A spatiotemporal collaborative approach for precise crop planting structure mapping based on multi-source remote-sensing data

In order to ensure food security, it is crucial to collect agricultural information efficiently and accurately. Remote sensing has become increasingly important in obtaining crop distribution information on a large scale. However, current research based on satellite platforms struggles to meet the requirements of high-precision and large-scale crop monitoring simultaneously. To address this challenge, we propose a method for achieving fine-scale crop classification by integrating remote-sensing data from various satellite platforms by constructing temporal-scale crop features within the parcels using Sentinel-2A, Landsat-8, and Gaofen-6. We adopt a feature-matching method to fill in missing values in the time-series feature construction process, to avoid issues with unidentifiable crops. The classification results of the Yellow River basin of the Ningxia region show that our method can achieve a wide range of crop discrimination on a fine scale, with an overall accuracy of 80%. Our proposed method demonstrates the potential of integrating multi-platform remote-sensing data to achieve fine-scale crop classification, which can aid decision-making for farmers, government agencies, and other stakeholders involved in the agricultural sector.