The implementation of the United Nations (UN) 2030 Agenda for Sustainable Development (2030 Agenda), with its 17 sustainable development goals (SDGs), faces challenges such as insufficient data, limited research methodologies, and uneven progress across regions. Earth observation (EO), particularly scientific satellites, offers unique advantages in supporting global sustainable development by providing objective, dynamic, and large-scale datasets for SDG evaluations and policymaking, as well as by facilitating the study of Earth’s environmental systems and their interactions with human activity. Sustainable Development Science Satellite 1 (SDGSAT-1), the world’s first scientific satellite dedicated to supporting the 2030 Agenda, was designed and developed by the International Research Center of Big Data for Sustainable Development Goals (CBAS). Its three advanced EO sensors, i.e., Thermal Infrared Spectrometer (TIS), Glimmer Imager (GLI), and Multispectral Imager (MSI), furnish high-quality data, enabling continuous monitoring of human activity and environmental changes to bolster SDG-related research and global sustainability initiatives. As of November 2025, SDGSAT-1 has collected over 480 000 global terrain coverage images since its launch in November 2021. All its datasets have been shared free of charge with the Global Scientific Community through the SDGSAT-1 Open Science Program initiated in September 2022. The datasets have enabled researchers from more than 110 countries, 10 UN agencies, and various international organizations to publish over 180 scientific articles, 17 UN reports, and numerous public data products. These have demonstrated applications in urban development, disaster response, environmental monitoring, agriculture, and marine conservation. This article reviews the technical innovations and mission specifications of the SDGSAT-1 satellite, demonstrates its contribution in leveraging space technology for SDG monitoring and evaluation, and discusses the future evolution of development of EO systems, specifically the planned Sustainable Development Satellite Constellation, for supporting the global achievement of SDGs.

In recent years, the popularity of LEO satellite internet has made satellite security the focus of industry and academia, such as jamming and spoofing attacks aimed at physical satellite signals. Therefore, developing sophisticated anti-jamming and anti-spoofing technologies is essential, and individual identification of satellites is a prerequisite for implementing this technology. However, off-the-shelf LEO system signals exhibit significant differences in fading characteristics, communication protocols, modulation schemes, and Doppler shifts compared to terrestrial signals, which challenges current individual identification. Besides, individual identification research on off-the-shelf LEO systems such as Starlink remains relatively scarce. This paper proposes SatTransformer, a novel satellite signal individual identification method for LEO system signals, integrating nonlinear mapping and Vision Transformer techniques. We enhanced signal spectrum and doppler shift features by nonlinear transform in our identification method, while maintaining a balance between local and global feature extraction. To evaluate the model's performance, we conducted an extensive data collection campaign, acquiring signals from Starlink satellites over a 25-day period, resulting in a dataset comprising over 30,000 data samples. Experimental results demonstrate that our proposed method achieves superior accuracy (91.3%) compared to existing approaches in the field of satellite signal identification.

The geometric characteristics of Moon-based Earth observation platforms differ significantly from those of satellite platforms, with geolocation being a key factor that impacts data quality. The geolocation of a Moon-based sensor is influenced by three key factors: lunar ephemeris (lunar position and libration), Earth orientation parameters (EOPs), and the Earth reference model. Measurement errors from these three sources can significantly affect the geolocation accuracy of a Moon-based sensor. This study proposes a new unbiased estimation method to quantify the geolocation uncertainty introduced by these factors, based on the fusion of multiversion datasets. The method avoids making assumptions about the error distribution of ephemeris parameters while providing an effective approximation of the spatiotemporal patterns of geolocation uncertainty. We integrate three types of ephemeris data, three Earth reference models, and multiple EOPs datasets to assess the overall distribution of geolocation uncertainty and separately evaluate the geolocation uncertainty introduced by each individual factor using control variates method. The results indicate that the maximum total geolocation uncertainty caused by the three factors is about 46 m. Ephemeris errors are the dominant contributor, accounting for more than 98% of the total uncertainty. In addition, measurement errors in lunar libration also account for why longitudinal uncertainty is significantly greater than latitudinal uncertainty.

A Moon-based sensor offers a unique view for continuous Earth observation. The 2-D turntable’s pointing performance is a critical factor influencing geolocation accuracy. The vast distance between the Earth and the Moon amplifies minor pointing errors of the turntable into significant geolocation inaccuracy. By establishing a geometric model, an analytic expression of Earth’s trajectory from the Moon-based view is derived. Three critical issues are discussed: 1) the Earth’s 18.6-year trajectory forms a$16^{\circ } \times 14^{\circ }$envelope, which determines the observation range for the sensor. The rotation angle and position of the envelope vary at different lunar locations, while its size and shape remain consistent; 2) geolocation errors caused by temporal interval vary periodically with a half-sidereal month cycle and can be compensated by calculating Earth’s velocity, while errors due to the step angle show irregular oscillations. Without calibration, both parameters can introduce geolocation errors on the scale of hundreds of kilometers. Reducing both parameters can significantly improve geolocation accuracy; and 3) even with optimization of both parameters, the geolocation accuracy cannot be reduced to within a single pixel. To achieve geolocation accuracy within design requirements, it is necessary to not only optimize these two factors but also adopt additional measures to improve precision. All these insights will inform the parameter optimization and design of the Moon-based sensor for future applications.

Deploying Moon-based sensors at the lunar south polar regions offers unprecedented opportunities for continuous Earth observation. However, previous studies have neglected the combined effects of lunar terrain and latitudinal libration on Earth visibility. This study systematically evaluates the Earth visibility conditions in the lunar south polar regions (80°S–90°S) using high-resolution Lunar Orbiter Laser Altimeter (LOLA) data and a novel visibility algorithm. Results reveal that terrain obstruction reduces Earth visibility by up to 80% in crater-rim and high-latitude regions, rendering the spherical Moon approximation inaccurate for these areas. A 14° maximum elevation angle threshold is identified for complete Earth visibility at lunar south polar regions. Optimal sites cluster near the lunar central meridian (0° longitude) in low latitudes (80°S–82.5°S), balancing extreme observation geometry and terrain occlusion effects. High-visibility areas (≥95%) are fragmented into small patches (82.5°S). This letter has quantitatively characterized the impact of lunar terrain on Moon-based Earth observations, offering guidance for future Moon-based Earth observation sensor deployment and lunar base planning.

As the first satellite specifically designed to serve Sustainable Development Goals (SDGs), the Sustainable Development Science Satellite 1 (SDGSAT-1) has attracted numerous attention by providing panchromatic glimmer imagery with a unprecedented clarity of 10 meters. However, unsatisfactory positional accuracy of glimmer imagery hinder further fine-grained studies. This study identifies the cause of these positional errors as misalignments generated during the data production process, and thus proposes a correction method based on deep learning object detection and cross-correlation. The method achieves accurate measurements of the position, direction, and distance of the misalignment, which are used to correct the glimmer imagery and to effectively improve the positional accuracy. In this study, we (1) propose a simulated misalignment dataset which demonstrated with excellent performance during the training of multiple deep learning object detection models, reaching up to 99.44% average precision (AP); (2) propose pixel brightness correlation coefficient based on cross-correlation and validate its effectiveness to characterize misalignments; (3) improve detection efficiency by the threshold design of window average brightness and object detection confidence; (4) explore the reasons for the misalignment suggests that it is the difference between the satellite’s designed operating time and the actual operating time that leads to the Charge-coupled Device(CCD) stitching flaw. Our proposed method can contribute to evaluating and improving the positional accuracy of SDGSAT-1 panchromatic glimmer imagery, providing a way to obtain datasets with good spatial consistency and to conduct related research.

With the explosive growth in the number of NSGO satellites, there is an increased demand for satellite tracking technology, making the accurate prediction of satellite trajectory and optimization of target tracking efficiency crucial. This paper proposes a strategy for tracking NGSO satellites, which employs a two-stage stochastic model with a greedy approach to optimize the real-time prediction of multiple NGSO satellite passes and improve the efficiency of noncooperative ground equipment. The first stage of the model employs SGP4/SDP4 functions for initial orbit predictions, isolating satellite data from ground equipment performance. The second stage iteratively refines these predictions, addressing the complexities associated with noncooperative ground equipment, constrained resources, the high frequency of NGSO constellation satellites pass, and effective target management. The resulting two-stage model achieves precise and efficient operations, effectively overcoming the traditional challenges of stochastic optimization in dynamic conditions involving multiple satellites. It successfully copes with the exponential surge in computational requirements as the number of satellites increases. A case study with an antenna system validates the practicality and effectiveness of the model. The optimized sequence significantly enhances overall performance concerning efficiency, utilization, idle time, angular displacement, and trajectory symmetry, achieving a 27.48% improvement over the traditional solution and a notable 58.33% increase in the number of effectively tracked satellites, all within a rapid 15-second execution window.

We analyzed the spatiotemporal variations of the imaging swath and region using the lunar-based synthetic aperture radar (LBSAR) for Earth observation. The ground and slant swaths following the Earth-observing geometry and synthetic aperture radar (SAR) configurations were formulated. The bounds of the imaging swath were highlighted for appropriately configuring the LBSAR. The results suggested that the imaging swath is determined by the coupling of orbit elements, near-look angle, antenna beamwidth, and to some extent by the Earth’s ellipsoidal shape and LBSARs site location on the Moon’s surface. The side-looking direction, despite its modest impact on the imaging swath, plays a dominant role in ascertaining LBSARs imaging region. Furthermore, spatiotemporal variations of ground swath in various epochs were analyzed to emphasize the challenge and importance of characterizing Earth observation capability in the LBSAR.

