A dataset of multi-source and multi-temporal remote sensing data of cash crop planting structure in Yangling Agricultural Demonstration Zone

Abstract

Satellite remote sensing technology can obtain the distribution of ground objects on a large scale in a timely manner, and provide great data and technical support for the acquisition of information on the planting structure of cash crops. Taking Yangling Agricultural Demonstration Area as the research area, this dataset is composed of four parts: remote sensing data, ground truth data, Yangling boundary and classification results. The remote sensing data consist of satellite data, such as Sentinel-2, Gaofen-1 (including Gaofen-1C satellite), Gaofen-2, and Gaofen-6 from April to September in 2021 after radiation correction, atmospheric correction, and remote sensing image processing such as orthorectification, image fusion, and image registration. Through on-the-spot investigation, visual interpretation of Google Earth, and near-ground remote sensing of UAVs in small areas, we established the ground truth distribution verification area. In terms of quality control, the remote sensing data are characteristic of little overall cloud content, uniform color, and a spatial resolution of 2m; the ground truth map, authentic and reliable, is drawn through field surveys. The dataset has been verified by random forest algorithm, and the overall classification accuracy is 86.17%. It can provide training samples for the research and application of related algorithms in the acquisition of cash crop planting structure, and can also provide data support for land use classification and changes as well as crop growth monitoring in Yangling Demonstration Zone.