Application of GEE and multi-source remote sensing in automated sample generation and classification of winter wheat：Taking Handan City as an example

doi:10.14088/j.cnki.issn0439-8114.2025.09.034

Abstract

Abstract: Taking Handan City as the study area, a method for automatic generation of training samples and classification of winter wheat was constructed based on the Google Earth Engine (GEE) platform and multi-source remote sensing data.By integrating SNIC segmentation, phenological feature screening, and the random forest algorithm, a multi-feature combination scheme incorporating spectra, vegetation indices, and texture was established. The results showed that feature combination ③ (spectra + vegetation indices + texture) achieved the best extraction performance, with the lowest relative error for three consecutive years (0.21% in 2023, 1.33% in 2024, and 0.44% in 2025), and the overall accuracy and Kappa coefficient improved annually.Based on this scheme, spatial distribution maps of winter wheat planting in Handan City from 2023 to 2025 were generated, revealing a distribution pattern concentrated in the eastern plains and sparse in the western mountainous areas. Growth monitoring indicated that the light, temperature, precipitation, and humidity conditions during the entire growth period of winter wheat in Handan City in 2025 were well-matched, the NDVI increment was predominantly favorable, and the overall growth status was better than that in 2023 and 2024. The automatic generation sample method demonstrated good applicability and stability in large-scale crop classification.

Key words: Google Earth Engine (GEE), multi-source remote sensing, winter wheat, sample generation, crop classification, Handan City

CLC Number:

P237

LI Ya-qiang, CAO Jun-tao, CHANG Yu-fei, MENG Cheng-zhen, ZHANG Jun, DAO Jian, ZHAO Chun-lei, QUAN Chang. Application of GEE and multi-source remote sensing in automated sample generation and classification of winter wheat：Taking Handan City as an example[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(9): 220-228.

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