In this paper, we propose an interference mitigation strategy for low Earth orbit (LEO) satellite systems based on adaptive beamforming that incorporates both sidelobe level (SLL) control and dynamic adaptation to reduce co-frequency interference by analyzing the real-time positions of interfering satellites and serving satellite relative to the user terminal. In this study, we consider a uniform rectangular array (URA) as the user terminal antenna configuration in the LEO satellite system. The adaptive beamforming technique based on the Taylor weighting algorithm is applied for sidelobe suppression by generating a beam pattern with the desired SLL. The real-time positions of interfering satellites and serving satellite relative to the user terminal are computed by solving the orbital parameters. Based on real-time position information, the adaptive beamforming technique is utilized to dynamically adjust the beam pattern and generate an appropriate SLL, thereby minimizing the impact of co-frequency interference to its maximum extent. The simulation results demonstrate that the proposed strategy achieves a user terminal received carrier-to-interference ratio (C/I) exceeding 27dB for 95% of the simulation time, representing a significant improvement of 47.5% compared to conventional methods. Moreover, the upper limit of C/I has also significantly escalated from 40dB to 80dB. These findings strongly validate the effectiveness of the proposed strategy in mitigating interference and enhancing overall system performance.

The Earth’s outgoing radiation (EOR) at the top of atmosphere (TOA) is an advantageous goal of Moon-based Earth observations. In this study, we focused on the Earth’s reflected radiation (ERR), which is a component of EOR and has a strong relationship with the Earth–Moon–Sun positions. To determine ERR characteristics, we parameterized the Moon-based Earth observation geometry and proposed a method to simulate the ERR at TOA for a Moon-based sensor under clear- and all-sky conditions. The ERR was found to have the distinct cycle of a synodic month and was inversely proportional to the Earth’s phase angle. Clouds increased ERR reflected to space under hemispheric-scale observation; thus, ERR under all-sky conditions was approximately twice as much as that under clear-sky conditions. The ERR was also influenced by incoming solar radiation (ISR), and the removal of the effect of ISR on ERR helped reveal ERR characteristics clearly. The results showed that when the Earth’s phase angle was between 0° and 90°, particularly when the Earth’s phase angle was in the range of 30°–60° and 60°–90° under all- and clear-sky conditions, respectively, the ERR–ISR ratio could indicate the reflective characteristics of the Earth under hemisphere-scale observations. These results will be helpful in demonstrating Moon-based ERR monitoring.

Rich datasets related to the earth have been obtained because of the rapid development of earth observation technologies. A broad range of prior research has investigated how to obtain the correlation relationships of relevant features from a large number of data containing spatio-temporal information, which is also the technical basis for big data analysis. Based on the dynamic time warping (DTW) algorithm, this research proposes a correlation analysis method of time series data, and applies it to the correlation analysis between the time series features of the surface temperature and melting area of the Antarctic ice sheet. The results show that our method based on the DTW algorithm can effectively distinguish the change details of the non-linear time series. The correlation coefficients between these two highly-correlated factors computed by our method are higher than Pearson’s correlation coefficients by more than 0.2 almost in all study areas where data are available. In summary, the method proposed in this study provides a new feasible way for the correlation study of time-series data. It outperforms than traditional correlation coefficients such as Pearson’s correlation coefficient in some fields, specifically when complex nonlinear time series data with certain periodicity are used.

A novel forest leaf area index (LAI) inversion scheme using polarimetric synthetic aperture radar (SAR) images is proposed. The advantage of inversion using SAR images over conventional optical methods is the ability to perform all-weather and all-time forest observation, especially in the tropical rain forest area. In this paper, leaves were assumed to be isotropic media, while branches were assumed to be anisotropic with a certain orientation. We firstly investigated the specific extinction coefficients of propagation through leaves and branches, respectively. Then, a method was employed to separate the attenuation of leaves and branches. Subsequently, an inversion scheme model for LAI inversion using polarimetric SAR images was designed based on the linear relationship between the LAI, and the products of forest height and extinction coefficient. Lastly, an experiment was performed using L-band SAOCOM (Satelite Argentino de Observación COn Microondas) spaceborne SAR image pairs. The forest height and extinction coefficients according to H and V polarizations were retrieved using the oriented volume over ground (OVoG) model. The linear relation between the LAI, and the products of forest height and extinction coefficient was verified using the data measuredin situ, and the coefficient of determination (R2) was 0.64. Finally, temporal decorrelation and the extinction component caused by the oriented elements were analyzed.

This paper studies the orbital perturbation effects on the azimuthal resolution in lunar-based synthetic aperture radar (LBSAR). We derive explicit expressions for the Doppler frequency modulation rate (DFMR) and beam-crossing velocity using the antenna beam pointing and orbit models. Following that, the azimuthal resolution is expressed in line with orbital elements and SAR configurations. The results show that the long-term orbital variations caused by accumulated perturbation effects significantly affect the azimuthal resolution, which, in effect, produces aperiodic variations in the azimuthal resolution. Such a phenomenon is most distinguished for a large LBSAR look angle, leading to a fluctuation of over 30% or even larger in the azimuthal resolution across different cycles. Additionally, the errors give rise by short-term orbital perturbations could impact azimuthal resolution to a lesser extent, with corresponding fluctuations consistently below 3%. The findings reveal that it is imperative to consider the irregular variability of azimuthal resolution due to orbital perturbations in the LBSAR.

The understanding of glacier and ice shelf front changes is of paramount importance in analyzing their material balance and their significant contributions to global sea-level variations. Recently, the increasing availability of remote sensing images has provided ample data support for calving front detection. By combining massive amounts of data, deep learning techniques have demonstrated great potential in front extraction, as they can automatically generate task-specific features. In this study, in contrast to the traditional framework based on a pure CNN architecture, we propose a novel calving front detection network, GLA-STDeepLab, by incorporating the Swin transformer module into DeepLabv3+ to enhance the modeling capability of long-range contextual dependencies. We also introduce Global-local attention mechanisms into the module, striving to foster interactions between the corresponding coarse and fine-grained feature maps. Through extensive experimentation on a diverse and challenging SAR database, CaFFe, our network is shown to surpass the current state-of-the-art methods, achieving results with an IoU of 0.94 and a mean distance error (MDE) of 473±34 m. More meaningfully, we extracted the time-series front information of the Amery Ice Shelf from 2015 to 2023 and precisely captured the calving events of the D-28 and D-32 icebergs. The findings demonstrate that our method enables precise monitoring of the dynamic information of glacier and ice shelf fronts, as well as their far-reaching implications for ice sheet mass balance and global climate change. The code used in this study is provided at https://github.com/Tangyu35/Calving-front-detection.

The variation of pointing vector direction for Moon-based Earth observations are analyzed in this paper. The pointing vector direction is parameterized as two angles, namely azimuth and elevation angle. We first establish the observation geometry model to calculate the pointing vector in Moon-centered Moon-fixed (MCMF) coordinate system. Then, the azimuth and elevation angles are defined and their theoretical expressions are deduced respectively. Besides, different observation periods are considered. It is found that the pointing vector direction shows regular variations over one orbital period and 18.6 years. The combination of azimuth and elevation angles can characterize variations of pointing vector direction. They mainly depend on the lunar sub- Earth points. The elevation angle ranges from 83 ° to 97° and the azimuth angle varies from −8° to 8° in an 18.6-year period. These characteristics will give valuable guidance for application of Moon-based platform.

Sustainable development poses a good way for the world of Arctic, especially the temperature and vegetation has been experiencing a rising trend in the north land area. The research aims to understand the impacts of the gas flaring to the local environment through the time series analysis of vegetation changes around the gas flaring sites, and natural development sites of from 2000 to 2021 over Russia Siberia regions, the Timan-Pechora basin and West Siberia basin oil/gas rich area. The results shows that gas flaring sites has a profound to the vegetation changes at a statistical level, with the temperature analysis, obviously the flaring shows the positive contribution to the regional environment. The result indicates that the gas flaring.

The Moon-based telescope with visible and near-infrared band is equipped on the lunar surface which is 360000 kilometers away from the Earth, and is expected to provide a unique observational data set for the Earth. In this paper, we first proposed an ideal telescope optical system of the Moon-based telescope, then preliminarily designed the stray light suppression structures including the outer baffle and the inner baffle. Further, the geometric model and properties of the telescope structure are established, and the stray light of the Moon-based telescope with visible and near-infrared band is simulated and analyzed by ZEMAX software. The results show that the when the off-axis angle is$4^{\circ}$, the Point Source Transmittance (PST) magnitude is 10−4; when the off-axis angle is more than$20^{\circ}$, the PST is 0. It can be seen that the baffle used in this paper can meet the requirements.

The outgoing longwave radiation (OLR) at the top of the atmosphere is a key component of Earth’s radiation budget. Moon-based OLR observations take Earth as a single point, and provide an alternative to near-Earth orbital measurements. However, in the sensitivity design of a sensor, the effects of adopted Earth’s shape on the design of the sensitivity are particularly severe in the Moon-based OLR observations, which needs to be fully considered. In this study, the observational solid angle related to the ellipsoidal Earth model during OLR measurements was analyzed to improve upon previous studies conducted under the assumption of a spherical Earth. The radiative equilibrium temperature of the Earth–atmosphere system was then applied to simulate the emitted OLR according to Stephen Boltzmann’s law. The magnitude and sensitivity of the Moon-based OLR measurements were estimated by combining the observational solid angle and the simulated OLR. The results showed that the sensitivity of Moon-based OLR measurements is on the order of$10^{-3}$W m−2K−1, and the effects of Earth’s shape on sensitivity estimates cannot be ignored. Further, quantitative estimates revealed that a more realistic (ellipsoid) shape is needed when designing the sensitivity of relevant sensors.

