Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine
Plastic mulch is widely used in agriculture, providing significant benefits. However, improper use can harm farmland ecosystems and threaten food security, especially in black soil regions, which are primary grain-producing areas. Timely and accurate monitoring of spatial and temporal changes in pla...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10804628/ |
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| author | Jingfa Zhong Dongmei Ji Lei Chang Yuefen Li |
| author_facet | Jingfa Zhong Dongmei Ji Lei Chang Yuefen Li |
| author_sort | Jingfa Zhong |
| collection | DOAJ |
| description | Plastic mulch is widely used in agriculture, providing significant benefits. However, improper use can harm farmland ecosystems and threaten food security, especially in black soil regions, which are primary grain-producing areas. Timely and accurate monitoring of spatial and temporal changes in plastic-mulched farmland (PMF) is crucial for controlling mulch pollution and promoting sustainable development. However, dynamic changes of PMF, diversity of background environment, and cloud cover seriously hinder the long-term and large-scale monitoring of PMF. Therefore, this article proposed a new process for monitoring PMF based on Google Earth Engine and multitemporal random forest probability synthesis (MRFPS) algorithms. Taking three typical black soil mulched areas, the random forest performance was compared in five feature scenarios by combining spectral bands, spectral indices, and texture information. The key features and combinations were optimized, and then the MRFPS algorithm was used to minimize the impact of cloud contamination and map the distribution of PMF. The results showed that the sowing period and vigorous growth period were the key periods for PMF identification; the red edge bands, BSI, retrogressive plastic greenhouse index, plastic-mulched citrus index, NDBBI, and EVI were the important features for PMF identification while the texture information had less influence. The classification results had high accuracy with an OA of more than 90%, outperforming other methods. Analysis of the spatial distribution from 2017 to 2023 revealed a continued shrinkage in PMF area, with regional differences in the frequency of PMF, which may be closely related to farming practices and government policies. This study provides essential support for exploring PMF distribution change patterns. |
| format | Article |
| id | doaj-art-1675d2ba08da4506a673d427376fdcb7 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-1675d2ba08da4506a673d427376fdcb72025-02-04T00:00:28ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184347436510.1109/JSTARS.2024.351942510804628Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth EngineJingfa Zhong0Dongmei Ji1Lei Chang2https://orcid.org/0009-0005-1288-0695Yuefen Li3https://orcid.org/0000-0002-6099-5893College of Earth Sciences, Jilin University, Changchun, ChinaJilin Province Research Institute of Land and Resources Planning, Changchun, ChinaCollege of Earth Sciences, Jilin University, Changchun, ChinaCollege of Earth Sciences, Jilin University, Changchun, ChinaPlastic mulch is widely used in agriculture, providing significant benefits. However, improper use can harm farmland ecosystems and threaten food security, especially in black soil regions, which are primary grain-producing areas. Timely and accurate monitoring of spatial and temporal changes in plastic-mulched farmland (PMF) is crucial for controlling mulch pollution and promoting sustainable development. However, dynamic changes of PMF, diversity of background environment, and cloud cover seriously hinder the long-term and large-scale monitoring of PMF. Therefore, this article proposed a new process for monitoring PMF based on Google Earth Engine and multitemporal random forest probability synthesis (MRFPS) algorithms. Taking three typical black soil mulched areas, the random forest performance was compared in five feature scenarios by combining spectral bands, spectral indices, and texture information. The key features and combinations were optimized, and then the MRFPS algorithm was used to minimize the impact of cloud contamination and map the distribution of PMF. The results showed that the sowing period and vigorous growth period were the key periods for PMF identification; the red edge bands, BSI, retrogressive plastic greenhouse index, plastic-mulched citrus index, NDBBI, and EVI were the important features for PMF identification while the texture information had less influence. The classification results had high accuracy with an OA of more than 90%, outperforming other methods. Analysis of the spatial distribution from 2017 to 2023 revealed a continued shrinkage in PMF area, with regional differences in the frequency of PMF, which may be closely related to farming practices and government policies. This study provides essential support for exploring PMF distribution change patterns.https://ieeexplore.ieee.org/document/10804628/Google Earth engine (GEE)plastic-mulched farmland (PMF)random forestSentinel-2 (S2) |
| spellingShingle | Jingfa Zhong Dongmei Ji Lei Chang Yuefen Li Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Google Earth engine (GEE) plastic-mulched farmland (PMF) random forest Sentinel-2 (S2) |
| title | Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine |
| title_full | Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine |
| title_fullStr | Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine |
| title_full_unstemmed | Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine |
| title_short | Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine |
| title_sort | monitoring interannual variability in spatial distribution of plastic mulched farmland in black soil areas using google earth engine |
| topic | Google Earth engine (GEE) plastic-mulched farmland (PMF) random forest Sentinel-2 (S2) |
| url | https://ieeexplore.ieee.org/document/10804628/ |
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