Tropical forest plays a critical role in climate regulation, carbon storage, and biodiversity conservation. Their vulnerability to climate change and human disturbances necessitates accurate, scalable tools for monitoring and predicting forest growth. While recent deep learning models have improved in capturing nonlinear and lagged effects, current remote sensing applications often lack explicit modeling of factor-variant climatic delays and neighborhood interactions. Here we present a remote sensing–oriented deep learning framework named Lag-aware Convolutional Transformer with Error Feedback and Geographic Feature Fusion (LCTEG). LCTEG explicitly models the delayed effects of climatic factors through structured multi-scale convolution, incorporates neighborhood features to learn local conditions, and leverages error-guided attention to improve the stability and interpretability of multi-step predictions. Applied to global tropical forest at 0.1° spatial and 16-day temporal resolution, LCTEG achieved a test R² of 0.966 and reduced MAE, MSE, and RMSE by at least 65%, 86%, and 64% compared to baseline models. Lag analysis confirms delayed climate effects. Perturbation experiments identify precipitation as the strongest driver and show proximity to water bodies as a key spatial factor, highlighting the dominant role of water availability. Furthermore, future projections (2030-2034) show consistent LAI increases under all three SSP scenarios, but with smaller gains under high-emission pathways, suggesting that potential water stress may constrain vegetation growth. Overall, LCTEG serves as a robust, interpretable tool for tropical forest growth prediction and for advancing climate-resilient ecosystem and policy strategies.

This paper describes an automatic blue ice extent mapping technique in Grove Mountains, East Antarctica, where the key focused area of the Antarctic scientific expedition and many meteorites are distributed. In previous studies, the monitoring of blue ice extent was usually conducted using satellite images with coarse spatial resolution or offline operation, which lowered the description performance of blue ice in detail as well as the efficiency of continent-scale data production. Using Google Earth Engine, we first developed an automated framework that employs Normalized Difference Water Index (NDWI) indices in joint with the Otsu thresholding method to distinguish blue ice areas on Landsat imagery. Taking Grove Mountains as a study case, we further analyzed the annual variability of blue ice extent in the austral summer from 2007 to 2021. The results indicated that the blue ice extent can be automatically detected based on cloud platform and has no significant variability trend in Grove Mountains over the study period fluctuating with an overall average area of 440~600 km 2 . This framework is expected to be used for automated mapping on the whole of Antarctica and other concentrated blue ice areas (e.g., the East Antarctica ice sheet periphery and the Transantarctic Mountains surroundings) in a rapid and efficient manner, which provides guidance on the meteorite recovery and the support of the polar sustainable development goals.

Supraglacial lakes have a great impact on the mass balance and dynamics of Greenland ice sheet. While the current studies on these lakes mainly utilize optical observation data, the validity is poor, and it is impossible to conduct spatio-temporal analyses. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) data. By processing Sentinel-1 SAR dual-polarized imagery, a train dataset of 2664 image patches are formed. We use U-Net for segmentation and the associated Dice coefficient could reach higher than 95%. Besides, the terrain shadow is removed by DEM. The results are validated by supraglacial lake detection using Landsat 8. We will integrate more training data and optimize the feasibility of the model in future work.

Synthetic aperture radar tomography (TomoSAR) obtains three-dimensional reflectivity of scenes by extending the aperture principle into the elevation direction, and thus solves the layover problem. Because the scatters are sparse along elevation, compressed sensing methods are introduced into TomoSAR. However, conventional compressed sensing methods based on discretized grids suffer from off-grid effect. As a method with continuous dictionary, reweighted atomic norm minimization (RANM) can avoid this problem completely. In this paper, we propose an algorithm for SAR tomographic imaging based on reweighted atomic norm minimization to eliminate off-grid effect and locate scatters more accurately. The performance of the proposed method is verified by simulated data.

Above-ground biomass (AGB) is a key parameter for estimating the carbon resources of forests; however, it is challenging to retrieve the AGB in tropical forests. Based on the modeling of the scattering process in an infinite-length lossy dielectric cylinder, in this work, the copolar phase difference (PPD),$\Delta \phi _{\mathrm {tr}}$, of the double-bounce scattering component from tree trunks was investigated. The results showed that there is an almost monotonic decrease in PPD as the trunk radius increases, meaning that the AGB can also be related to the PPD through the allometric equation. The data analyzed in this letter were obtained from the fully polarimetric P-band airborne data set acquired over Franch Guiana in 2009 as part of the European Space Agency campaign, TropiSAR. The PPD of the double-bounce scattering component was obtained using the Freeman–Durden decomposition algorithm. As expected, it was found that AGB was well correlated with the PPD, giving an$R^{2}$value of 0.7576.