The Moon-based platform has different observation angular characteristics from artificial satellites due to its peculiar orbit. To illustrate the difference, we first normalize the positions and attitudes of the Sun, Earth, and Moon into the same reference system using coordinate transformations. Then, we calculate the observation angles (including viewing elevation angel, solar elevation angel and relative azimuth angle) distribution of satellites in LEO, GEO and at Lagrange L1 satellites during the same period. Furthermore, the observation angles distribution of the Moon-based platform in different seasons with the Earth observed point located at different positions are analyzed. Results revealed that compared to artificial satellites, the Moon-based platform has continuous viewing observation angles and solar observation angles. Its observation angles change with the seasons, and it provides more angular information for equatorial regions than polar regions of the Earth.

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.

Although estimating the Earth radiation budget (ERB) by satellite platforms have become more and more reliable, it is still difficult to carry out conclusive evidence that supports whether the Earth is warming. As the important component of ERB, the measurement requirements of Earth's outgoing radiation are to acquire global-scale absolute calibration data. Observing Earth's outgoing radiation on the lunar surface is a feasible way to improve the consistency and continuity of such data. Since the Moon is a celestial body and the sensor can be equipped anywhere on the near-side of the Moon, the site selection issue of a Moon-based platform will be the most vital step. This paper mainly discusses the site selection issue of a Moon-based Earth radiation budget experiment platform from the perspective of Earth observation geometry. By analyzing observation performance, geolocation accuracy, radiation response and solar invasion effects, we suggest that the mid-high latitude will be a better position to equip sensors.

Moon-based platform is a potential platform that can realize the observations of large-scale geoscience phenomenon. Unlike existing earth observation platforms, the moon-based platform is equipped on a natural celestial body. Its position is calculated by the lunar position and libration derived from the planetary ephemeris. However, limited to the astrometric model and accuracy of observational data, no planetary ephemeris can provide absolutely accurate data, and this will lead to a platform's position error. This letter investigates the impacts of the platform's position error on geolocation for a moon-based sensor. We first made comparisons to the lunar position and libration derived from different planetary ephemerides so as to evaluate the magnitude of the platform's position error. Then, the Monte Carlo method was applied to simulate the platform's position error. According to the geometric model, the effects of the platform's position error on geolocation were presented. The results showed the effects of lunar libration error are associated with position on the lunar surface, while the differences of lunar position error effects are not evidently shown in different positions on the lunar surface. Furthermore, high-latitude regions of the moon are demonstrated to have less impact on the platform's position error, which would be suitable for equipping earth observation sensors.

The spatiotemporal coverage of a Moon-based synthetic aperture radar (SAR) is analyzed based on the imaging geometry, upon which the spatial coverage and image formulation rely. The distance from the Earth to the Moon-based SAR and bounds of the grazing and azimuthal angles jointly determine the coverage area on the Earth's surface. Meanwhile, the ground coverage of the Moon-based SAR is determined by the bounds of the grazing and azimuthal angles and geographic coordinates of the nadir point at a specified time. Moreover, the temporal variation in the spatial coverage is pertinent to the temporally varying nadir point of the Moon-based SAR on the Earth's surface. Furthermore, numerical simulations using the lunar ephemeris data are carried out to complement the analysis and to illustrate the spatiotemporal coverage. Finally, a guideline for the optimal site selection of a Moon-based SAR is proposed. In conclusion, a Moon-based SAR has the potential to perform long-term, continuous Earth observations on a global scale to enhance our capability to understand the planet.

Recently, moon, the unique earth's natural satellite, was proposed as a new platform for large-scale geoscience phenomena observation. However, the geometry between sun, earth and moon, was the precondition to understand the moon-based earth observation from the point of the sun-target-sensor geometry. If a sensor was arranged on moon's surface, the location of the sensor will affect the earth observation geometry directly due to the longer observation distance. After designing and implementing the geometry simulation system, we use it to simulate the geometry for moon-based earth observation (MEO) and discuss the influence of moon-based sensor (MBS)'s location on earth observation. In this work, some of the intelligible aspects are taken into account, including the MBS's subpoint, the earth interests' azimuth-elevation angle for the MBS and the observation duration. We calculated the three aspects under the geometry numerical simulation system and using the JPL DE data, and the results show that the MBS's location affect the three aspects directly but with different degree.

There is increasing concern about the new potential platform for Earth observation - the Moon-based Earth observation platform. The Moon-based platform has the advantages of large-scale, constant and long-term dynamic Earth observations, when compared with the space-borne and airborne platforms. Some scholars have presented ideas for the construction of Moon-based Earth observation, however, most of them ignored the impact caused by lunar terrain. In this paper, we propose a Moon-based Earth observation intervisibility algorithm, based on the Lunar Orbiter Laser Altimeter (LOLA) data. Then we apply the algorithm to the Sinus Iridum area and the Mare Nectaris area and analyze the inter-visibility conditions of the two areas. This intervisibility algorithm will give support to the site selection of future Moon-based Earth observation system or lunar exploration missions.

To deepen the understanding of the Moon-based Earth observations, we establish a new kind of expression of the observation angle. Considering the Earth as a single point, the relative azimuth angle and viewing zenith angle of a Moon-based platform are defined. We first summarize the general procedures of the geometrical model. Based on the geometrical model, the positions of the Sun, the Earth, and the Moon can be acquired. Then, the new expressions of the observation angles are defined in the International Terrestrial Reference System (ITRS). The relative azimuth angle is related to the solar direction, while the viewing zenith angle describes the relationship between the lunar position and the Earth Equator. Results revealed the characteristics of the viewing zenith angle and relative azimuth angle, demonstrating the features of Moon-based Earth observations.

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.

Large-scale geoscience phenomena are increasingly attracting more attention because of their great scientific and social significance. However, many existing earth observation systems lack the ability to conduct long-term continuous observations at a regional-to-global scale because of spatial and temporal coverage limitations and systematic bias. Recently, the Moon, the unique Earth's natural satellite, was proposed as a new platform for earth observation of large-scale geoscience phenomena. However, the geometry relationship between the Sun, Earth and Moon was the precondition to understand the moon-based earth observation. In this work, a simulation system of moon-based earth observation was developed based on the Jet Propulsion Laboratory ephemerides, the reference systems transformation, and which was implemented under the Microsoft. NET framework using C# language. In this system, we will carry out the following studies: (1) The position and attitude change rule of moon-based platform. The position and posture of moon are the main factors that affect the Earth observation, which needs to be studied based on the relative motion law and the moon-based platform simulation system. Then, we will study the regularity of the position and the attitude periodicity of the moon-based platform. (2) The regularity of moon-based platform for Earth observation. Based on the proposed moon-based Earth observation simulation system, the changes of the solar angle, observation angle, observation range, sub-satellite point position, twilight line, oblique distance and observation period of the typical moon-based Earth observation sensor are analyzed.

We analyze the error caused by `stop-and-go' assumption on Moon-based SAR image focusing. In Moon-based SAR imaging of earth, the extremely long propagation time delay and curved trajectory violate the `stop-and-go' assumption. The separation of transmitting and receiving antenna in Moon-based SAR is not regarded as monostatic, but quasi-bistatic mode. The Doppler parameters should be estimated under this condition. Preliminary analysis shows that error caused by stop-and-go assumption can seriously affect the focusing of Moon-based SAR imaging, and should be corrected or compensated.

We simulate to perform Earth radiation budget on the lunar surface. This simulation is to support the study of the Earth radiation budget from a new observation strategy. There are two instruments deployed on the lunar surface for monitoring the Earth outgoing radiation and detecting changes at global scale. As opposed to small instantaneous spatial coverage of the space-borne instruments, the instruments equipped on the lunar surface will have the observational scope of whole Moon-facing hemisphere to allow for a better quantification of the Earth outgoing radiation on the planetary scale. In this paper, we make simulations of equipping multispectral camera and active cavity radiometer on the lunar surface and analyze the characteristics of the Earth outgoing radiation acquired by the Moon-based platform.

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.

We investigate the ionospheric effect on the global change observation lunar-based synthetic aperture radar (GCOLB-SAR), which offers an unprecedented temporal resolution and spatial coverage. However, the GCOLB-SAR is confronted with a great challenge: an extra-long distance electromagnetic wave propagating through atmospheric and ionospheric effects. The ionospheric effect for the satellite-borne SAR is no longer applicable for the GCOLB-SAR due to its ultra-long synthetic aperture time. In this paper, we consider the temporal-spatial variation effects of the ionosphere on the imaging of GCOLB-SAR. For the purpose of the study, an L-band GCOLB-SAR observation is considered. The signal model based on curved trajectory for GCOLB-SAR is derived and the temporal-spatial variation background ionosphere is analyzed. Preliminary analysis shows that the range shift is on the order of hundreds of meters, while the azimuth shift is of tens of meters. Such geometric deviations apparently should be corrected or compensated for GCOLB-SAR.

