Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1461
• Main Authors: Li, Jian, Yu, Weilin, Du, Jia, Song, Kaishan, Xiang, Xiaoyun, Liu, Hua, Zhang, Yiwei, Zhang, Weijian, Zheng, Zhi, Wang, Yan, Sun, Yue
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; Agricultural development; Agricultural engineering; Agricultural practices; Agricultural production; Alliances; Cloud computing; Conservation; Conservation tillage; Corn; Crop residues; Environmental aspects; Farming; Geospatial data; Google Earth Engine; Growth; maize residue cover; Mapping; Methods; Monitoring; Remote sensing; Sentinel-2; Soil conservation; Soil erosion; Spatial data; Spatial distribution; Streamlining; Sustainability; Sustainable agriculture; Sustainable development; Temporal distribution; Tillage; tillage indices; Time series; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051461

As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development.