Suffering from speckle noise and complex scattering phenomena, classification results of SAR images are usually noisy and shattered, which makes them difficult to use in practical applications. Deep-learning-based semantic segmentation realizes segmentation and categorization at the same time, and thus can obtain smooth and fine-grained classification maps. However, this kind of methods require large data sets with pixel-wise categorical annotations, which are time consuming and tedious to retrieve. Compared with photographs and optical remote sensing images, manually annotating SAR data is even harder, which results in a delay of using relevant techniques in this field. In this letter, a new data set is proposed to support semantic segmentation for high-resolution PolSAR images. Limited by the aforementioned problems, the data set is only a small one with 50 image patches. Therefore, two transfer learning strategies are proposed, which adopt the fully convolutional network (FCN) and U-net architecture, respectively, and use distinct pretraining data sets to adapt to different situations. The experiments demonstrate the good performance of both methods and a promising applicability of using small training sets. Moreover, although trained with small patches, both networks can perfectly apply on large images. The new data set and methods are hopeful to support various PolSAR applications as baselines.

Nowadays, we are easy to access time series earth observation data of multiple kinds. However, since the data is so big, to effectively use them, the traditional way of processing has been challenged. Google earth engine is a powerful cloud platform that equipped with petabyte-scale archive of publicly available remote sensed data and high-performance online analysis abilities. In this paper, the patterns of global forest NPP is analyzed using this amazing tool in a data-driven perspective. Nine categories of forest are obtained with the SOM method based on their NPP levels, and their correlation with different climate zones and forest types are obtained. The correlations between their influence factors are also presented, and different patterns are found with interesting indications.

Aiming at the problems and prospects in millimeter-wave ultrahigh resolution synthetic aperture radar applications, we have developed a method with a new feature set for sophisticated classification of large images. It includes innovative parameters derived from different kinds of spectral and characteristic signatures, such as the correlation signature, radial spectrum, and angular spectrum. These features can mine repetitive information from the fragmented patterns and enhance the texture description in different aspects. In the experiment, the proposed feature set achieves 89% overall accuracy which is 25% higher compared with the gray-level co-occurrence matrix feature set. The four new features contribute to over 50% of the accuracy improvement with a significant increase of the accuracy for vehicles and show a fair performance for all the categories.

How to jointly use spatial and polarimetric information in PolSAR analysis has long been an open question. Benefiting from advanced architectures and large visual databases, deep convolutional neural networks or deep convnets (DCNNs) can generate high-level spatial features and achieve state-of-the-art performance in image analyses. However, because PolSAR data are not only multiband but also complex valued, these models cannot be easily borrowed to process them. In light of this problem, we develop a new data set to explore the abilities and potentials of DCNN on PolSAR scene classification. We observe that these models learn fixed semantic information in each layer and adapt to a different data type via changing middle-level filters. Instead of detecting colorful patterns, filters for PolSAR data tend to generate features in separate colors, which may naturally enable the network to differentiate polarimetric mechanisms. Therefore, an ensemble transfer learning framework is proposed to incorporate manifold polarimetric decompositions into a DCNN without throwing away the prelearned spatial analytic ability. Different polarimetric parameters can reflect polarization mechanisms in diverse aspects and introduce new discriminative features to enhance the object recognition. The framework achieves 99.5% validation accuracy and may benefit PolSAR applications in a wide spectrum of fields.

More information can be obtained with the improved spatial resolution of ultra-high resolution (UHR) SAR, whereas the increasing complexity and rich details lead to extreme difficulties in the automatic interpretation. In this paper, we propose a new texture feature set which involves four types of characteristic signatures and nine features to benefit applications of UHR SAR. Experiment based on real data shows that the new feature set can well describe the spatial patterns of UHR SAR in different aspects and has much better performance than the Gray Level Co-occurrence Matrices (GLCM) feature set.

