Optimal feature selection and crop extraction using random forest based on GF-6 WFV data.

For precision agriculture, agricultural development planning, and regional food security, accurate crop mapping is essential. The GF-6 satellite offers novel approaches for crop identification. As Jinzhou, Hebei province, China, comprises a mix of grain and fruit trees, this research has selected the area for a case study. First, spectral, index, and texture features are combined in various classification feature schemes. Second, the random forest algorithm is employed to ascertain the significance of these features. Optimal features are then chosen to accurately identify winter wheat, summer maize, and pear orchards. Concurrently, land cover for wheat and maize seasons is mapped. The results demonstrate that: (1) multisource features considerably enhanced the accuracy of crop classification based on GF-6 WFV data. Among these, index features have the greatest contribution, followed by spectral features, while the contribution of texture features, is almost negligible. (2) The classification effect utilizing random forest feature optimization is superior, with an overall accuracy of 94% and a kappa coefficient of 0.91. The extracted land area for each scheme exhibits an accuracy of over 70% when compared to the statistical area. (3) The critical time phases for extracting winter wheat, summer maize, and pear orchards are identified as, in sequence, the leaf-out and jointing stage, the tasselling stage, the leaf-fall stage, fruit development, and the first flowering/full flowering stages. Based on GF-6 WFV data, these research findings will offer a technical reference for the identification of grain crops and the extraction of pear orchards. [ABSTRACT FROM AUTHOR]