Graph neural networks (GNNs) are gaining popularity in diverse application domains and growing in complexity.As a result, it is crucial to achieve high-performance GNN execution.Among various techniques, algebraic transformations, including operator reordering and operator fusion, have been successfully applied to improve the computation and memory access efficiencies of DNN models.However,

The uncertainty inherent in validating satellite-derived soil moisture (SM) products is significantly attributed to the spatial underrepresentation of in situ SM measurements. The main reason for this phenomenon is the varying environmental conditions (called as environmental heterogeneity) within the satellite footprint. To better understand this issue, we assessed the spatial representativeness of in situ SM from 383 strictly screened stations worldwide relative to the coarse-resolution (~0.25°) satellite footprint and analyzed the effects of four environmental factors (i.e., soil texture, land cover, elevation, and vegetation coverage) using the extended triple collocation (ETC) technique. Results show about 63% of the sites have satisfactory levels of spatial representativeness (ETC derived correlation coefficient ⩾0.7). Land cover is the foremost factor affecting the spatial representativeness of SM sites. The in situ SM can better represent the true variability of SM when the proportion of land cover types where the site is located is higher or there are fewer land cover types within the satellite pixels.

The spatial resolution of existing satellite soil moisture products is very coarse (~25 km), and thus there is usually significant spatial heterogeneity in the land surface covered by satellite footprints. However, the effects of spatial heterogeneity on satellite soil moisture products are largely under-studied previously. The study firstly evaluated seven satellite soil moisture products comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA) and FY-3C at a global scale using the extended triple collocation (ETC) method. Then, the skills of these products under a wide range of surface heterogeneity including heterogeneity in vegetation coverage, terrain, land cover, and soil texture were ascertained. The results indicate: (1) SMAP-IB and SMAP DCA products generally outperform others, followed by SMOS-IC and SMAP MTDCA which also exhibit satisfactory performance; (2) heterogeneity in vegetation coverage, terrain, and land cover generally decreases the R value of satellite soil moisture products, while heterogeneity in soil texture has an insignificant effect on product skills; (3) L-band products demonstrate greater stability compared to C/X-band datasets across various surface heterogeneity.

Satellite soil moisture products have great potential for many applications, such as drought monitoring and landslide warning. However, these applications often require accurate and continuous soil moisture records, and the missing values in satellite soil moisture datasets often hamper the usefulness of these products for such applications. This study firstly proposed and compared three approaches, i.e., linear regression, linear rescaling, and random forest to fill the missing values in the SMAP soil moisture products in both temporal and spatial dimensions, based on the seamless ERA5 data from 2016 to 2019. Then, a total of twelve auxiliary data were incorporated into the training datasets of random forest to improve the accuracy of gap-filled SMAP data. Finally, the gap-filled SMAP data were compared with the original SMAP data and validated by in situ measurements from 1071 sites worldwide. The results indicate: 1) when using only the ERA5 datasets, the random forest performs better than linear regression and linear rescaling methods in the training phase, but its skill degrades noticeably in the validation phase; 2) by adding the auxiliary data, the performance of random forest improves significantly in the validation phase; 3) the gap-filled SMAP data maintain or even exceed the accuracy of the original SMAP soil moisture, demonstrating the effectiveness of the proposed gap-filling method.

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

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

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

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

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

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

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

The SMAP (soil moisture active passive) which is one of the satellites that specifically designed for soil moisture monitoring, was launched on 31 January 2015. Recently, the SMAP radiometer soil moisture product has been released to the public. It is very urgent to evaluate the reliability of this product before it can be widely used in hydrometeorological studies. In the study, we carried out an initial evaluation of SMAP radiometer soil moisture product against in-situ measurements from three networks. The three networks cover different land surface conditions, including two dense networks established in United States and Finland, and one sparse network set up in Romania. The results show that the SMAP soil moisture product agrees very well with the in-situ measurements although it sometimes exhibits dry or wet bias at different network regions. The overall ubRMSE of SMAP product is 0.036 m3m-3, well within the mission requirement of 0.04 m3m-3. Considering the algorithms are still under refinement, it can be reasonably expected that applications such as climate modeling and flood forecasting will benefit from the SMAP passive soil moisture product.