Forests are simplified as homogeneous volumes constituted of randomly uniform particles, characterized by a constant extinction coefficient in the random volume over ground (RVoG) model, which has been extensively applied to polarimetric synthetic aperture radar (SAR) interferometry for forest height estimation. This letter takes into account the heterogeneous vertical structure reflected by the vertically varying extinction coefficient curve in the forest volume layer, and modifies the RVoG model to make it be more suitable for height inversion of forests with complex structures. For this purpose, the normalized extinction coefficient curve is fit by large-footprint light detection and ranging full waveform data using the Gaussian function. Finally, the varying extinction RVoG model is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system. The results are compared with in situ measurements, which indicate that the varying extinction RVoG model can obtain more accurate results for forest height inversion.

Aimed at the problems that SAR (Synthetic Aperture Radar) environment parameter inversion and data processing are lack of both overall design and synergy, this paper proposed a novel unification scheme for environmental remote sensing application. Compared to the reality that application follows data processing in the frontend, the performance of application is highlighted as the ultimate objective in the proposed scheme, where the frontend procedures should serve the backend application to get a better result. Frontend processing is divided into three parts: system design, imaging processing and post-processing procedure, while backend processing is categorized as image processing and environment parameter inversion. The concept of unification scheme focuses on the feedbacks between frontend and backend, and some demonstrations are also given in the following sections.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. One of the reasons is that the study of SAR imaging processing, SAR image processing and SAR applications are usually conducted respectively, and the study on integrating the three processes is lacked. Focusing on the science problem, taking the typical natural distribution targets (surface deformation, sea ice classification) and man-made targets (building complex and collapsed buildings) as examples, some application studies oriented to SAR environmental parameters inversion are conducted, and the information integrated frames and methods are proposed.

Information containing in the complex data is seldom considered by researchers when dealing with single synthetic aperture radar (SAR) image processing. In 2015, the statistical noncircularity, which indicates the distribution consistency between the real and imaginary parts, has been found to be surprisingly effective when analyzing the ultrahigh-resolution (UHR) millimeter-wave (MMW) SAR data set, particularly for man-made structures. However, the proposed parameter can only measure the overall noncircular level, which is inadequate to separate different noncircular behaviors. Moreover, its extraction method based on the complex generalized Gaussian distribution is very time consuming and thus makes it hard to be applied. Therefore, in this letter, we present a much simpler and more universal way to compute the noncircularity level and propose three specific parameters to specifically denote different noncircular behaviors. We also give two examples based on the real Chinese UHR MMW SAR (CUM-SAR) data set to show the abilities of the noncircularity level parameter and one example to show the preliminary ability of the specific noncircular parameters. Finally, the potentials and future application directions of these parameters are discussed. We believe that noncircularity parameters will benefit various research areas and promote the applications of UHR MMW SAR systems.

Until now, most polarimetric synthetic aperture radar interferometry (PolInSAR) research has been based on full-polarization (HH + HV + VH + VV) SAR data, which can provide complete polarimetric information but usually have a smaller swath and lower spatial resolution than dual-polarization (dual-pol) data. Some existing researches concern the dual-pol PolInSAR; however, these works did not perform the coherence optimization process which may make the inversion unstable. In this paper, the PolInSAR technique based on dual-pol (HH + HV) SAR data is investigated in order to demonstrate its validity for forest height retrieval and thus show that PolInSAR is better able to meet the requirements of global-scale research. We extend the three-stage inversion process and coherence optimization algorithm to dual-pol PolInSAR. In addition, based on the random volume over ground model, a search method for finding the volume-only coherence on ambiguous line segments is proposed. Finally, the dual-pol PolInSAR technique is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system over the Traunstein test site in Germany. The forest heights estimated by dual-pol PolInSAR are compared with those estimated using the full-pol mode and also with measurements made in situ. The results show that dual-pol PolInSAR can obtain similar estimated forest heights to the full-pol mode and also that the search method for volume-only coherence retrieval can improve the inversion accuracy. The coefficient of determination (r2) for the relation between the dual-pol PolInSAR and the in situ measurements is 0.7287.

The structure of forests is an important indicator of ecosystem dynamics and enables the modeling and monitoring of ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of superresolution imaging and a small number of measurements, compressive sensing (CS) inversion techniques for SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of CS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at P-band. A new CS-based FP MB-InSAR tomography method is proposed: a sum of Kronecker product (SKP) decomposition-based CS FP MB-InSAR tomography method (FP-SKPCS TomoSAR method). The method, based on an assumption that the reflectivity signal of a single scattering mechanism (SM) is more sparse than that of a composite of SMs, recovers the reflectivity profile of different SMs by using the CS technique. This method not only allows superresolution imaging with a low number of acquisitions but also can estimate the polarimetric SM of the vertical structure of forested areas. The effectiveness of these novel techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne data sets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana, and the results of the vertical structure of forested areas derived by the method are verified by in situ test data.

