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

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

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