Crop Classification and Soil Moisture Retrieval over Agricultural Fields Using Time-Series Synthetic Aperture Radar Images

Crop monitoring is crucial for enhancing crop yields and ensuring food security. However, the diversity of climates and regional management policies present challenges for large-scale crop monitoring. The advent of high spatial-temporal resolution synthetic aperture radar (SAR) satellites offers a promising solution for extensive and long-term crop monitoring. This thesis explores the potential of time-series SAR data for monitoring two critical variables: crop type and soil moisture (SM). To leverage the statistical information in SAR images, new statistical descriptor, namely Object-based Generalized Gamma Distribution (OGΓD) features, were developed for time-series Sentinel-1 images. These features improved classification accuracy for corn, soybean, and wheat, achieving an overall accuracy of 96.66% and a Kappa coefficient of 95.34%, thereby facilitating further analyses such as crop rotation mapping. For early-season crop classification, the Local Window Attention Transformer (LWAT) was applied to RADARSAT Constellation Mission (RCM) and Sentinel-1 data. RCM with compact polarization outperformed Sentinel-1 with dual polarization during early crop stages, achieving comparable accuracy with the fusion of RCM and Sentinel-1 data. The optimal time for early-season crop mapping was determined to be six weeks after seeding, corresponding to the stem elongation stage of crops. To enhance the reliability for SM estimation in agricultural regions, a polarimetric decomposition-based change detection (PDCD) algorithm was proposed for compact polarization SAR data. It eliminates volume scattering from the backscattering, resulting in more accurate SM estimation. The PDCD method achieved a Root Mean Square Error (RMSE) of 5.058 Vol.% and 4.309 Vol.% in RCH and RCV polarizations, respectively. Consistent trends were observed when comparing time-series estimations with reference data, effectively reflecting rainfall events. Additionally, the retrieval depth of SM for multi-frequency and multi-polarization SAR was investigated using theoretical simulation and in-situ data evaluation. The theoretical penetration depth of L-band SAR ranges from 3 cm to 30 cm, which is deeper than C-band SAR, which penetrates only the top surface soil. Based on polarimetric features and in-situ data, the retrieval depth of L-band ALOS-2 was determined to be between 5 cm to 20 cm, while C-band RCM achieved lower accuracy for all depths due to its sensitivity to vegetation coverage.