关键词: 三大主粮作物, 农业保险, 交叉验证, 随机森林, 多季稻识别, 水稻完全成本保险, 保险绩效评价

Abstract:

Objective Rice, wheat, and corn, account for over 50% of government-subsidized premiums. Therefore, ensuring the authenticity of insurance underwriting data of three major staple crops is crucial for safeguarding fiscal funds and promoting the high-quality development of agricultural insurance. Currently, verifying the authenticity of underwriting data relies on remote sensing technology to achieve high-precision, high-efficiency, and low-cost crop identification. However, in the multi-season rice-growing areas of southern China, remote sensing identification still suffers from insufficient accuracy and delayed timeliness. This study, targeting the actual business needs of agricultural insurance, explores a "fast, accurate, and low-cost" identification method for multi-season rice in the hilly areas of Southern China. Cross-validation of underwriting data authenticity is conducted based on case studies, providing technical support for fiscal fund security and the high-quality development of agricultural insurance. Methods The high-resolution remote sensing data of China was integrated with internationally available data. First, a deep learning algorithm and high-resolution imagery were used to rapidly extract cultivated land parcels as classification units. Combined with field sampling and samples derived from high resolution imagery, rice classification and identification were performed on the Google Earth Engine (GEE) platform using Sentinel-1 radar data and Sentinel-2 multispectral data. Three methods, random forest, support vector machine, and classification and regression tree, were compared, and the optimal model result was selected for cross-validation of rice insurance data. Validation metrics included four categories: area difference (AD), the difference between remote sensing and insured area, cover ratio (CR), crop insurance coverage, overlapping ratio (OR), overlap of policy parcels, and crop proportion (CP), crop proportion within policy parcels. Results and Discussions Based on the cultivated land units extracted using deep learning, a classification feature set was constructed by integrating Sentinel-1 radar polarimetric signatures from March to October with Sentinel-2 multi-spectral reflectance and NDVI from July and August. The random forest model achieved 0.93 identification accuracy, meeting the accuracy and cost requirements for verifying mid- and late-season rice insurance data. However, due to the lack of multi-spectral data at key time phases required for early rice identification, its identification timeliness is poor and is only suitable for post-warning and deterrence the following year. Cross-validation results show that the county's overall insured area is basically the same as the remote sensing identification area, but there are significant differences in township scale: Among the 33 townships, 14 towns have AD more than 10 000 hm², indicating false insurance. As for CR, 10 townships have a crop insurance coverage rate of more than 1, 1 township has a CR less than 0.4, and another 2 townships have not carried out insurance. As for policy parcels, the 31 townships with insurance records, 10 did not provide plot data; among the 21 townships that provided data, 3 townships had an overlap rate of more than 40% for more than 50% of the policy plots, and 14 townships had an overlap rate of more than 40% for more than 20% of the policy plots. These results suggest that some areas may have problems such as false insurance, duplicate insurance, or non-standard operations, and regulatory authorities need to intervene and verify in a timely manner. Conclusions A technical system suitable is developed for rapid remote sensing identification and cross-validation with insurance data. Its feasibility in practical regulatory applications has been verified, providing effective methodological support for authenticity verification and precise regulation of agricultural insurance underwriting.

Key words: three major staple crops, agricultural insurance, cross-validation, random forest, multi-season rice identification, full-cost rice insurance, insurance performance evaluation