More and more attention has been paid to taking the Earth as a whole for researching. Though space-borne and airborne platform have acquired various data from the Earth, the existing Earth observation system lack the ability of long-term continuous observation at a global scale. Three-polar regions include the Arctic, Antarctic and Tibet Plateau, which characterized by its large scale and need long-term observation. This will need large-scale, constant and long-term dynamic Earth observation system. Here we established a new platform, Moon-based Earth observation platform, which focuses on the observation of global scale scientific phenomena, turning out to be an ideal platform to study Three-polar regions environment comparison research. In this paper, we propose the Moon-based observation platform, and discuss the system parameters performance briefly and next focus on the potential applications of Three-polar regions.

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 the precision of the surface parameters calculated from satellite data gets higher and higher, it is still difficult to guarantee the temporal consistency and spatial continuity for large scale geoscience phenomena. Developing new earth observation platforms is a feasible way to improve the consistency and the continuity. As the only natural satellite of the Earth, the Moon has special advantages as a platform for earth observation. This paper mainly discusses the advantages and the potential applications of moon-based earth observation, as well as the train of thoughts of further researches.

Sea ice charts are provided operationally by the Canadian Ice Service (CIS) for the convenience of people in high-latitude regions. Hence, approaches to ice-water discrimination are in demand. An approach is proposed in this paper, in which no manual interpretation is involved in the selection of training data. It is done based on the discrepancy of incidence angle dependence between sea ice and open water. This approach has been tested on five synthetic aperture radar (SAR) images from Envisat Advance SAR (ASAR). The validation shows that the approach works fairly well in identifying large areas of open water with an average accuracy of 92.97%.

The seasonal snow over the Tibetan Plateau is an important parameter for the regional energy and water cycle. The snow there experience a different trend compared to that of the Northern Hemisphere in the past decades. Because of the shallow and patchy snow with a fast precipitation process and cloud occurrence, the traditional snow algorithm (18GHz and 27GHz gradient brightness temperature) experiences an insufficient accuracy there when employed the global coefficients. In this paper, the atmosphere condition was assumed to play a key role. Through the simulation, and the realistic atmospheric balloon measurement, the transmittance difference between Plateau and low land area was calculated, and the correspondent cloud influence was analyzed. As a result, we need to consider the cloud and water vapor effect, the method over the Tibetan Plateau needs to consider these two components, i.e. the existence of clouds over snow cover areas, and the atmospheric water vapor content, which typically separates high elevation regions from low lying landscapes.

Central Asia and Xinjiang, China are conjunct areas, located in the hinterland of the Eurasian continent, where the snowfall is an important supplement water resource. The induced seasonal snow cover is vita factors to the regional energy and water balance, remote sensing plays a key role in the snow mapping filed, while the daily remote sensing products are normally contaminated by the occurrence of cloud, that obviously obstacles the utility of snow cover mapping. In this paper, based on the daily snow product from Moderate Resolution Imaging Spectroradiometer (MODIS), a cloud removing method was developed by considering the regional snow distribution characteristics with latitude and altitude dependence respectively. In the end, the daily cloud free products was compared with the same period of eight days MODIS standard product, and validated with the help of the in-situ observations, revealing that the methodology are feasible with ~ 91% Recall, ~81% Precision and 85% Balanced F when snow depth is greater than 3cm. The cloud free snow products are kept as the same accuracy, recall and precision rate as the MODIS source products, while could provide higher temporal resolution, and more details over Center Asia and Xinjiang Province of China.

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.

It's a new concept to set up sensors on the Moon to observe the large-scale Earth science phenomena. In order to reveal its potentials and characteristics, this paper focuses on the simulative moon-based Earth observation which makes use of the Jet Propulsion Laboratory (JPL) ephemeris and transformations of relative reference frames. By simulating the observation scenes at different time, we analyze the observation conditions, including observation time series, geometry and effective coverage. The result shows that the moon-based Earth observation has advantages in wide swath, continuous observation and large effective coverage which contributes to the monitoring and understanding of large-scale Earth science phenomena.

Lake ice is an important component of the hydrosphere and cryosphere, especially over high-lat itudes regions of High Mountain Asia (HMA), the long-term changes of freezing and thawing time can directly reflect the changes of climate. Remote sensing technology provides unreplaceable complement for HMA where the lake ice phenology is almost no monitoring, except that of Qinghai Lake. Considering the microwave signature's sensitivity to the snow and ice phase changes, a method based on the freezing and thawing probability were proposed. The long time series of Swath Brightness Temperature (TB) data from satellite-based passive microwave sensor AMSR-E (Advanced Microwave Scanning Radiometer for EOS) and AMSR2 (The Advanced Microwave Scanning Radiometer 2) were employed to retrieval the lake ice status of four lakes with different areas, Qinghai Lake, Siling Co, Hala Lake and Aqqikkol Lake. The method was validated with the result from higher resolution of daily cloud-free MODIS (Moderate Resolution Imaging Spectroradiometer) image, the result shows that, for four selected lakes, the R-Square of start of freeze-up(FUS) is 0.80466, end of freeze-up(FUE) is 0.92197, start of break-up(BUS) is 0.83882 and end of break-up(BUE) is 0.96127, respectively.

Global change refers to changes in earth system function at a global scale including atmospheric and ocean circulation, hydrologic and biogeochemical cycles, and changes in resource, land use and others. Global change is characterized by its large scale and needing long-term observation. Moon is an ideal platform for observing global change. In order to reveal its potentials for observing of global change sensitive regions, this paper focuses on the coverage analysis of moon-based earth observation which makes use of the JPL ephemeris and generates observation Boolean matrixes of global change sensitive regions. The result shows that the moon based earth observation has advantages in wide swath, continuous observation and large effective coverage which contribute to the monitoring and understanding of global change.

This letter is aimed to compare the performance of canopy height models (CHMs) derived from airborne laser scanning (ALS) data and unmanned aerial vehicle (UAV) stereo images in the extraction of individual tree height and crown size. Treetops were identified using the local maximum algorithm from the Gaussian filtered CHMs. A parabola fitting was used to determine the crown size. Factors affecting the delineation results, such as point cloud density and the spatial distribution and growing status of trees, were analyzed. The results showed that the UAV stereo images, together with the ALS-derived digital elevation model (DEM), can achieve better performance than ALS data alone based on our data set. The match ratio between delineated and field-measured trees varied significantly, with the highest ratio of 66.94% obtained by UAV in the young aspen forest and the lowest ratio of 33.76% obtained by ALS in the old forest. Aside from the influence of point density, this letter also shed light on the important role that the spatial distribution and growing status of trees play in the delineation of individual trees. To conclude, integrating UAV stereo images with the ALS-derived DEM is effective in delineating individual tree attributes in small-scale windbreaks, which provides some suggestions for the future management of agriculture land.

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.

Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

The Goldstein filter is one of the most commonly used filters for synthetic aperture radar (SAR) interferograms. The level of noise after filtering is controlled by a filter parameter, “alpha,” the value of which is determined by pixels within the moving window. However, when there exist different features within a single filter window, especially along the border, the value of alpha as estimated from the pixels within the window can be inaccurate and this may result in blurred borders in filtered interferograms. This letter proposes a modified Goldstein filter based on the adaptive-neighborhood technique. The idea of this method is to filter each pixel of the interferogram within an adjusted filter patch. In this adjusted patch, the adaptive-neighborhood pixels retain the original phase values while the “background” pixels are replaced by the mean value of adaptive-neighborhood pixels. Then, the Fourier transform of the complex phase is applied to this adjusted filter patch. The difficulty of estimating the noise level near the borders of different features can be decreased using this new filtering method. The quantitative results from real data show that this newly developed method could reduce the phase noise efficiently while also outperforming the Goldstein, Baran and empirical mode decomposition (EMD) filters by preserving the edges in interferograms.

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 paper addresses built up information extraction from high resolution imagery from two satellites, ZY-1 02C and SPOT-5. The testing is part of a larger experiment that explores spectral, textural and morphological satellite image properties aiming to extract built up information for global built up mapping and characterization. In this work we test SPOT-5 and ZY-1 02C satellites for textural properties associated to built-up. The work is exploratory in nature. We compute image textures using a ”built-up presence index (Pantex)” algorithm. We then compare the texture values with a reference built-up map derived from building footprints map by computing the Receiver Operative Characteristics (ROC). We processed four bands: ZY-HR Panchromatic, ZY Panchromatic, and two “brightness” bands derived from the ZY Pan-sharpened, and SPOT Pan-sharpened. Pan-sharpened products combine both the panchromatic and the multispectral imagery. For each dataset we use two texture windows for a total of eight tests. The study is conducted on imagery covering the city of Firenze, Italy. The landscape is characterized by dense city centres as well as rural scattered settlement. The area is deemed suitable to benchmarking imagery for built up information related to human settlements of different densities. The test shows that the information from SPOT and ZY are generating similar result. However, spatial resolution of panchromatic bands show to have great importance over other factors in extracting built up information.

