Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine
Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/1004 |
| _version_ | 1797476787419086848 |
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| author | Yan Guo Haoming Xia Li Pan Xiaoyang Zhao Rumeng Li |
| author_facet | Yan Guo Haoming Xia Li Pan Xiaoyang Zhao Rumeng Li |
| author_sort | Yan Guo |
| collection | DOAJ |
| description | Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China. |
| first_indexed | 2024-03-09T21:08:39Z |
| format | Article |
| id | doaj.art-e6b1676617bd40fd9cab02affeac19ff |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:39Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e6b1676617bd40fd9cab02affeac19ff2023-11-23T21:55:29ZengMDPI AGRemote Sensing2072-42922022-02-01144100410.3390/rs14041004Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth EngineYan Guo0Haoming Xia1Li Pan2Xiaoyang Zhao3Rumeng Li4College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaDouble cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China.https://www.mdpi.com/2072-4292/14/4/1004mappingcropping intensitynorthern limitphenologyGoogle Earth Enginekernel density estimation |
| spellingShingle | Yan Guo Haoming Xia Li Pan Xiaoyang Zhao Rumeng Li Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine Remote Sensing mapping cropping intensity northern limit phenology Google Earth Engine kernel density estimation |
| title | Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine |
| title_full | Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine |
| title_fullStr | Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine |
| title_full_unstemmed | Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine |
| title_short | Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine |
| title_sort | mapping the northern limit of double cropping using a phenology based algorithm and google earth engine |
| topic | mapping cropping intensity northern limit phenology Google Earth Engine kernel density estimation |
| url | https://www.mdpi.com/2072-4292/14/4/1004 |
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