Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Geography

Supervisor

Wang, Jinfei

Abstract

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.

Summary for Lay Audience

Monitoring crops is essential for increasing food production and ensuring food security. However, different climates and farming practices make it challenging to monitor crops on a large scale. The development of advanced satellite technology, specifically synthetic aperture radar (SAR), offers a new way to monitor crops effectively over large areas. This thesis investigates how time-series SAR data can be used to identify crop types and measure soil moisture (SM), which are two important aspects of agriculture.

Firstly, to achieve robust crop type identification using SAR images, statistical information of time-series Sentinel-1 images was used. The method significantly improved the accuracy of identifying different crop types, achieving an accuracy of 96.66%. This improvement helps in mapping crop rotations, such as the common corn-soybean rotation found in the study area.

Secondly, for identifying crops in the early growing season, a new deep-learning method was used on images from the RADARSAT Constellation Mission (RCM) and Sentinel-1 satellites. The RCM data, which includes a special type of polarization, performed better in the early stages of crop growth. The best time for early-season crop mapping was found to be six weeks after planting.

Additionally, to better estimate SM using SAR data, a new technique called polarimetric decomposition-based change detection (PDCD) was developed. This method removes certain types of signal interference from crop canopies to provide more accurate SM predictions. The PDCD method showed strong performance, accurately tracking SM changes in response to weather events like rainfall.

Finally, the study explored how deep SAR signals can penetrate the soil to measure moisture. The L-band SAR could penetrate deeper, between 5 cm and 20 cm, making it better suited for measuring moisture deeper in the soil. The C-band SAR, on the other hand, could only measure moisture at shallow depths and was less accurate when plants covered the soil.

By developing new methods to analyze satellite data, this thesis enhances the ability to accurately identify crop types and measure SM. These advancements can lead to better farming practices, more efficient use of resources, and increased food production to meet the growing global demand.

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Available for download on Sunday, November 01, 2026

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