Using the brightness temperature data measured by an Advanced Microwave Scanning Radiometer (AMSR-E) and the in-situ measurement, this paper compared the accuracy and applicability of five snow-depth retrieval algorithms (Chang algorithm, GSFC 96 algorithm, AMSR-E SWE algorithm, the improved algorithm for Qinghai-Tibet Plateau and atmosphere-correction algorithm) in the regions of Xinjiang, Qinghai-Tibet Plateau, Inner Mongolia, Northeast China, Northwest China and the North China Plain. Generally, over the whole China area, the results of the study show that, the improved algorithm for Qinghai-Tibet Plateau shows a relatively higher accuracy, with a root-mean-square error (RMSE) of 9.16, 9.96 and 9.63cm and a mean relative error (MRE) of 59.77%, 52.79% and 48.47%. According to regional verification respectively, the accuracy algorithm for Xinjiang region is the GSFC 96 algorithm, the RMSE of which is 6.85cm~7.48cm; For Inner Mongolia, it is the QTP algorithm, the RMSE of which is 5.9, 6.11 and 5.46cm; and For Northeast China, it is also the Qinghai-Tibet Plateau algorithm, the RMSE of which is from 6.21cm to 7.83cm. None of the five algorithms is in a good quality for Northwest China and the North China plain regions. For the Qinghai-Tibetan plateau region, the author is not able to obtain the results of statistical verification due to a lack of actually measured data.

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.

The Goldstein filter is one of the most commonly used synthetic aperture radar (SAR) interferogram filters. This letter proposes a new method to find filter parameters of the Goldestein filter based on noise level derived by the empirical mode decomposition (EMD) method. The filtering parameter determined by this method has a definite physical meaning. We used bidimensional empirical mode decomposition (BEMD) to extract features of an interferometric phase image into multiple scales of spatial frequencies, called intrinsic mode functions (IMF). We constructed a pseudo-SNR (signal-to-noise ratio) with the given IMF component, then the new parameter was applied to the Goldstein filtering method in place of the original fixed value ascertained artificially. The results from simulation and real data show that the performance of the new algorithm outperforms the original Goldstein filter, and its enhanced version, the Baran filter. The quantitative evaluation also shows that modification based on the EMD proposed in our paper minimizes the loss of phase while still reducing the level of noise in an interferogram.

Numerous opportunities for advancement exist in the field of man-made target detection using synthetic aperture radar (SAR). With the development of SAR sensors, the high spatial resolution violates the validity of the zero-mean assumption, particularly for urban applications. In 2012, we refined the conventional azimuth stationarity extraction method by adopting a nonzero-mean model-the Rician distribution-to better adapt to the high-resolution SAR imagery of urban areas and improved the man-made target detection result significantly. However, the model cannot make full use of the multipolarization information because it is calculated with the amplitude data. This study extends the method by employing another nonzero-mean model-the multivariate normal distribution-to consider the scattering vector of polarimetric SAR (PolSAR) images. The proposed method achieves an excellent performance with overall accuracy of 88.76%. Two previous methods, including the PolSAR method using the zero-mean model and the single polarization method using the nonzero-mean model, are also involved in the comparisons. The effectiveness of applying nonzero-mean models to urban area SAR images is well demonstrated by experimental results.

Due to the lack of accuracy in the navigation system, deviations in the order of centimeters between the real trajectory and the measured one, called residual motion errors (RMEs), frequently appear in synthetic aperture radar (SAR) images. They usually cause azimuth defocusing and phase errors. For very high-resolution SAR imaging and repeat-pass SAR interferometry, the RME must be estimated and compensated. In the literature, the algorithms used to estimate such errors are mainly through interferometry. The error difference for the SAR image pair is calculated, and then the correction is applied either on one image, or alternatively fractions of it on both images. However, the accuracy of this approach is greatly deteriorated by the decorrelation and ground movements. In this paper, we associate the phase difference between two adjacent subaperture images with the second derivative of the RME, and propose a new algorithm to estimate the RME for individual SAR image. It exploits point-like targets distributed along the azimuth direction, and not only corrects the phase error, but also improves the azimuth focusing. Therefore, besides in airborne repeat-pass interferometry, the algorithm can also be applied in very high-resolution SAR imaging. Simulated and real SAR data are used to demonstrate its feasibility and accuracy. Its limitations and extensions are also discussed at the end of the paper.

With several advantages superior to low earth orbit SAR, such as high space resolution together with large range swath and short revisit interval, the moon based SAR (MB-SAR) could be a potential data source for global changes monitoring and environment change research. Due to the high stability and ease of maintenance, the novel system is competent for offering a long and continuous time series of remote sensing imagery. In this paper, we discuss the system performance briefly at the beginning, and next focus on its possible applications.

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.

Glacier, due to the sensitivity to the climate change, has become an important indicator for global change research, especially the glaciers in the Arctic, Antarctic, and the third pole (Qinghai-Tibet plateau). Remote sensing, which has a capability to acquire the regional information of glaciers quickly, has become an effective tool to monitor and study the change of glaciers. In this paper, combining the NECP/NCAR reanalysis climatic data, the long time-series and continental area change of the glaciers and their melted lakes in West Kunlun with different watersheds were comparative analyzed by using LandSat TM/ETM+images from 1991 to 2009, which is located in the northern margin of the Qinghai-Tibet plateau. The results show: 1) The total area of the glaciers in eastern West Kunlun reduced firstly then increased. Meanwhile the total area of the glaciers in western West Kunlun sustained decreased in recent 20 years. 2) There is a significant negative correlation between the area change of glaciers and the corresponding welted lakes in West Kunlun. 3) The area change difference of the glaciers in the eastern and western regions in West Kunlun is mainly induced by temperature.

The International Space Station Agriculture Camera (ISSAC), Sponsored by the National Aeronautics and Space Administration's (NASA) Education Office and developed by University of North Dakota, aims to demonstrate the applications of precision farming and natural resource management from the International Space Station (ISS). With a designed Ground Sampling Distance (GSP) of ~20 meters, an accurate geo-referencing algorithm is important for near-real time delivery of images that can be used immediately. One outstanding feature of ISSAC is its tilting capability in cross-track direction so that a wide area can be covered. At a relatively low altitude, the ISS, where the ISSAC will be hosted, experiences a large variation in attitude. Additionally, the optics window on ISS where the ISSAC sees through is not at the center of mass of the ISS. All these factors need to be taken care of in the algorithm. Evaluated by the commercial navigation software, Satellite Tool Kit (STK, Analytical Graphics, Inc.), the proposed algorithm can achieve theoretical accuracy within 0.5 meters.

Global climate change has caused sea level rise, and one of the most extremely consequences are the increased frequency and hazards of the storm surge disasters, therefore, how to effectively assess the risk of storm surge disaster is of importance to hazard reduction and mitigation. However, the storm surge forecast models have complex parameters, which computational inefficiency. Traditional large-scale assessment usually takes elevation-area method based on GIS software, which result in large errors. The present study is attempted to: (1) Select proper two-dimensional hydraulic storm surge inundation model. This model not only has the physical realism but simple and efficient. (2)The present study will focus on the method to extract the required surface parameters based on the remote sensing data to integrate remote sensing data into the model. This project aims to provide the theoretical basis and methodologies for flood risk assessment in coastal zone of China.

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.

Remote sensing is an effective tool in urban extent mapping and monitoring. In this study, a procedure to detect urban expansion is presented using SPOT5 panchromatic image with 2.5m resolution. First of all, the urban extents of different images are extracted with a PANTEX methodology, which use the anisotropic rotation-invariant textural grey-level co-occurrence measures as the presence of built-up areas. The change detection is performed on the two built-up index images using principal component analysis technique. Finally, the accuracy of this procedure is assessed with visual analysis results. The method shows good performance according to the overall accuracy of 91.18% (Kappa coefficient=0.8719).

The ecosystem of Tibetan Plateau is fragile and sensitive to global climate change. Land surface phenology detects the change of terrestrial ecosystems at the regional and global scale. In this study, we investigated the spatial and temporal variation of vegetation phenology in the Tibetan Plateau using GIMMS normalized difference vegetation index (NDVI) dataset from 1982 to 2003.The results show that the changing trends of phenological metrics varied strongly across the Tibetan Plateau during the study period. In the southwest of TP, the start of season(SOS) advanced and delayed in the east from 1982 to 2003. For the end of season (EOS), it advanced in the east and delayed in the west. Therefore, the length of growing season is shortened in the eastern region of TP, whereas it is extended in the southwest.

Land Surface temperature (LST) is a critical parameter in water and energy circle supporting the global climate research. The traditional optical/thermal remote sensing estimates LST under clear-sky conditions, while the microwave remote sensing provides the all-weather LST estimation capability. In this paper, we derivate a new LST retrieval algorithm based on the emissivity parameterization using the Advanced Microwave Scanning Radiometer - Earth Observation (AMSR-E), considering the atmospheric influence, to tackle the LST retrieval under the non-scattering rain and cloud condition. The new derived LST algorithm has a physical basis and the RMSE is 1.8K with the station validation over boreal forest area at Sodänkylä, Finland.

The motion of mountain glaciers on the Tibetan Plateau is one of the key parameters in the research of environmental changes, especially the surface velocity variation over a period of years. Due to the abominable geographical conditions of steep terrain and freezing temperature, it is hard to acquire enough information using traditional ground survey. Optical remote sensing images could be useless during some special period with too much cloud cover. The main objective of this paper is to evaluate the performance of the alternative method-tracking offsets from SAR images, and is trying to add to the lacking information of glacier monitoring in west Kunlun Mountain. Three complete and detailed maps of glacier flow velocity fields during 2007-2010 are obtained by procedures of offset detection using cross correlation in Fourier domain. We found that winter glacier motions on the north slope are averagely 1 cm/day faster than south slope, well corresponding to the local topography. Accuracy of the results could be evaluated based on the offsets extracted from the regions obviously stationary, like the flat ground or the bed rock. The potential deviation is on the order of 0.12-0.25 pixels, which is acceptable in such large scale.

