Evaluating Multi-Temporal Sentinel-1 and Sentinel-2 Imagery for Crop Classification: A Case Study in a Paddy Rice Growing Region of China
Abstract
Highlights What are the main findings? The decomposition parameters mv derived from the dual-polarization model-based decomposition can effectively discriminate different crop types. Multi-temporal optical data with low cloud cover can effectively support crop classification. Incorporating dual-polarimetric SAR data further enhances the classification accuracy, particularly for rice and corn. What is the implication of the main finding? A practical classification strategy was proposed for crop type identification. A 10-m-resolution thematic map was produced to classify crops in the study area. Abstract Information on crop planting structure serves as a key reference for crop growth monitoring and agricultural structural adjustment. Mapping the spatial distribution of crops through feature-based classification serves as a fundamental component of sustainable agricultural development. However, current crop classification methods often face challenges such as the discontinuity of optical data due to cloud cover and the limited discriminative capability of traditional SAR backscatter intensity for spectrally similar crops. In this case study, we assess multi-temporal Sentinel-1 and Sentinel-2 Satellite images for crop classification in a paddy rice growing region in Helonghu Town, located in the central region of Xiangyin County, Yueyang City, Hunan Province, China (28.5° N–29.0° N, 112.8° E–113.2° E). We systematically investigate three key aspects: (1) the classification…
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