Abstract:In order to accurately obtain spatial distribution information of citrus orchards and achieve adjustments in citrus cultivation structure, yield estimation, and resource management, focusing on three main citrus-producing regions in southern Jiangxi: Xinfeng County, Anyuan County, and Xunwu County, in addressing the challenge posed by frequent cloud cover and rainfall in the southern region, resulting in a scarcity of traditional optical images, Sentinel series data and the PIE-Engine platform were employed. Spectral features, vegetation water body index features, red edge band features, and texture 〖JP3〗features were constructed and optimized. Furthermore, the backscatter coefficients of time-series Sentinel-1 synthetic aperture radar (SAR) data were incorporated to collectively explore the recognition and extraction effects of different feature combinations on citrus plantations. Based on the random forest algorithm and the fusion of Sentinel-2 and temporal Sentinel-1 SAR feature recognition, the citrus planting area in Gannan was extracted. The results indicated that the average backscatter coefficient separation between citrus plantations and other ground features was most pronounced in May, September, and November, which were the critical periods for citrus identification and extraction. The involvement of index features and texture features in classification proved advantageous for classification effectiveness and enhanced classification accuracy. In comparison with single SAR features, as well as index and texture features, the overall accuracy of the classification results with the inclusion of temporal SAR features was 90.084%, with Kappa coefficient of 0.863. misclassification and leakage errors were relatively small, aligning with the actual distribution of land objects, signifying the availability and practicality of temporal SAR features. The research result can provide reference for the identification and extraction of citrus orchards in the cloudy and rainy southern regions, and it had certain application potential.
