Exploring the Research Progress of SAR Remote Sensing in Monitoring Rice Planting Area

Abstract

As global food security issues become increasingly prominent, accurate monitoring of rice planting areas is particularly important as it is a major food crop. Radar remote sensing has unique advantages in rice planting monitoring due to its all-weather and all-day imaging capabilities. This paper explores the application of SAR remote sensing in rice planting area monitoring and reviews two types of methods that are currently widely used. This paper concludes that the time series-based method has high computational efficiency, but it is difficult to meet the requirements of long-term monitoring; there are many branches of machine learning methods, among which the accuracy of supervised classification and deep learning methods is guaranteed, but there are problems of large computational workload and poor migration ability. In general, the current problems of SAR monitoring of rice planting areas mainly include complex data processing, limited classification accuracy, and low standardization. Future research should focus on improving the versatility of algorithms, enhancing data fusion capabilities, and promoting the widespread application of radar remote sensing in agricultural remote sensing.