Agricultural greenhouse extraction using multi-temporal spectral feature with decision tree algorithms in Jilin Province, China

Agricultural greenhouses (AGHs) are one of the main technological methods for regulating crop growth environments globally. They play a crucial role in food production, resource conservation, and rural economies, yet they also present environmental security threats. The current research on AGH extraction primarily focuses on small scales. However, the problem of confusing greenhouses with other features has not been completely resolved, and accurately obtaining the distribution of AGHs on large spatial scales is a challenge. We extract multi-temporal spectral feature data from Sentinel-2 images using Google Earth Engine (GEE) and build a hierarchical extraction decision tree model to extract AGHs. The model achieves an overall accuracy of 95.65%, with a Kappa coefficient of 0.89. Utilizing this model, a spatial distribution map of AGHs in Jilin Province for 2021 was generated, offering an initial insight into the overall distribution of AGHs. This method also demonstrates the significant potential and extensive application prospects of utilizing Sentinel-2 data and the GEE platform for identifying AGHs. It offers a scientific foundation for the sustainable development of agricultural regions and holds significant importance in advancing the development of facility agriculture by using Sentinel-2 data embedded in GEE at a regional or global scale.