Remote sensing of soil moisture in a bistatic mode has attracted increasing attention in recent years. This paper investigates the bistatic radar response of soil moisture and surface roughness at L-band by using the advanced integral equation model (AIEM). To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, were evaluated using a defined sensitivity index. Results show that sensitivity is enhanced in a bistatic mode compared to the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Among the combinations, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture, and thus seems to be a good candidate for sensing soil moisture in bistatic configuration.

A new soil moisture retrieval algorithm developed in this paper, using C- and K-band microwave radiometer channels of Ocean Salinity Satellite (OSS). In this new algorithm, K-band(23.8GHz) brightness temperature (BT) is used to estimate land surface temperature, and C-band BT in H polarization used to retrieve soil moisture by τ - ω model, in which soil roughness (h) and vegetation parameters (τ) are combined in a single factor for their similar change trends with BT. The validation is done using AMSR-E data of the same channels with Naqu soil moisture monitoring network data in the central Tibetan Plateau, result shows the new algorithm has very good accuracy, at correlation coefficient, bias and RMSE.

This letter investigates the bistatic radar response of soil moisture and surface roughness of bare soil surfaces at L-band using the advanced integral equation model (AIEM). It focuses on the use of bistatic geometries away from the specular region. To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, are evaluated using a defined sensitivity index. The results show that sensitivity is enhanced in a bistatic mode compared with the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Moreover, the forward region is preferred to soil moisture sensing regardless of the surface correlation function. Among the combinations investigated, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture response, and thus seems to be a good candidate for soil moisture sensing in bistatic configuration.

The Soil Moisture Active Passive (SMAP) mission, which is the newest L-band satellite that is specifically designed for soil moisture monitoring, was launched on January 31, 2015. A beta quality version of the SMAP radiometer soil moisture product was recently released to the public. It is crucial to evaluate the reliability of this product before it can be routinely used in hydrometeorological studies at a global scale. In this paper, we carried out a preliminary evaluation of the SMAP radiometer soil moisture product against in situ measurements collected from three networks that cover different climatic and land surface conditions, including two dense networks established in the U.S. and Finland, and one sparse network set up in Romania. Results show that the SMAP soil moisture product is in good agreement with the in situ measurements, although it exhibits dry or wet bias at different network regions. It well reproduces the temporal evolution and anomalies of the observed soil moisture with a favorable correlation greater than 0.7. The overall ubRMSE (unbiased root mean square error) of SMAP product is 0.036 m3· m-3, well within the mission requirement of 0.04 m3· m-3. The error sources of SMAP soil moisture product may be associated with the parameterization of vegetation and surface roughness but still needs to be tested and confirmed in more extent. Considering that the algorithms are still under refinement, it can be reasonably expected that hydrometeorological applications will benefit from the SMAP radiometer soil moisture product.

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

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

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

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

Surface soil moisture is an important parameter in draught monitoring and crop yield estimation, it is important to obtain spatial-temporal soil moisture information in large range. Microwave signal is much related to dielectric constant of object observed, and soil dielectric constant is determined by soil moisture, which was the basis of the use of microwave remote sensing technology for soil moisture monitoring. To solve the transformation of soil moisture and soil dielectric constant, the Dobson semi-empirical model was used to build a simulated database, and then the Hallikainen formula calibrated by the least square regression method at 1.26/1.4/3.2/5.3/6.9 and 9.6GHz frequency-points were performed to set up the simplified models to transform the real part of the dielectric constant to the soil volumetric moisture content. The validations were performed shows that the simplified models have good accuracy and practicability.

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

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

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

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

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

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

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

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

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

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