Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data
Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient...
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| author | Lixiran Yu Hongfei Tao Qiao Li Hong Xie Yan Xu Aihemaiti Mahemujiang Youwei Jiang |
| author_facet | Lixiran Yu Hongfei Tao Qiao Li Hong Xie Yan Xu Aihemaiti Mahemujiang Youwei Jiang |
| author_sort | Lixiran Yu |
| collection | DOAJ |
| description | Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R<sup>2</sup> = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones. |
| format | Article |
| id | doaj-art-0b3534b0321b4ebe8eefe801c9cd757e |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Agriculture |
| spelling | doaj-art-0b3534b0321b4ebe8eefe801c9cd757e2025-08-20T02:23:01ZengMDPI AGAgriculture2077-04722025-05-011511119610.3390/agriculture15111196Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series DataLixiran Yu0Hongfei Tao1Qiao Li2Hong Xie3Yan Xu4Aihemaiti Mahemujiang5Youwei Jiang6College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaChangji Water Conservancy Management Station, Santunhe River Basin Management Office, Changji 831100, ChinaXinjiang Uygur Autonomous Region Ecological Water Resources Research Center, Academician and Expert Workstation of the Department of Water Resources of the Xinjiang Uygur Autonomous Region, Urumqi 830052, ChinaCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaIrrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R<sup>2</sup> = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.https://www.mdpi.com/2077-0472/15/11/1196Sentinel-2Sentinel-1random forestobject-orientedcrop classification |
| spellingShingle | Lixiran Yu Hongfei Tao Qiao Li Hong Xie Yan Xu Aihemaiti Mahemujiang Youwei Jiang Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data Agriculture Sentinel-2 Sentinel-1 random forest object-oriented crop classification |
| title | Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data |
| title_full | Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data |
| title_fullStr | Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data |
| title_full_unstemmed | Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data |
| title_short | Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data |
| title_sort | research on machine learning based extraction and classification of crop planting information in arid irrigated areas using sentinel 1 and sentinel 2 time series data |
| topic | Sentinel-2 Sentinel-1 random forest object-oriented crop classification |
| url | https://www.mdpi.com/2077-0472/15/11/1196 |
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