A Decision-Tree Approach to Identifying Paddy Rice Lodging with Multiple Pieces of Polarization Information Derived from Sentinel-1

Abstract

Lodging is one of the typical abiotic adversities during paddy rice growth. In addition to affecting photosynthesis, it can seriously damage crop growth and development, such as reducing rice quality and hindering automated harvesting. It is, therefore, imperative to accurately and in good time acquire crop-lodging areas for yield prediction, agricultural insurance claims, and disaster-management decisions. However, the accuracy requirements for crop-lodging monitoring remain challenging due to complicated impact factors. Aiming at identifying paddy rice lodging on Shazai Island, Guangdong, China, caused by heavy rainfall and strong wind, a decision-tree model was constructed using multiple-parameter information from Sentinel-1 SAR images and the in situ lodging samples. The model innovatively combined the five backscattering coefficients with five polarization decomposition parameters and quantified the importance of each parameter feature. It was found that the decision-tree method coupled with polarization decomposition can be used to obtain an accurate distribution of paddy rice-lodging areas. The results showed that: (1) Radar parameters can capture the changes in lodged paddy rice. The radar parameters that best distinguish paddy rice lodging are VV, VV+VH, VH/VV, and Span. (2) Span is the parameter with the strongest feature importance, which shows the necessity of adding polarization parameters to the classification model. (3) The dual-polarized Sentinel-1 database classification model can effectively extract the area of lodging paddy rice with an overall accuracy of 84.38%, and a total area precision of 93.18%. These observations can guide the future use of SAR-based information for crop-lodging assessment and post-disaster management.