The rapid development of synthetic aperture radar (SAR) sensors results in the acquisition of substantial ultrahigh-resolution SAR images. In this paper, we, for the first time, present three scenes of single-polarization SAR images with decimeter resolution obtained by a millimeter-wave (MMW) Chinese airborne SAR system. An innovative framework based on the complex generalized Gaussian distribution (CGGD) model is proposed to extract land use information from them, and three CGGD parameters, including the shape parameter, the non-Gaussianity parameter, and the noncircularity parameter, are selected to identify different kinds of ground objects. It is shown that these parameters can reveal plentiful land surface information and will be extremely helpful for single-polarization SAR image interpretations. Moreover, a decision tree classifier is built to categorize these images into homogenous natural surfaces, vegetation textures, circular man-made targets, and noncircular man-made targets. Several interesting experiments are implemented, and their results well demonstrate the effectiveness of the new framework.

The geomorphic features of alluvial fans in arid and semi-arid areas can contain vast amounts of information for the study of paleoclimatic and paleoenvironmental changes. Taking the Shule River Alluvial Fan (SRAF) as study area, the research of surface roughness estimation, one of the important geomorphic features of the arid and semi-arid alluvial fans, was carried out by using Radarsat-2 polarimetric synthetic aperture radar (SAR) data in this paper. A modified roughness inversion model was developed to solve the roughness overvalued problem when the conventional models are used in arid surface of alluvial fans directly. In this model, the dielectric constant of the gravels exposed in the surface, instead of the moisture, becomes a more important influencing factor on backscattering coefficients. After comparing the results retrieved from the conventional and modified roughness invention models, the correlation coefficient increases from 0.68 to 0.85, and the absolute difference between the inversion and field measured value reduces obviously. As a result, the proposed model improves the accuracy effectively and is suitable for the roughness parameter inversion in the arid surface of alluvial fans.

Cloud shadows in satellite imagery hinder understanding of ground surface conditions due to reduced illumination and the potential for confusion with illuminated low-reflectance objects such as water bodies. This paper extends the application of the haze optimized transform (HOT) from haze mapping to include object-oriented detection of clouds and cloud shadows. An integrated processing chain encompassing these tasks has been implemented and successfully applied to Landsat Enhanced Thematic Mapper Plus and Multispectral Scanner imagery covering a variety of land covers and landscapes. The results confirm that the HOT-based method for cloud shadow detection is robust and effective. Cloud shadows have been identified and extracted with overall accuracy of about 95.3%. Clear-sky dark pixels (e.g., small lakes) are well separated from cumulus cloud shadow pixels. The spatial distribution of HOT response in a given cloud patch can be used to estimate the extent and variation of incoming visible radiation reduction in its corresponding shadow patch. This information, in turn, has been used to apply a radiometric gain to compensate for the shadowing effect on the land. The HOT response has been tested for radiometric characterization of cloud shadows and subsequent shadow illumination compensation.

In the last two decades, the earth has suffered unprecedented urban expansion. The object of this paper is to reveal the difference of thermal response of extremely high temperature in JINGJINTANG agglomeration in two urbanization stage by using WRF model and real-Synchronization Landsat data. And so does it for GTHA agglomeration. By comparison of the two differences, it well reflects Land Surface Temperature difference caused by the urbanization. For both agglomerations, urbanization has less impact on the urban center, while it has more impact on new urban. Jingjintang has stronger UHI extent which reached to 6°C -8°C than in the day while GTHA has less LST difference at night and more LST difference in the day comparing to Jingjintang. Overall, this method provided in this research can be used to analyze impacts on urban thermal environment and also used for thermal environment monitoring and prediction.

Urban area man-made target detection based on SAR images has been a challenging field for years due to the complicated scattering mechanisms of dense buildings and the poor visual quality of SAR images caused by speckle noises. To overcome the effect of speckle noise, a substantial portion of SAR image processing methods are based on statistical characteristics. In this paper, the importance of using non-zero mean models is discussed in the experiments, and two statistical models are proposed for polarization SAR image processing. Moreover, a non-zero mean index is invented to test the scattering determinacy level. This work will be very helpful for the improvement of urban area information extraction based on fully polarized SAR images.

