interferometry (Pol-InSAR) data is presented. Comparing the result of inversion with field measurement, it indicates that the inversion algorithm can obtain the height of vegetation with good accuracy.

This paper analyzes the long sequence time series NDVI datasets and yields statistic results respectively, using simple differencing, slope map of biomass, and principle component analysis techniques. The correlation characteristics for the images of change acquired according to the algorithms mentioned above, implies that the majority pixels of the different land covers have experienced nearly the same changes in change direction and magnitude. The change of biomass of land-cover classifications between the 1980s and present are detected from the satellite data, and the land covers of the former 10 years (1981-1991) are in a better growth than the latter 10 years (1991-2001). Based on the discussion of the exotic factors affected the NDVI, like satellite shift or sensor degradation, the statistics on the slope images of biomass indicates that land cover deteriorated extensively in the past few years. The degradation of grassland or deforestation of forest regions confirmed such a fact that the status of the vegetation of the west part of China in the past 20 years (1981-2001) is suffering an extensive deterioration and only an improvement in part of the region.

ERS-l/ERS-2 synthetic aperture radar interferometry and Landsat TM was used to study the Maergaichaka fault where Manyi earthquake occured on Nov. 8, 1997 in Tibet, China. We derived an accurate digital elevation model(DEM) and the deformation interferogram of the Manyi earthquake using a tandem ERS-l/ERS-2 image pair and modeled the left- lateral slip of the fault in three dimension using half infinite elastic model. Detail geological and geomorphological offsets revalued using river valleys and structural markers on the Landsat ETM images at different scales are used to constrain the localization, total displacement at the Maergaichaka fault, Tibet China. The river network morphology associated with small rivers is offset by several meters to several kilometers along the maergaichaka fault. Our results indicates that the leftlateral slip of the fault has accommodated the shortening of the India-Eurasian lithospheric plates.

Some results of analysis conclude that the internal wave can affect the sea wind-generated wave crucially, so that the spectrum accounts for the interactions between the two kinds of sea water masses. These backscattering coefficients of the internal wave sea surface of the South China Sea are calculated using some existing models and the results are compared with the calculations of SIR-C/X-SAR data. Different results among the radar bands exist and are affected by sea surface roughness. The ocean wave spectrum is derived from the X-SAR image. Finally, we point out that the tidal current is the main factor that causes the internal waves.

Two basic microwave approaches are used to measure soil moisture, one is passive which is based on radiometry and the other is active and uses radar. Both approaches utilize the large contrast between the dielectric constant of dry soil and water. A total backscattering amount for a vegetated surface include volume, surface, and surface-volume interaction scattering terms. The backscattering model here is based on without surface-volume interaction scattering terms. In attempt to use active microwave remote sensors in estimation of soil moisture, two major problems, effects of surface roughness and vegetation cover, are faced. For a given sensor, we assume the roughness under the condition of no change during data acquisitions. The main problem for retrieval of surface dielectric properties is separate the volume scattering item from total backscattering. With the time-serial soil moisture map from L band passive microwave radiometry, the Electronically Scanned Thinned Array Radiometer (ESTAR) at Southern Great Plains 1997 (SGP97), we calculated the surface reflectivity with 800 m resolution. The volume scattering items at 800 m resolution can be derived using multi-temporal resample calibration Radarsat SAR and surface reflectivity data. Weighting the ratio of NDVI at different resolution from NOAA/AVHRR and TM, the surface reflectivity change with 50 m resolution can be estimated according to the total backscattering and volume scattering, then soil moisture change be mapped at 50 m resolution.

There are serious environmental problems, such as deforestation, soil erosion, salinization, and desert encroachment in the western China, its natural conditions is very delicate. But little is known about change in this region. The remote sensing data and digital image processing techniques provide consistent, reliable and quantifiable regional scale land-cover for environmental change research. The objective of this study is analyzing the environment change in a year in western China by using remote sensing data. In this paper, three typical experiment sites, which are a desert and oasis area, a loess plateau area and the source area of the Yellow River, are selected for detail change analyses. The change of NDVI, land-cover classifications between 1980s and present are detected from the satellite data. The datasets of remote sensing include Landsat-TM, ETM, SPOT and ASTER, all of these image are normalized to 30 m resolution and same coordination system for change analyses. The results show that industry development, cultivated land increment, soil erosion, forest and grassland decrement are the main reason of environmental deterioration.

A new phase unwrapping algorithm based on fringe line detection is presented. Dyadic wavelet theory is used to extract the multi-scale edge of an SAR interferogram. The phase characteristic of the interferogram are derived utilizing an active contour algorithm of the gradient vector flow.

From the SIR-C polarimetric L-band data of Hotan, China, on 9 and 10 October 1994, the DEM of polarimetric SAR interferometry (Pol-InSAR) and the conventional L-band HH-HH interferometric pair are extracted. The difference of DEM generation using Pol-InSAR and conventional InSAR is discussed in detail. Based on the result of comparison and analysis of two DEM, it is conclude that the accuracy of DEM generated from the polarimetric SAR Interferometry data is significantly improved, especially for the area covered by rich vegetation with enough high coherence, the error less than 10 m. Finally, error sources of DEM generated by polarimetric SAR interferometry are further analyzed.