remote sensing Article Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China Yuankang Xiong 1,2, Qingling Zhang 1,3,*, Xi Chen 1, Anming Bao 1, Jieyun Zhang 1 and Yujuan Wang 4 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou 510006, China 4 China-Asean Environment Cooperation Center, Beijing 100875, China * Correspondence:
[email protected] Received: 8 July 2019; Accepted: 30 August 2019; Published: 6 September 2019 Abstract: Plastic mulching has been widely practiced in crop cultivation worldwide due to its potential to significantly increase crop production. However, it also has a great impact on the regional climate and ecological environment. More importantly, it often leads to unexpected soil pollution due to fine plastic residuals. Therefore, accurately and timely monitoring of the temporal and spatial distribution of plastic mulch practice in large areas is of great interest to assess its impacts. However, existing plastic-mulched farmland (PMF) detecting efforts are limited to either small areas with high-resolution images or coarse resolution images of large areas. In this study, we examined the potential of cloud computing and multi-temporal, multi-sensor satellite images for detecting PMF in large areas. We first built the plastic-mulched farmland mapping algorithm (PFMA) rules through analyzing its spectral, temporal, and auxiliary features in remote sensing imagery with the classification and regression tree (CART).