Application Of Polarimetric SAR For Surface Parameter Inversion And Land Cover Mapping Over Agricultural Areas

In this thesis, novel methodology is developed to extract surface parameters under vegetation cover and to map crop types, from the polarimetric Synthetic Aperture Radar (PolSAR) images over agricultural areas. The extracted surface parameters provide crucial information for monitoring crop growth, nutrient release efficiency, water capacity, and crop production. To estimate surface parameters, it is essential to remove the volume scattering caused by the crop canopy, which makes developing an efficient volume scattering model very critical. In this thesis, a simplified adaptive volume scattering model (SAVSM) is developed to describe the vegetation scattering as crop changes over time through considering the probability density function of the crop orientation. The SAVSM achieved the best performance in fields of wheat, soybean and corn at various growth stages being in convert with the crop phenological development compared with current models that are mostly suitable for forest canopy. To remove the volume scattering component, in this thesis, an adaptive two-component model-based decomposition (ATCD) was developed, in which the surface scattering is a X-Bragg scattering, whereas the volume scattering is the SAVSM. The volumetric soil moisture derived from the ATCD is more consistent with the verifiable ground conditions compared with other model-based decomposition methods with its RMSE improved significantly decreasing from 19 [vol.%] to 7 [vol.%]. However, the estimation by the ATCD is biased when the measured soil moisture is greater than 30 [vol.%]. To overcome this issue, in this thesis, an integrated surface parameter inversion scheme (ISPIS) is proposed, in which a calibrated Integral Equation Model together with the SAVSM is employed. The derived soil moisture and surface roughness are more consistent with verifiable observations with the overall RMSE of 6.12 [vol.%] and 0.48, respectively

Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

In this paper, we analyze the robustness of the parameter inversion provided by general polarimetric model-based decomposition methods from the perspective of a quantitative application. The general model and algorithm we have studied is the method proposed recently by Chen et al., which makes use of the complete polarimetric information and outperforms traditional decomposition methods in terms of feature extraction from land covers. Nevertheless, a quantitative analysis on the retrieved parameters from that approach suggests that further investigations are required in order to fully confirm the links between a physically-based model (i.e., approaches derived from the Freeman–Durden concept) and its outputs as intermediate products before any biophysical parameter retrieval is addressed. To this aim, we propose some modifications on the optimization algorithm employed for model inversion, including redefined boundary conditions, transformation of variables, and a different strategy for values initialization. A number of Monte Carlo simulation tests for typical scenarios are carried out and show that the parameter estimation accuracy of the proposed method is significantly increased with respect to the original implementation. Fully polarimetric airborne datasets at L-band acquired by German Aerospace Center’s (DLR’s) experimental synthetic aperture radar (E-SAR) system were also used for testing purposes. The results show different qualitative descriptions of the same cover from six different model-based methods. According to the Bragg coefficient ratio (i.e., β ), they are prone to provide wrong numerical inversion results, which could prevent any subsequent quantitative characterization of specific areas in the scene. Besides the particular improvements proposed over an existing polarimetric inversion method, this paper is aimed at pointing out the necessity of checking quantitatively the accuracy of model-based PolSAR techniques for a reliable physical description of land covers beyond their proven utility for qualitative features extraction.This work was supported by National Natural Science Foundation of China (No. 41531068, 41371335, and 41274010), the Spanish Ministry of Economy and Competitiveness and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P, and China Scholarship Council (No.201406370079)

Exploration of Data Fusion between Polarimetric Radar and Multispectral Image Data

Typically, analysis of remote sensing data is limited to one sensor at a time which usually contains data from the same general portion of the electromagnetic spectrum. SAR and visible near infrared data of Monterey, CA, were analyzed and fused with the goal of achieving improved land classification results. A common SAR decomposition, the Pauli decomposition was performed and inspected. The SAR Pauli decomposition and the multispectral reflectance data were fused at the pixel level, then analyzed using multispectral classification techniques. The results were compared to the multispectral classifications using the SAR decomposition results for a basis of interpreting the changes. The combined dataset resulted in little to no quantitative improvement in land cover classification capability, however inspection of the classification maps indicated an improved classification ability with the combined data. The most noticeable increases in classification accuracy occurred in spatial regions where the land features were parallel to the SAR flight line. This dependence on orientation makes this fusion process more ideal for datasets with more consistent features throughout the scene.http://archive.org/details/explorationofdat1094517375Civilian, Department of the NavyApproved for public release; distribution is unlimited

Determination of Soil Moisture and Vegetation Parameters from Spaceborne C-Band SAR on Agricultural Areas

Soil moisture is an important factor influencing hydrological and meteorological exchange processes at the land surface. As ground measurements of soil moisture cannot provide spatial-ly distributed information, remote sensing of soil moisture using Synthetic Aperture Radar (SAR) offers an alternative. To derive soil moisture from vegetated areas with SAR, the influ-ence of vegetation parameters on SAR backscatter must be considered, though. The first part of the study analyses the potential to use a qualitative soil moisture index from ERS-SAR with high spatial resolution that can be used without ground truth soil moisture and vegetation data. The index ranges from low to high soil moisture instead of giving absolute soil moisture values. The method is applied to agricultural areas in the catchment of the river Rur in Germany. The soil moisture index represents wetting and drying tendencies well when compared to precipitation records and behaves like in-situ soil moisture regarding its variabil-ity. The analysis of spatial patterns from the soil moisture index by using semivariograms re-veals that differences in management that result for example in differences in evapotranspira-tion from one to the next agricultural field, are the only influence on spatial patterns of soil moisture in the Rur catchment. This study confirms the applicability of a high-resolution soil moisture index for monitoring soil moisture changes and to analyze spatial soil moisture pat-terns. The soil moisture index could be used as input to hydrological models and could substi-tute antecedent precipitation, which needs precipitation stations, as a proxy to soil moisture. The second part of the study examines the capability of dual-polarimetric C-Band SAR data with high incidence angles from the Sentinel-1 satellites to derive soil moisture and vegetation parameters quantitatively. A processing scheme for Sentinel-1 Level-1 data is presented to produce images of different SAR observables that are compared to extensive ground meas-urements of soil moisture and vegetation parameters. It shows that soil moisture retrieval is feasible from bare soil and maize with an RMSE of 7 Vol%. From other land use types, dif-ferent vegetation parameters could be retrieved with an error of around 25 % of their range, in median. Neither soil moisture nor vegetation parameters could be derived from grassland and triticale due to the influence of the thatch layer and the missing of a clear row structure. Both grassland and triticale are in contrast to the other crops not sown in rows on our research fields. The analysis has shown that the incidence angle is of main importance for the capability of C-band SAR to derive soil moisture and that the availability of at least one co- and cross-polarized channel is important for the quantitative retrieval of land surface parameters. The dual-pol H2α parameters were not meaningful for soil moisture and vegetation parameter re-trieval in this study