Precision Crop Identification via Integrated Spatial- Temporal-Spectral Remote Sensing Feature

Agriculture is the key foundation of the national economy, impacting livelihoods and security. Accurate crop distribution data is essential for guiding agricultural production and addressing food security issues. This study analyzes remote sensing feature extraction using spatial, temporal, and spectral information, with Jilin Province and Yitong County as the focus. Based on remote sensing image data from Sentinel-2, Landsat-8, and Landsat-7 covering the entire growth cycle of vegetation, feature space was constructed and optimized. Four commonly used pixel-based and object-oriented classification methods, as well as deep learning methods, were used to extract crop planting information in the test area. Finally, by validating and comparing the crop identification accuracy of five classification methods, the method with the highest classification accuracy and best overall mapping effect was applied to the fine identification of crops in Jilin Province from 2002 to 2022. The driving force analysis was conducted using the geographical detector. The research results show that: ① The decision tree had the highest classification accuracy at 93.66% with a Kappa coefficient of 0.93. ② Corn and rice areas in Jilin showed a rising trend, with corn increasing by nearly 15%. ③ Changes in crop areas were significantly affected by vegetation coverage and proximity to residential areas, with q values above 0.5. The study provides a replicable and efficient remote sensing monitoring approach tailored for Jilin Province and comparable areas. This method not only facilitates extensive agricultural surveillance but also delivers robust technical assistance for precision agriculture and land resource management. Future work will focus on quantifying the influence of individual feature variables on classification accuracy and exploring combinations of different classification methods to enhance performance