Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine
With the decline of cultivated land quality and area in recent decades, the intensification of land use plays an important role in meeting the growing demand for food. Cropping intensity refers to the number of crop planting cycles in one year, which is important for improving food production and sa...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-10-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243421000830 |
| _version_ | 1811317573778669568 |
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| author | Li Pan Haoming Xia Jia Yang Wenhui Niu Ruimeng Wang Hongquan Song Yan Guo Yaochen Qin |
| author_facet | Li Pan Haoming Xia Jia Yang Wenhui Niu Ruimeng Wang Hongquan Song Yan Guo Yaochen Qin |
| author_sort | Li Pan |
| collection | DOAJ |
| description | With the decline of cultivated land quality and area in recent decades, the intensification of land use plays an important role in meeting the growing demand for food. Cropping intensity refers to the number of crop planting cycles in one year, which is important for improving food production and safety at the local, regional and national scales. Therefore, it is necessary to develop an accurate high spatial resolution dataset of cropping intensity. The existing datasets of cropping intensity were generally developed based on MODIS or Landsat images, both of which have defects in spatial and temporal resolutions. In this paper, we improved the quality of the dataset on the Google Earth Engine (GEE) platform, and developed a new algorithm incorporating crop phenology. The algorithm was based on the Landsat 7/8 and Sentine-2A/B time series imageries to map the 30 m cropping intensity in the Huaihe basin in 2018 by extracting complete growth cycle. Results show that single cropping, double cropping and triple cropping in the Huaihe basin accounted for 41.6%, 57.7% and 0.7% of the total cultivated area in 2018, respectively, and the proportion of multiple cropping reached 58.4%. The accuracy of single cropping, double cropping and triple cropping are 92.93%, 91.39%, and 72.78% respectively. The overall accuracy is 91.38% and the kappa coefficient is 0.84. This algorithm accurately captures the seasonal dynamics of planting patterns in arable land, which can be used to produce cropping intensity products with high-resolution and provide a reference for large-scale regional vegetation monitoring. |
| first_indexed | 2024-04-13T12:10:39Z |
| format | Article |
| id | doaj.art-67d51fe52ab7449d809130fc8584a409 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T12:10:39Z |
| publishDate | 2021-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-67d51fe52ab7449d809130fc8584a4092022-12-22T02:47:29ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-10-01102102376Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth EngineLi Pan0Haoming Xia1Jia Yang2Wenhui Niu3Ruimeng Wang4Hongquan Song5Yan Guo6Yaochen Qin7College of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, ChinaCollege of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, China; Corresponding author at: Henan University, College of Environment and Planning, No.1, the JinMing Avenue, Kaifeng 475001, China.Department of Forestry, Mississippi State University, Starkville, MS 39762, USACollege of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, ChinaCollege of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, ChinaCollege of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, ChinaCollege of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, ChinaCollege of Environment and Planning, Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, Henan, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China; Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng 475004, ChinaWith the decline of cultivated land quality and area in recent decades, the intensification of land use plays an important role in meeting the growing demand for food. Cropping intensity refers to the number of crop planting cycles in one year, which is important for improving food production and safety at the local, regional and national scales. Therefore, it is necessary to develop an accurate high spatial resolution dataset of cropping intensity. The existing datasets of cropping intensity were generally developed based on MODIS or Landsat images, both of which have defects in spatial and temporal resolutions. In this paper, we improved the quality of the dataset on the Google Earth Engine (GEE) platform, and developed a new algorithm incorporating crop phenology. The algorithm was based on the Landsat 7/8 and Sentine-2A/B time series imageries to map the 30 m cropping intensity in the Huaihe basin in 2018 by extracting complete growth cycle. Results show that single cropping, double cropping and triple cropping in the Huaihe basin accounted for 41.6%, 57.7% and 0.7% of the total cultivated area in 2018, respectively, and the proportion of multiple cropping reached 58.4%. The accuracy of single cropping, double cropping and triple cropping are 92.93%, 91.39%, and 72.78% respectively. The overall accuracy is 91.38% and the kappa coefficient is 0.84. This algorithm accurately captures the seasonal dynamics of planting patterns in arable land, which can be used to produce cropping intensity products with high-resolution and provide a reference for large-scale regional vegetation monitoring.http://www.sciencedirect.com/science/article/pii/S0303243421000830Cropping intensityPhenologyRemote sensingGoogle Earth EngineHuaihe basin |
| spellingShingle | Li Pan Haoming Xia Jia Yang Wenhui Niu Ruimeng Wang Hongquan Song Yan Guo Yaochen Qin Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine International Journal of Applied Earth Observations and Geoinformation Cropping intensity Phenology Remote sensing Google Earth Engine Huaihe basin |
| title | Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine |
| title_full | Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine |
| title_fullStr | Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine |
| title_full_unstemmed | Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine |
| title_short | Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine |
| title_sort | mapping cropping intensity in huaihe basin using phenology algorithm all sentinel 2 and landsat images in google earth engine |
| topic | Cropping intensity Phenology Remote sensing Google Earth Engine Huaihe basin |
| url | http://www.sciencedirect.com/science/article/pii/S0303243421000830 |
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