A comparative study has been carried out on the most recent methods for Land Surface Emissivity (LSE) estimation using Landsat TM/ETM+ data. The popularly used method, the integrating NDVI and classification is chosen and analyzed further. The result shows that the estimation model is not accurate enough for LSE is underestimated at either end of the fractional vegetation cover (Pv) range which would lead to overestimating of corresponding Land Surface Temperature (LST). The drawback is modified based on the threshold method, that is, for natural surface and when Pvsoil; for town surface and when Pvm; and for Pv>; 0.5, ε= εv. Finally, a processing way adapting for the improved model in large area is presented and the emissivity model before modification and after improvement is applied to Beijing, China to identify emissivity and further to retrieve LST using image based method. The results show that the large difference between LSE and corresponding LST is located in the town surface and soil with low Pvand that a decrease of emissivity by 0.011058 at 318K will increase LST by about 1K. Thus a promising improvement of comparative accuracy can be expected.

The objective of this paper is to map large-area impervious surfaces in bohai ring megalopolis areas from Landsat imagery using SVM method. Then, combined with the sixth population census data, the map of the impervious surface area (ISA) per person is derived. Finally, this paper analyses the relationship between the impervious surfaces and population census data, and gross domestic product (GDP) data. Our results indicated that the density of ISA in Beijing and Tianjin were great higher than in other administrative regions. By comparing the ISA with population census and GDP data, our results also indicated that the ISA was highly correlated with population census and GDP data. A strong relationship (R2= 0.879) was observed between the ISA and the population census data in the whole study area. In addition, a strong and positive relationship was observed between the ISA and GDP data (correlation coefficient R2= 0.779 for the whole study area). This research can provide a simple method for policy makers to assess potential urbanization impacts of future urban planning and development activities.

A method based on Rician distribution is proposed in this article to detect nonstationary targets in urban area from Synthetic aperture radar (SAR) image. Rician distribution is an adaptive model which could better fit the statistical characters of urban area SAR image than Wishart distribution, and then improve the detection results. The method has been proved to be effective by the experimental results based on E-SAR data. Particularly, the result in azimuth direction is highly appropriate to discriminate man-made targets and natural targets, which will be very meaningful in urban area studies.

We developed a new method to monitor the ice sheet melting periods with passive microwave measurements. Our method combined the original brightness temperature time series with those simulated by time series simulation software package TIMESAT in order to obtain a time series with similar behaviour, but less noise. A generalized Gaussian model was used to classify the pixels to wet and dry snow. Based on the steep rise and drop of the new time series, we detected the onset and end of the Antarctic ice sheet melting from 1988 to 2008. The results indicated that the whole Antarctic experienced a deceasing melting during the 20 years. The Antarctic Peninsula region exhibited a long and stable melt occurrence, in comparison with which the Ross Ice Shelf has the nearly shortest melt duration and the largest variability in melt extent.

Large-scale earthquakes severely damage people's lives and properties. Airborne and spaceborne remote sensing can be used accurately and effectively to monitor and assess earthquake disasters in near real time, providing an important scientific basis and decision-making support for government emergency command and postdisaster reconstruction. This letter proposes a new H-α-ρ method (H is the entropy, α is the average scattering mechanism, and ρ is the circular polarization correlation coefficient) for extracting the spatial distribution of collapsed buildings using RADARSAT-2 fine-mode polarimetric synthetic aperture radar (SAR) data. The method was tested on imagery from the Yushu earthquake in the Qinghai Province of China which occurred in 2010. When compared with high-resolution optical imagery, the result indicates that the proposed method can be effectively used to identify collapsed buildings and also demonstrates that polarimetric SAR data have potential for application in urban disaster monitoring and assessment.

Polarimetric interferometric synthetic aperture radar (POLInSAR) has been used to extract the forest height successfully. Leaf area index (LAI) is an important parameter for the estimation of forest biomass, however presently the most of LAI extraction approaches are based on optical data. For the dense forest, the optical image is mainly the reflection of the canopy surface, and it is saturated easily for the LAI retrieval. Microwave has the relative long wave length, can penetrate into the forest and even get the backscatter information of the ground surface, so it should be an ideal tool to estimate LAI. This paper built the linear relationship between the LAI and the product of tree height and extinction coefficient, the latter two parameters can be inversed by the means of POLInSAR technique. At last, this study provides a preliminary experimental result, which shows that the linear relationship between the LAI retrieved from the SPOT-5 image and the product of tree height and extinction coefficient inversed from POLInSAR based on RADARSAT-2 image pairs.

