Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China
Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting t...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/39 |
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| author | Kaiwen Zhong Jian Zuo Jianhui Xu |
| author_facet | Kaiwen Zhong Jian Zuo Jianhui Xu |
| author_sort | Kaiwen Zhong |
| collection | DOAJ |
| description | Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City. |
| format | Article |
| id | doaj-art-16b9538e145b401087799f2e1ee1354c |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-16b9538e145b401087799f2e1ee1354c2025-01-10T13:20:02ZengMDPI AGRemote Sensing2072-42922024-12-011713910.3390/rs17010039Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, ChinaKaiwen Zhong0Jian Zuo1Jianhui Xu2Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaDue to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City.https://www.mdpi.com/2072-4292/17/1/39multi-temporal SAR dataricebackscattering coefficientU-Netspatio-temporal changes |
| spellingShingle | Kaiwen Zhong Jian Zuo Jianhui Xu Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China Remote Sensing multi-temporal SAR data rice backscattering coefficient U-Net spatio-temporal changes |
| title | Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China |
| title_full | Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China |
| title_fullStr | Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China |
| title_full_unstemmed | Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China |
| title_short | Rice Identification and Spatio-Temporal Changes Based on Sentinel-1 Time Series in Leizhou City, Guangdong Province, China |
| title_sort | rice identification and spatio temporal changes based on sentinel 1 time series in leizhou city guangdong province china |
| topic | multi-temporal SAR data rice backscattering coefficient U-Net spatio-temporal changes |
| url | https://www.mdpi.com/2072-4292/17/1/39 |
| work_keys_str_mv | AT kaiwenzhong riceidentificationandspatiotemporalchangesbasedonsentinel1timeseriesinleizhoucityguangdongprovincechina AT jianzuo riceidentificationandspatiotemporalchangesbasedonsentinel1timeseriesinleizhoucityguangdongprovincechina AT jianhuixu riceidentificationandspatiotemporalchangesbasedonsentinel1timeseriesinleizhoucityguangdongprovincechina |