Mapping cropland in Yunnan Province during 1990–2020 using multi-source remote sensing data with the Google Earth Engine Platform

Abstract

Cropland is the foundation of agriculture and the cornerstone of ensuring national food security. Ongoing advancement of China’s urbanization and social economy results in a tendency toward ‘non-agriculturalization’ of cropland in pursuit of economic benefits, which greatly threatens food security. Therefore, long-term monitoring of cropland is urgently needed. Based on the Google Earth Engine (GEE) platform, this paper uses the Landsat series and Sentinel-2 multisource images from 1990–2020 to dynamically monitor cropland in Yunnan Province. First, a multiresolution collaborative sample selection method was proposed and sample labels with resolutions better than 30 m from 1990 to 2015 were obtained. An ‘impervious surface-spectrum-texture-geometry’ (ISTG) feature system was constructed and the Jeffries-Matusita (JM) distance method was used for feature optimization. Finally, the proposed collaborative classification system method of land classification and binary classification was used to extract cropland in Yunnan Province. The results show that: (1) The accuracy of cropland extraction was highly reliable, with an overall accuracy (OA) wall-to-wall ranging from 93.15–98.14% and a kappa coefficient (Kappa) ranging from 86.29–96.26%. (2) The change trend of the cropland area in Yunnan Province from 1990 to 2020 exhibited certain fluctuations, but overall decreased annually. (3) The province’s average annual cropland area is 567.71 × 103 hectares. The proposed method has great potential in large-scale cropland extraction.

Keywords

• Cropland extraction
• long time-series remote sensing
• Google Earth Engine
• sample selection
• feature selection