The main objective of this paper is to examine and evaluate the performance of the airborne SAR system through interferometric processing and error analysis. Firstly, the paper describes how high-precision DEMs are derived from the airborne dual-antenna InSAR data. Based on airborne dual-antenna InSAR bore-sight model, this paper summarizes the main factors which influence the accuracy of DEM in data processing, and analyses the error of those factors. Then, the POS/AV510 system parameters are used for analyzing the quantitative relationship between the platform height, baseline length, baseline angle, look angle and DEM error. The experimental data used is airborne dual-antenna X-band InSAR data, and the measured GCPs are used to validate the accuracy of DEM. Evaluation results in terms of the standard deviation (SD) and the average mean error (AME) are derived by comparing the reconstructed InSAR DEM with the reference GCPs. The AME of the DEM is up to 1.7628 m. The SD of the DEM are up to±1.0858 m.

Variation in the building aspect angle, defined as the angle between the flight direction of a satellite (azimuth direction) and the vertical wall of a building, is a significant cause of accuracy reduction in building detection and terrain classification from Polarimetric Synthetic Aperture POLSAR (POLSAR) images. However, the existing building detection methods usually are effective for the buildings with a limited aspect angle range. Considering the relationship between the aspect angle of the building and the orientation angle shift, an aspect angle normalization method is introduced to remove the disadvantageous influences caused by the aspect angle. From a comparison between the double-bounce building detection results of the original and aspect angle normalized RadarSat-2 data acquired in Beijing, China, buildings with any aspect angle can be detected effectively from aspect angle normalized data.

Inland lakes in the arid and semi-arid areas are sensitive to the climate change and human activities, The long time-series remote sensing data, with a capability to provide valuable information of the distribution and changes of inland water bodies, have become an effective tool for lake study. In this paper, the lakes in Ejin alluvial fan, located in a typical arid region in the west of Inner Mongolia, China, are chosen as study lakes. Twenty years period TM data are used to obtain the long time-series and continental information of the lake change from 1987 to 2008. Climate data and hydrological data are also collected for correlational study. The results show that 1) in latest 20 years, the total area of the studied lakes in Ejin alluvial fan reduced firstly then increased after 2000. 2) The area changes of the four lakes are different. 3) Both the environmental change and the man-kind factors are the drivers of the lake change in Ejin alluvial fan. The latter might exert a main influence.

The advances in Polarimetric SAR Interferometry (PolInSAR) techniques provide a promising way to recover ground characteristics such as sub-canopy soil moisture and roughness using SAR data. Spectral analysis techniques have been applied to extract the vegetation and building parameters. Yamada et al proposed the ESPRIT algorithm to estimate vegetation height; Sauer et al apply the spectral analysis techniques to estimate building heights and extract physical properties from Multi-baseline (MB) PolinSAR data. In these applications, the parameters are mainly estimated from the phase information or phase center, but the validity of the sub-canopy soil backscattering or reflectivity estimation from polarimetric spectral analysis technique is not investigated. In this paper, the ground scattering center is first located by po-larimetric MUSIC algorithm and then the ground reflectivity is recovered using a polarimetric least-square method. The validity of the polarimetric spectral analysis technique for the sub-canopy ground reflectivity estimation is demonstrated using simulated and real SAR data.

Finding the target contour information from a remote sensing image is one of the fundamental steps for image analysis. Conventional target contour extraction methods are usually based on the statistics information of the image. In this paper, using the maximum return value of the normalized scattering matrix derived from full-polarized Synthetic Aperture Radar (PolSAR), the relationship between the contour of targets and their corresponding dominate scattering type is preliminary researched. Then a novel target contour information extraction method based on the physical scattering mechanism of terrain targets is proposed, which is more effective and adaptable due to the scattering mechanism of terrain targets do not depend on the radar backscattering intensity, but its proportion among different polarizations. After applying to E-SAR airborne data, the results show that this method has a good capability to extract the target contour information.

In this study, the Touzi and Cloude-Pottier decompositions are compared for land cover characterization using ALOS/PALSAR polarimetric data collected at September, 4, 2006, over the Weinan region of Shanxi Province in China, the most useful parameters are combined for land cover classification. The classification results are assessed and validated using Landsat TM image of Weinan area acquired at August, 9, 2006.

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.

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

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.

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.

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.

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.