| Title Of The Magazine |
International Journal of Applied Earth Observation and Geoinformation |
| Year Information |
2025 |
| KeyWord |
Crop classification, Red Edge Maize-Cotton Index, BFINet, Random forest, Ebinur Lake Basin |
| Authors |
Yan Xie, Hongwei Zeng b, Junbin Li, Hang Zhao, Qiangyi Yu, Bingwen Qiu,Shukri Ahmed, Bingfang Wu |
| Intro |
Accurate crop type classification remains challenged by dependence on ground-based samples and the presence
of ‘salt-and-pepper’ noise. This study presented a hierarchical parcel-level classification framework for multicrop
mapping, integrating the boundary-field interaction network (BFINet), the Red Edge Maize-Cotton Index
(RMCI), and a random forest (RF) classifier. BFINet enables precise delineation of agricultural field boundaries,
reducing the influence of non-cropland areas and minimizing pixel-level noise. RMCI is a new spectral index
designing for maize and cotton classification. The RF classifier is used to separate cropland into dominant crops
and minor crops, and subsequently to classify the minor crops into different crops. Applied to 2023 Sentinel-2
imagery in the Ebinur Lake Basin (ELB), the framework produced the region’s first detailed crop type map.
BFINet delineated agricultural parcels in ELB with IOU of 82.3 % and OA of 87.8 %. RMCI achieved an overall
accuracy (OA) of 98.6 % for maize–cotton separation, outperforming RF classifier (98.4 %). For minor crops, the
RF model attained an OA of 92.3 %. Compared to directly using standalone RF approach, The hierarchical
framework outperformed the standalone RF classifier in classifying all crop types in the ELB with F1 for cotton
(99.04 % vs. 87.28 %), maize (97.44 % vs. 96.22 %), wheat–maize (88.2 % vs. 82.0 %), grape (92.7 % vs. 89.0
%), and zucchini (94.4 % vs.75.6 %). This framework offers a scalable and accurate solution for crop mapping in
complex agricultural landscapes of arid regions. |
| Links To Magazines |
https://doi.org/10.1016/j.jag.2025.104765 |
English
中文