Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images
文献类型：期刊论文
| 作者 | Wang, Jie3 ; Xiao, Xiangming3; Liu, Luo4; Wu, Xiaocui3; Qin, Yuanwei3; Steiner, Jean L.1; Dong, Jinwei2
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| 刊名 | REMOTE SENSING OF ENVIRONMENT
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| 出版日期 | 2020-09-15 |
| 卷号 | 247页码:16 |
| ISSN号 | 0034-4257 |
| 关键词 | Agriculture Crop mapping Land cover Phenology Vegetation indices Remote sensing |
| DOI | 10.1016/j.rse.2020.111951 |
| 通讯作者 | Xiao, Xiangming(xiangming.xiao@ou.edu) |
| 英文摘要 | Sugarcane is a major crop for sugar and ethanol production and its area has increased substantially in tropical and subtropical regions in recent decades. Updated and accurate sugarcane maps are critical for monitoring sugarcane area and production and assessing its impacts on the society, economy and the environment. To date, no sugarcane mapping tools are available to generate annual maps of sugarcane at the field scale over large regions. In this study, we developed a pixel- and phenology-based mapping tool to produce an annual map of sugarcane at 10-m spatial resolution by analyzing time-series Landsat-7/8, Sentinel-2 and Sentinel-1 images (LC/ S2/S1) during August 31, 2017 - July 1, 2019 in Guangxi province, China, which accounts for 65% of sugarcane production of China. First, we generated annual maps of croplands and other land cover types in 2018. Second, we delineated the cropping intensity (single, double and triple cropping in a year) for all cropland pixels in 2018. Third, we identified sugarcane fields in 2018 based on its phenological characteristics. The resultant 2018 sugarcane map has producer, user and overall accuracies of 88%, 96% and 96%, respectively. According to the annual sugarcane map in 2018, there was a total of 8940 km(2) sugarcane in Guangxi, which was similar to 1% higher than the estimate from the Guangxi Agricultural Statistics Report. Finally, we identified green-up dates of those sugarcane fields in 2019, which could be used to support the sugarcane planting and management activities. Our study demonstrates the potential of the pixel- and phenology-based sugarcane mapping tool (both the algorithms and the LC/S2/S1 time series images) in identifying croplands, cropping intensity and sugarcane fields in the complex landscapes with diverse crop types, fragmented crop fields and frequent cloudy weather. The resultant annual maps from this study could be used to assist farms and sugarcane mills for sustainable sugarcane production and environment. |
| WOS关键词 | PADDY RICE AGRICULTURE ; DIFFERENCE WATER INDEX ; SAO-PAULO STATE ; CROP CLASSIFICATION ; RAPID EXPANSION ; NATIONAL-SCALE ; MAP SUGARCANE ; COVER ; AREA ; PERFORMANCE |
| 资助项目 | NASA Land Use and Land Cover Change program[NNX14AD78G] ; USDA NIFA[2020-67104-30935] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000549189200049 |
| 资助机构 | NASA Land Use and Land Cover Change program ; USDA NIFA |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/158360]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xiao, Xiangming |
| 作者单位 | 1.Kansas State Univ, Agron Dept, Manhattan, KS 66502 USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Oklahoma, Dept Microbiol & Plant Biol, 101 David L Boren Blvd, Norman, OK 73019 USA 4.South China Agr Univ, Guangdong Prov Key Lab Land Use & Consolidat, Guangzhou 510642, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jie,Xiao, Xiangming,Liu, Luo,et al. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images[J]. REMOTE SENSING OF ENVIRONMENT,2020,247:16. |
| APA | Wang, Jie.,Xiao, Xiangming.,Liu, Luo.,Wu, Xiaocui.,Qin, Yuanwei.,...&Dong, Jinwei.(2020).Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images.REMOTE SENSING OF ENVIRONMENT,247,16. |
| MLA | Wang, Jie,et al."Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images".REMOTE SENSING OF ENVIRONMENT 247(2020):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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