Biomass, as an indicator of vegetation productivity, can evaluate the contribution of wetland vegetation to carbon sink and carbon source. Long time and quantitative biomass study can help to acknowledge and understand the global carbon balance and carbon cycle. RADAR, which can work all day/weather and can penetrate vegetation in some extent, can be used to retrieve vegetation structure information, even the biomass. Here, the RADARSAT-2 data was used to retrieve vegetation biomass in Poyang Lake wetland. Based on the canopy scattering model, which is based on radioactive transfer model, the vegetation backscatter characteristics at C band were studied and good relationship between simulation results and backscatter in RADATSAT-2 image were achieved. Using the backscatter model, pairs of training data (backscatter coefficients in HH, VV, HV polarization mode and polarization decomposed components) were built and were used to train the Back Propagation (BP) artificial neural network (ANN). The biomass was inversed using this ANN, and compared to the field survey. It shows that the combination of the canopy scatter model and polarimetric decomposition components can improve the inversion precision efficiently.

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.

Linfen area is dominated by a series of strike-slip active faults and is presently characterized by low or medium seismicity. The Multi-temporal Interferometry SAR (MT-InSAR) methodology provides high resolution assessment of surface deformations over long periods of observation. Stanford Method for Persistent Scatterers (StaMPS) includes a Permanent Scatterer InSAR (PS-InSAR) method and a Small Baseline (SB) method, which are the two categories of MT-InSAR. Over 100,000 PS velocity along the satellite Line Of Sight (LOS) was calculated using ENVISAT images acquired as well as the time series of LOS displacement based on SB method of StaMPS, which mainly show a successive dynamic pattern, i.e., mountains or sub-uplift zones keep rising, and basins or depressions remain subsiding. Moreover, uplift with large scale emerges in the region where Hancheng Fault runs crossing Luoyun Piedmont Fault, which is perhaps caused by the being obstructed of its right-lateral slip. As a result, crustal stress field is accumulating and seismic hazard becomes increasing.

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.

Over the western China, due to the influence from shallow snow, the passive microwave remote sensing algorithm show its imbecility when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this paper, we employee several ground time-series snow depth dataset and the corresponding satellite brightness temperature to evaluate the 36.5Ghz/l 8.7Ghz and high frequencies' (85Ghz or 89.0Ghz/18.7Ghz) ability for the shallow snow retrieval. From the analysis, we can get that the high frequencies has its potential for the shallow, which is suit for the snow situation over the high land, even with the atmosphere influence at high frequency. The result tell that the relative deep snow (>; 20cm) the Tbs at 36-18GHz are more reliable than that of high frequency, while over the shallow snow especially <;20cm), the pair 36-18 is insensitive, but the high frequency pair (89/85-18GHz) shows its obvious possibility.

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.

The velocity of glacier is the most important parameter in the study of glaciers and remote sensing is a powerful tool to calculate their surface velocities. Due to persistent cloud cover in this region, it is impossible to acquire enough optical images to provide measurements. However, measurement of the offsets between two SAR images is an effective way to determine surface velocity. In order to do this, offsets both in slant range and azimuth directions are derived from two SAR images. The movement of the glacier during the SAR data acquisition time is calculated after the global part of offsets has been removed by the polynomial fit method. The offsets used for removing the global part are selected on the basis of the Single-to-Noise ratio (SNR) and correlation in area without glaciers but with large topographic changes. The surface velocity of the whole glacier using SAR data will make a significant contribution to the study of glacier dynamics. The Kekesayi glacier can be divided into four parts, based on the velocity map. The results show that the surface velocity of the Kekesayi glacier is different on the different part of the glacier, and offset measurements are an effective method for the study of glaciers.

The Qinghai-Tibet Plateau is one of the most sensitive and fragile regions to climate changes, which has been found experiencing distinct warming trend in recent decades. We detected the vegetation greenness trends with the MODIS data in the Qinghai-Tibet Plateau during 2000-2009. The annual average normalized difference vegetation index (NDVI) increased 0.036 yr-1in this region. Statistically significant increasing trend appeared in about 20% of the vegetated area, primarily in the northeastern Plateau. Only 4% of the study region, mostly in the southern Plateau, showed significant decreasing NDVI trend.

A new integrated SAR signal processor, geo-coding and interferometric processing commercial software (GeoWatch software) has been developed. It supports high speed and large data processing based on ERS-1&2, ENVISAT, ALOS PALSAR, JERS-1, RADARSAT-1&2, TerraSAT and CSK SAR data, with user friendly GUI and parallel processing on the latest 64 bit Windows and UNIX/Linux systems and both personal computers and powerful multicore CPU+GPU cluster platforms. It has been applied to the DSM estimation in a mountain area and subsidence estimation at an airport using its application-oriented step-by-step pipeline processing, and the results showed the following key features: 1. Deformation monitoring coverage expansion from coherent points to distributed scatters; 2. Deformation monitoring period reduction and accuracy improving by multi-track SAR interferometric processing; 3. Truly ortho-rectified interferogram, image, digital surface model (DSM) and deformation map production.

Snow extent and snow depth (SD) are critical parameters in metro-hydrological models and are sensitive to the global climate change. Over the western China, due to the influence from shallow snow, changing seasonal permafrost and the sparse observation stations, the passive microwave remote sensing algorithm show its applicability when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this work, we employ one whole-winter Tb extracted from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) L2A swath dataset and the ground measurements of snow depth (SD) to analyse the snow microwave emission and gradient algorithm ability. The time series analysis shows that the Tb differences (36.5-18.7) and (36.5-10.7) are sensitive to relatively deep snow (>20cm), while the Tb differences (89.0-18.7) are sensitive to the occurrence of the new snow, with a promising correlation with shallow snow (<;15cm) and quickly decreasing (melting) snow depth, which suggest that a high frequency Tb difference could potentially be a good snow monitoring signal for the shallow snow cover over western China.

The Qinghai-Tibet Plateau is the largest lake area in China, with a total area of existing lakes of 36,900 km2, accounting for 52% of the total lake area of China. Lakes on the Tibetan Plateau play critical roles in the water cycle and ecological and environment systems of the Plateau. The obvious changes of lake area in Qinghai-Tibet Plateau can be directly expressed by the remote sensing images. Three periods of Landsat images, one period of topographic map and the meteorological materials in recent 50 years were collected to extract the changes. After analyzing the change trend of lake area, annual mean temperature and annual mean precipitation, it was found that the change of lake area has a close correlation to the change of annual mean temperature but not to the change of annual mean precipitation.

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.

This paper presents a new alteration mineral mapping method based on statistical analysis of spectra. First of all, this method processes a cluster of measurement data of spectral of field samples, in order to distinguish different sample area from the overall types. Second, the results of the clustering of different mineral alterations were established their respective discriminant functions. Thus, mapping major alteration type accords with the clustered reference spectra by given remote sensing images. Finally mapping further alteration types based on the discrimant function of second step, which lead to final alteration map. This method takes full account of the different combination of alteration types, as well as the regional differences of alterations, and the establishment of the discriminant function for alteration minerals is more scientific. Moreover, we access the reliability of mapping to a certain extent. The method applied to a study area of Baogutu in Xinjiang Province, which represent a good result.

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.

In satellite passive microwave measurements, the sky brightness temperature is a function of frequencies, sensitive to parameters such as water vapor content, liquid water (cloud and precipitation), oxygen, hydrometeors and atmospheric temperature. In order to investigate the atmospheric influence to the retrieval of snow parameters quantitatively, firstly, we combined the HUT (Helsinki University of Technology) snow emission model (except the atmosphere parameterization) and an atmosphere model to do theoretical simulation estimations. We indicate that the C and X band atmospheric influence could be ignored, while the atmosphere is a non-negligible absorber and emitter of microwave radiation at frequencies higher than 19 GHz. We also launched a 13-day experimental measurement in winter time over Sodankyla¿, Finland, with synchronous satellite (AMSR-E) and tower-based radiometer measurements, together with extensive in-situ atmospheric measurement dataset. The evaluation result indicates that the atmosphere plays a relative positive contribution (about 20K for 36.5GHz and 89.0/94.0GHz). The difference between satellite observation and point experiment comparison suggests conducting more physical model work with atmosphere contribution.

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.

Poyang Lake, which has a high level of biomass, is the largest freshwater lake in China with an area of about 3,000km2. Its wetland ecosystem has a significant impact on China's environment change. In order to investigate the biomass information of Poyang Lake wetland plants, the characteristics of wetland plants must be researched firstly. The MIMICS (Michigan Microwave Canopy Scattering) forest backscattering model was modified to accommodate the two scattering components of wetland plants. Using GoogleEarth as one of the references, a reasonable field sampling route was planned and carried out during from 2007-3-29 to 2007-4-3. Thus we can calculate the backscattering coefficient in MIMICS model using surveyed data. By analyzing of the output, it is found that height, gravimetric moisture content and leaf number density are the main three factors dominating the backscattering coefficients, in other words, the major scattering is from crown directly. At last, ANN (Artificial Neural Networks) method was used to retrieve the biomass information of Poyang Lake wetland. The biomass is 1.06 × 109kg in April, 2007.

By identifying temporarily stable natural reflectors or persistent scatterers (PS), PSInSAR (Persistent Scatterers for SAR Interferometry) technique can analyze this subset of pixels in SAR images, even with long temporal and space baselines, to get high accuracy deformation measurements. We implement the PSInSAR process that is briefly summarized in this paper and apply this method in Tianjin area to detect the deformation phenomena using ENVISAT ASAR images. Calibration of ASAR images helps us select more PSC and using calibrated backscattering coefficient threshold we can discard the pixels whose amplitude are relatively stable while whose backscattered signals are weak and incoherent. Results obtained by processing 14 images show the distribution and the relative deformation value of the displacement field. The estimated linear velocities of PS are not accurate enough because of the relatively small number of images.

