Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform
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 promo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1461 |
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| author | Jian Li Weilin Yu Jia Du Kaishan Song Xiaoyun Xiang Hua Liu Yiwei Zhang Weijian Zhang Zhi Zheng Yan Wang Yue Sun |
| author_facet | Jian Li Weilin Yu Jia Du Kaishan Song Xiaoyun Xiang Hua Liu Yiwei Zhang Weijian Zhang Zhi Zheng Yan Wang Yue Sun |
| author_sort | Jian Li |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-11T07:12:04Z |
| format | Article |
| id | doaj.art-f86b30d70919402993db77ca06cb8782 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:12:04Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f86b30d70919402993db77ca06cb87822023-11-17T08:33:26ZengMDPI AGRemote Sensing2072-42922023-03-01155146110.3390/rs15051461Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud PlatformJian Li0Weilin Yu1Jia Du2Kaishan Song3Xiaoyun Xiang4Hua Liu5Yiwei Zhang6Weijian Zhang7Zhi Zheng8Yan Wang9Yue Sun10College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaAs 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 R<sup>2</sup> 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.https://www.mdpi.com/2072-4292/15/5/1461Google Earth Engineconservation tillageSentinel-2maize residue covertillage indices |
| spellingShingle | Jian Li Weilin Yu Jia Du Kaishan Song Xiaoyun Xiang Hua Liu Yiwei Zhang Weijian Zhang Zhi Zheng Yan Wang Yue Sun Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform Remote Sensing Google Earth Engine conservation tillage Sentinel-2 maize residue cover tillage indices |
| title | Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform |
| title_full | Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform |
| title_fullStr | Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform |
| title_full_unstemmed | Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform |
| title_short | Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform |
| title_sort | mapping maize tillage practices over the songnen plain in northeast china using gee cloud platform |
| topic | Google Earth Engine conservation tillage Sentinel-2 maize residue cover tillage indices |
| url | https://www.mdpi.com/2072-4292/15/5/1461 |
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