An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China
Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/5/1208 |
| _version_ | 1797473908737179648 |
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| author | Yueran Hu Hongwei Zeng Fuyou Tian Miao Zhang Bingfang Wu Sven Gilliams Sen Li Yuanchao Li Yuming Lu Honghai Yang |
| author_facet | Yueran Hu Hongwei Zeng Fuyou Tian Miao Zhang Bingfang Wu Sven Gilliams Sen Li Yuanchao Li Yuming Lu Honghai Yang |
| author_sort | Yueran Hu |
| collection | DOAJ |
| description | Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together. |
| first_indexed | 2024-03-09T20:23:40Z |
| format | Article |
| id | doaj.art-d0a9b6634bc94ebc9b8f1f948f29aa22 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:23:40Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d0a9b6634bc94ebc9b8f1f948f29aa222023-11-23T23:43:08ZengMDPI AGRemote Sensing2072-42922022-03-01145120810.3390/rs14051208An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, ChinaYueran Hu0Hongwei Zeng1Fuyou Tian2Miao Zhang3Bingfang Wu4Sven Gilliams5Sen Li6Yuanchao Li7Yuming Lu8Honghai Yang9State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaVlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, 2400 Mol, BelgiumKey Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaBig Data Center of Geospatial and Nature Resources of Qinghai Province, Xining 810001, ChinaCrop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.https://www.mdpi.com/2072-4292/14/5/1208crop type classificationrandom forest classifierinterannual transferGPSvideo and GIS (GVG)Google Earth Engine |
| spellingShingle | Yueran Hu Hongwei Zeng Fuyou Tian Miao Zhang Bingfang Wu Sven Gilliams Sen Li Yuanchao Li Yuming Lu Honghai Yang An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China Remote Sensing crop type classification random forest classifier interannual transfer GPS video and GIS (GVG) Google Earth Engine |
| title | An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China |
| title_full | An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China |
| title_fullStr | An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China |
| title_full_unstemmed | An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China |
| title_short | An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China |
| title_sort | interannual transfer learning approach for crop classification in the hetao irrigation district china |
| topic | crop type classification random forest classifier interannual transfer GPS video and GIS (GVG) Google Earth Engine |
| url | https://www.mdpi.com/2072-4292/14/5/1208 |
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