2026-10-12T03:29:27Z https://www.scidb.cn/oai

10.57760/sciencedb.IGA.001d2 2026-08-31T11:24:04Z

2026-08-31 XDA28100502 - Research Paper on Crop Growth Monitoring Technology doi:10.57760/sciencedb.iga.001d2 en This dataset adopts time-series Sentinel-2 satellite remote sensing data combined with field survey data of typical ground objects in the Sanjiang Plain. Characteristic bands of major crops and surrounding typical land cover types are screened. The random forest classification algorithm is applied to extract a remote sensing monitoring dataset of the planting distribution of staple crops (rice, corn and soybean) in the Sanjiang Plain from 2020 to 2022. Verified by field survey samples, the overall accuracy of crop classification in 2020, 2021 and 2022 reaches 95.18%, 95.0% and 94.5% respectively, with corresponding Kappa coefficients of 0.924, 0.925 and 0.919. Crop planting distribution; Sentinel-2; Random forest; Sanjiang Plain; Remote sensing monitoring PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank