Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google

删除或更新信息，请邮件至freekaoyan#163.com(#换成@)

本站小编 Free考研考试/2021-12-12

论文编号:
论文题目:	Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine
英文论文题目:	Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine
第一作者:	罗冲
英文第一作者:	L. Chong
联系作者:	刘焕军
英文联系作者:	H. J. Liu
外单位作者单位:
英文外单位作者单位:
发表年度:	2021
卷:	20
期:	7
页码:	1944-1957
摘要:	Rapid and accurate access to large-scale, high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development. Due to the limitations of remote sensing image quality and data processing capabilities, large-scale crop classification is still challenging. This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine (GEE) and Sentinel-1 and Sentinel-2 images. We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018 (May to September), combined monthly composite images of reflectance bands, vegetation indices and polarization bands as input features, and then performed crop classification using a Random Forest (RF) classifier. The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area, and the overall accuracy (OA) reached 89.75%. Through experiments, we also found that the classification performance using time-series images is significantly better than that using single-period images. Compared with the use of traditional bands only (i.e., the visible and near-infrared bands), the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly, followed by the addition of red-edge bands. Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%, respectively, compared to using only the Sentinel-2 reflectance bands. The analysis of timeliness revealed that when the July image is available, the increase in the accuracy of crop classification is the highest. When the Sentinel-1 and Sentinel-2 images for May, June, and July are available, an OA greater than 80% can be achieved. The results of this study are applicable to large-scale, high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.
英文摘要:	Rapid and accurate access to large-scale, high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development. Due to the limitations of remote sensing image quality and data processing capabilities, large-scale crop classification is still challenging. This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine (GEE) and Sentinel-1 and Sentinel-2 images. We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018 (May to September), combined monthly composite images of reflectance bands, vegetation indices and polarization bands as input features, and then performed crop classification using a Random Forest (RF) classifier. The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area, and the overall accuracy (OA) reached 89.75%. Through experiments, we also found that the classification performance using time-series images is significantly better than that using single-period images. Compared with the use of traditional bands only (i.e., the visible and near-infrared bands), the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly, followed by the addition of red-edge bands. Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%, respectively, compared to using only the Sentinel-2 reflectance bands. The analysis of timeliness revealed that when the July image is available, the increase in the accuracy of crop classification is the highest. When the Sentinel-1 and Sentinel-2 images for May, June, and July are available, an OA greater than 80% can be achieved. The results of this study are applicable to large-scale, high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.
刊物名称:	Journal of Integrative Agriculture
英文刊物名称:	Journal of Integrative Agriculture
论文全文:
英文论文全文:
全文链接:
其它备注:
英文其它备注:
学科:
英文学科:
影响因子:
第一作者所在部门:
英文第一作者所在部门:
论文出处:
英文论文出处:
论文类别:
英文论文类别:
参与作者:	L. Chong, H. J. Liu, L. P. Lu, Z. R. Liu, F. C. Kong and X. L. Zhang
英文参与作者:	L. Chong, H. J. Liu, L. P. Lu, Z. R. Liu, F. C. Kong and X. L. Zhang

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google

本站小编 Free考研考试/2021-12-12

相关话题/

领限时大额优惠券,享本站正版考研考试资料!