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.

Using SIR-C/X-SAR that provided simultaneously images of Southern-China Sea at three microwave wavelengths, L-band quad-polarization, C-band quad-polarization and X-band with VV polarization, the present paper calculate and analyze the optimum polarization of the backscattering from the rough sea surface. The utmost applications of radar remote sensing of ocean come down to quantitative derivation of some oceanographic parameters instead of simple interpretation of patterns on the SAR image itself of ocean surface. Accordingly, we concern mainly the polarimetric parameters of return signals from sea surfaces relate some physical oceanographic variables. Since the backscattering matrix contains the backscattering information of moving sea surface, concentration should be on understanding the properties of this matrix alone. Meanwhile, polarization is a key to aim this goal. A physical approach is to study polarization optimization. The optimization result shows that an optimum polarization changes with position of sea surface. And as a heuristic derivation, we prove that there exist optimum polarimetric responses with orientations at all sea-wind conditions.

Five spacecrafts named in the Shenzhou series (SZ in abbreviation) have been launched during 1999 to 2003 in China. Earth observation is a major scientific mission of the SZ spacecrafts. SZ-3 carried a 34-band medium resolution spectrometer, and SZ-4 was equipped with multimode microwave sensors composing a microwave scatterometer, radiometer, and radar altimeter. Our research group made "spacecraft-aircraft-ground" synchronous measurements. This paper presents the review of the mission of SZ spacecrafts, and gives the results of data processing and environment and geoscience applications.

In order to provide relevant data and decision support information to the Beijing Olympic Organizing Committee and International Olympic Organizing Committee, we are building up an environmental remote sensing monitoring system for Beijing 2008 Olympic Game using multiresolution, multiband, multitemporal remote sensing method and virtual reality technique. This system has four main features: 1) a 10 years long term monitoring from 1999 to 2008; 2) a stereo observation from spaceborne, airborne and ground remote sensing means; 3) a large area monitoring from Olympic game sites, Beijing area to its surrounding areas; 4) comprehensive monitoring for ecological environment, engineering construction, environmental pollution, traffic situation etc. This system serves as a huge platform for dynamic Earth observing monitoring and virtual Olympic environment with annual report and seasonal report forms. This work presents a preliminary result of constructing the system for Beijing 2008 Olympic Game.

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 multiple model microwave remote sensor is an important part of the payload carried by Shenzhou IV spacecraft launched on 30 December 2002 and is also China's first experimental microwave remote sensing system operating in space. The system includes three kinds of microwave remote sensors, namely microwave radiometer, radar altimeter and radar scatterometer. The radiometer instrument provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles. Here we discussed the retrieval of land surface parameters by using the Shenzhou IV brightness data aimed at evaluating the performance of this microwave radiometer. The results shows that the microwave radiometer observations of Shenzhou IV can be used to study seasonal and interannual changes of land surface parameters. Although compared with other satellite passive microwave sensor, the radiometer of Shenzhou IV has it's limitation.

This work describes the application of neural networks to targets classification from multi-polarization ENVISAT-ASAR imagery. The used neural network is multilayer perception (MLP) with fast learning (FL), which is fully interconnected network. Accordingly, the training data sets may be taken from a known truth data in the ground. And finally, the results of proposed method are compared with that of the other classification ones, the in situ test data are from Zhaoqing in Guangdong Province of China.

There are some problems not to be resolved very well, when we merge images by the wavelet transform method. One problem is how many levels the original images should be decomposed to, in order to make fusion images obtain more information. Other problem is how to reduce the spectral distortions of the fusion images more efficiently, when we enhance the spatial resolution of the low-resolution images. We rightly select the number of wavelet decomposition levels by computing entropy of the fusion images. When performing wavelet reconstruction, we introduce the local correlation coefficient and set up the different thresholds at different levels of wavelet decomposition, in order to reduce the spectral distortions of the fusion images. In our experiment we merge three multispectral images with a panchromatic image of Quickbird data by our method. The results demonstrate that our method is a good fusion method to increase information and reduce spectral distortions.

Edge detection is important and key to image segmentation, information extraction, mapping etc. It is more difficult with SAR images than optical images, because of speckle. The aim of the paper is to show how boundaries between land and water can be detected from SAR images by using wavelet transform method, block-tracing algorithm and snake algorithm. The Ku-Band SAR image was obtained on 14 July 2003. The test area is near Huai River, located in city of Fuyang, Anhui Province. This SAR is made by Institute of Electronics, Chinese Academy of Sciences. Its azimuth resolution is 1.25 m and its range resolution is 1.2 m. To detect water edge, we apply a wavelet transform method to obtain the low-resolution image of the original image and suppress the speckle. Then we use a block-tracing algorithm to determine the coarse boundary between land and water and obtain a continuous edge. In the end, we use the snake algorithm to obtain the accurate edge. The result proves our method is a good method.

This paper presents the experimental results regarding changes in the dielectric properties of artificially moistened and salinized soils and on soil samples taken from a salt lake. The complex dielectric constants of soil samples were measured using a microwave network analyzer. We evaluated the real and imaginary parts of dielectric constants of artificially moistened and salinized soil samples prepared in the laboratory as a function of microwave frequency, salinity, and water content The frequency and the salinity of soils have little influence on the real part of dielectric constant /spl epsiv/'. The results show that, in the frequency range of 1-6 GHz, the imaginary part /spl epsiv/" has greater sensitivity to soil salinity. The dielectric constant measurements for soil samples collected in Jilantai Salt Lake are in agreement with the results of the artificially moistened and salinized laboratory soil samples. These dielectric measurements were subsequently compared with the backscattering coefficients extracted from a RADARSAT image (C-HH) that was acquired at same time with the soil sampling at the Jilantai Salt Lake area. We discovered that the correlation coefficient between /spl sigma//sup 0/ extracted from the RADARSAT image and /spl epsiv/" measured in the soil sample is 0.70. The correlation coefficient between /spl sigma//sup 0/ and the soil sample salinity is 0.69. This suggests that soil salinity has a significant contribution to the backscattering coefficient, /spl sigma//sup 0/ recorded in a SAR image. Consequently, a SAR image can be a useful tool for monitoring soil salinity.

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.

The major goals of the variable SARs were applications in ocean wave research and wave forecasting. Two-dimensional ocean wave spectra can be derived from SIR-C/X-SAR images by inversion of the SAR imaging mechanism. But for a composite sea surface, i.e., there exist local wind-generated waves, long wave (or swell) from far fetch, internal waves and/or wave breakers, to separate very certain wave information or wave spectrum is difficult in practice. Inversion of a 2D spectrum of internal wave surface of South-China Sea is computed from SIR-C/X-SAR image data. A new inversion method is presented which estimates two dimensional wave spectra. The scheme uses prior information from ocean wave models to add missing information and to account for the fact that the SAR ocean wave imaging mechanism model is not a one to one imaging process. The scheme takes the shape of different ocean wave components as information and uses the SAR information to adjust the mean wavelengths, propagation directions and waveheights of the prior spectrum. The scheme is based on stochastic models for both the measurement error and the prior distribution. The method can separate internal wave from wind-generated waves efficiently.

Some progress has been made in recent years in the development of microwave remote sensing for the salinity of soil. In order to further understand the influences of salinity on the dielectric properties of soil, soils with different texture are collected and made into samples with different salinity and moisture content. In this paper, we describe the use of open-ended coaxial probes for the measurement of dielectric properties of soil samples. We evaluate the microwave dielectric behavior of moist soil-salinity mixtures as a function of salinity, water content, and soil textural compositions, using the dielectric measurements of five soils ranging from sandy loam to silty clay at frequencies between 0.2GHz and 20GHz at room temperature. For solution of salt soil, especially at lower frequencies dielectric loss depends on the salinity. Furthermore, salinity has influence on static dielectric constant and relaxation time of soil solution. The real part of dielectric constant slowly declines with the increment of salinity. Therefore we cannot neglect the influences of salinity on microwave dielectric behavior of moist salt soil at P, L, C-band of microwave remote sensing. We add salinity factor into the Dobson semiempirical soil mixing model. The improved models yields values that correlate well with the results of dielectric measurement.

The research has been done to derive the soil moisture information at local scale by using single polarization, single frequency sensors such as ERS-1/2, Radarsat, and JERS-1. There is a need to develop a technique to estimate soil moisture information from these currently available data sources at both regional and local scales. In this study, a soil moisture change detection algorithm was developed for using the multi-temporal 50m resolution Radarsat SCANSAR image data. It was based on the theory model analysis results, with the correction of vegetation and incident angle effects. The relative soil moisture change value can be derived. The results were compared with in-situ measured soil moisture data from 3 different sampling sites at study area. The validation indicated our algorithm with RMSE error of 0.44 in estimating soil moisture change ratio.

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.

In the recent past there have been several technological developments which people have proceeded to use separately in isolation from one another. What we aim to discuss in this presentation is an attempt at synthesis. In this paper, we describe our pilot telegeoprocessing programme which is currently being carried out in the Institute of Remote Sensing Applications, Chinese Academy of Science, China.

A study on efficient visualization and real-time interactivity of large-scale scenes is discussed. Introducing parallel processing technology, we present a parallelizable strategy with the pipeline algorithm, realize this parallel algorithm based on shared-memory, and then apply this program to a test site, the Peking Olympic Games planning mixed scenes, including real-time rendering, dynamical texture loading, quick browsing and so on. The results show a running performance and real-time interactivity improvement of DEPS (Digital Earth Prototype System) when using this algorithm. The parallel program of this paper was developed and running on a Silicon Graphics multiprocessor, Onyx 3200, with four MIPS R12000 processors and InfiniteReality 3 graphic accelerator, under IRIX 6.5 operating system.

Real-time simulation technologies of a large-scale virtual scene are studied. Besides the building of LOD models (including terrain and cultural features) based on a viewpoint in the Peking Olympic Games Planning Regions as a demonstration area, we adopt Active Surface Definition, a real-time framework, to manage the terrain data, apply ClipTexture to manage the high-resolution textures efficiently, and utilize parallel processing technology to deploy the scene data dynamically, which result in higher fidelity and better simulation effects in the virtual scene we finally build up.

Sharp and repetitive stripes exist in many bands of Chinese Moderate Imaging Spectroradiometer (CMODIS) images because of gain and offset variations between neighboring forward and reverse scans of 22 detectors, which can distractingly and obstructively affect the interpretation and application of CMODIS data. In this letter, we suggest a method for destriping based on a finite-impulse response (FIR) filter in frequency domain and present the results obtained with the method on the experimental CMODIS images of heterogeneous targets. We also compare the method with some traditional destriping methods (e.g. lowpass filtering, moment matching) and display the result of comparison using one of the images. The destriping effectiveness is evaluated by means of appropriate indexes of quality in this letter.

This paper presents the experimental results regarding changes in the dielectric properties of artificially moistened and salinized soils and on soil samples taken from a salt lake. The complex dielectric constants of soil samples were measured using a microwave network analyzer. We evaluated the real and imaginary parts of dielectric constants of artificially moistened and salinized soil samples prepared in the laboratory as a function of microwave frequency, salinity, and water content. The soil moisture strongly affected the real part of dielectric constant /spl epsiv/'. The frequency and the salinity of soils have little influence on the real part of dielectric constant /spl epsiv/'. The imaginary part of the dielectric constant /spl epsiv/'' is strongly affected by both the salinity and moisture of soil samples, especially at low frequencies. When f<2 GHz, the imaginary part /spl epsiv/'' decreases with each increment of frequency; in contrast, at higher frequencies, /spl epsiv/'' tends toward a constant. The results show that in the frequency range of 1-6 GHz, the imaginary part /spl epsiv/'' has greater sensitivity to soil salinity. Therefore, C and L band are more suitable for soil salinity detection, but L band is better than C band. The dielectric constant measurements for soil samples collected in Jilantai Salt Lake are in agreement with the results of the artificially moistened and salinized laboratory soil samples. These dielectric measurements were subsequently compared with the /spl sigma//sup 0/ backscattering coefficients extracted from a RADARSAT image (C-HH) that was acquired at same time with the soil sampling at the Jilantai Salt Lake area. We discovered that the correlation coefficient between /spl sigma//sup 0/ extracted from the RADARSAT image and /spl epsiv/" measured in the soil sample is 0.70, whereas the correlation coefficient of /spl sigma//sup 0/ and /spl epsiv/' is 0.27. The correlation coefficient between /spl sigma//sup 0/ and the soil sample salinity is 0.69. This suggests that soil salinity has a significant contribution to the backscattering coefficient /spl sigma//sup 0/ recorded in a synthetic aperture radar (SAR) image. Consequently, a SAR image can be a useful tool for monitoring soil salinity.

Using the Vector Microwave Network Analyzer in broad band (from 0.2 to 18 GHz), a controlled laboratory experiment was conducted on artificially prepared wet saline soil samples. The complex dielectric constants of soil samples in a wide range of moisture and salinity contents were measured and the relationship to the moisture and salinity of the soils were analyzed. The experiment results shows that the imaginary part of the dielectric constant of wet soil is more sensitive to the salinity of soils at low frequency range (f<5 GHz). It indicates that L band or P band SAR can be more effective in soil salinity detection. Secondly, 41 soil samples were collected along a profile from a dried salt lake in late April of 2001. Their moisture and salinity vary along the profile differently. The Vector Microwave Network Analyzer measured the complex dielectric constants of the collected soil samples in laboratory. C band, HH polarization RADARSAT data were simultaneously acquired on April 23, 2001. The backscatter intensity taken from RADARSAT image correlates with the complex dielectric constant. The correlation coefficient between the backscatter intensity and the real part of the complex dielectric constant is 0.23, and the imagine part of the complex dielectric constant is 0.66. This result shows that SAR will contribute to salinity monitoring program.

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.

After the mosaic and geometric correction of 6 scenes of TM data, this paper analyzes the distributions of the strata exhibited in the Kalpin area, which underlie the precise interpretation of the structure. Integrated SIR-A data analysis and processing with field surveys, the information of secondary order structure and the borderline of geological bodies under shallow sediments are analyzed in this paper. The movements and the planar reappearance of the thrust sheets implies a Kalpin area strike sinistral slip of E-W and NE trend in a N-S trend compressional tectonic setting, which indicates the deformation frame and its kinematic process. Combined with the new advances of structural geology, the structure deformation features are described in detail, and strike-slip fault related models and GIS techniques are applied in the precise interpretation of the planar structures in Kalpin orogeny. Therefore, the structural shortening of sinistral slip is calculated precisely using the remote sensing data, which provide us with a new quantitative means for planar structure research.

The Advanced Land Observing Satellite (ALOS) will carry three advanced sensors for addressing many environmental issues, such as vegetation change, water resource management, disaster and earthquake mitigation, and cryosphere monitoring, in a broad range of earth science disciplines. PALSAR has many unique features in its L-band, multi-imaging mode (high-resolution, ScanSAR, and polarimetric), varying incident angles, and multi-polarization, compared to other SAR satellites operating in the orbit now or in the past. In our research proposal for ALOS research announcement, we focus on the use of PALSAR data in flood management and surface change monitoring for the Yangtze River, detection of surface subsidence in oil field, monitoring of coastal expansion and land cover changes in the Yellow River estuary, and study for surface deformation related to potential volcanic activities, palaeo-environment evolution, and snow and ice etc. Although the launch of ALOS in 2003 makes us unable to give a report of research result with ALOS data, we will present our research plan and expectation with PALSAR and other sensor data, and give some results of our study using other SAR data in relation to the issues being addressed in our research with ALOS data.

This paper makes three contributions. It clarifies the definition of edge preservation in SAR images. The definition of edge preservation is important for edge preservation evaluation of digital speckle filters. Only if the definition of edge preservation is reasonable, can the edge preservation evaluation be successfully performed. Second, an algorithm to evaluate edge preservation of digital speckle filters is proposed. Third, we analyze the reason why the enhanced Lee, enhanced Frost, gamma, and Kuan filters cannot effectively preserve edges. The main contribution in this paper is that we clarified the difference between edge preservation and edge point preservation. It is important to develop new methods to reduce speckle in SAR images, which can successfully preserve edges in smoothed images.

An effective algorithm using the multiscale technique for detecting edges in synthetic aperture radar (SAR) images is presented utilizing the empirical mode decomposition (EMD). Since EMD is a one-dimensional data processing method, EMD is employed to image in two directions: horizontally (0/spl deg/) and vertically (90/spl deg/) to obtain two directionally smoothed images at different scales. In this way, different scales of image data were obtained. In order to obtain edge points of single pixel width, the grad of different image scales is computed. The edge map is extracted from different image scales based on a uniform space condition. Application of this method to SAR images has shown that the effectiveness of the method is quite satisfactory.

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.

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.

This paper presents the modeling results of dielectric constants from different rice growth stages in Zhaoqing test site using Debye-Cole dual-dispersion model of vegetation. In the test site, two crops of early season rice and late season rice are planted in 1997 and 1996, and the growing period of rice crop is divided into five major stages: transplant, seedling developing, ear differentiation, heading and mature period. Based on the results, the paper discussed the influences from microwave frequency, gravimetric moisture content of rice, temperature, salinity and bulk density of dry vegetation materials on dielectric constant. The rice crops of different growth stages have the various dielectric constants, and the dielectric constants of early and late season rice crops show the diverse changes. The parameters, including in microwave frequency, gravimetric moisture content of rice, temperature and bulk density of dry vegetation materials, made the influence on dielectric constant, but salinity had no effect on the dielectric constant.

Urban 3D visualization is an exciting domain, with a growing number of important applications. Researches on VR (Virtual Reality) and GIS (Geographic Information System) provide strong technological support for real-time visualization of urban scenery. This paper focuses on the interconnection and interoperability between urban 3D visualization technology and GIS, including a scheme to exchange data between the GIS database and rendering engines for visualization, navigation and manipulation. A virtual scene of IRSA (Institute of Remote Sensing Applications), based in Beijing, China, which shows a fusion between urban 3D visualization and GIS is presented and realized.

Strong speckle noise of SAR interferogram is a great obstacle to phase unwrapping. In this article, we propose to filter this noise with a multiresolution analysis of the interferogram, the real part and the imaginary part of the interferogram are processed respectively. Several different probability density functions are modeled with the Pearson system of distributions, then the wavelet coefficients of the noiseless data are estimated with a Bayesian model, maximizing the a posteriori probability density function. The result is compared to that of another method and shows that the proposed method is powerful to interferogram speckle noise reduction.