地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (5): 938-947.doi: 10.12082/dqxxkx.2021.200291
姜伊兰1,2(), 陈保旺1, 黄玉芳1, 崔佳琪1,2, 郭宇龙1,2,*(
)
收稿日期:
2020-06-08
修回日期:
2020-08-24
出版日期:
2021-05-25
发布日期:
2021-07-25
通讯作者:
郭宇龙
作者简介:
姜伊兰(1997— ),女,河南洛阳人,硕士生,主要从事土地资源管理研究。E-mail:934121453@qq.com
基金资助:
JIANG Yilan1,2(), CHEN Baowang1, HUANG Yufang1, CUI Jiaqi1,2, GUO Yulong1,2,*(
)
Received:
2020-06-08
Revised:
2020-08-24
Online:
2021-05-25
Published:
2021-07-25
Contact:
GUO Yulong
Supported by:
摘要:
为提高农作物种植信息遥感监测的效率,扩展数据适用范围,本文提出了一种基于时间序列NDVI差异指数的作物种植区提取方法。随着海量遥感与云计算的发展,Google Earth Engine作为一个全球尺度地理空间分析云平台,弥补了单机计算耗时长的不足,为快速遥感分类带来了新机遇。基于Google Earth Engine平台,以河南省开封市杞县为研究区,以2019—2020年杞县地区多时相Sentinel-2影像为数据源,结合物候信息,根据不同作物在时间序列NDVI曲线上的差异构建NDVI时序差异指数,从而提取作物种植区,区分不同作物类型,并与其他方法进行了精度验证和对比。结果表明:① NDVI时序差异指数法以作物物候信息为基础,与GEE高性能的计算能力相结合,形成了作物种植信息快速提取框架,可以方便快捷地进行作物种植区提取,较本地处理具有明显优势;② 杞县冬小麦和大蒜种植区有明显的空间分异性,冬小麦种植区主要集中在研究区西北部以及南部的农村居民点周围,而杞县大蒜则由于产品流通需要,主要集中在研究区中部以及东北部,居民点较为密集,交通便利的城市周边;③ 与时间序列支持向量机法和最大似然法相比较, NDVI时序差异指数进行作物种植区提取的总体精度达到83.72%, Kappa系数为0.67,分别比最大似然法提高了10.02%和0.21,比支持向量机法提高了4.18%和0.09,表明该方法能更高效率,更高精度地提取作物种植信息,实现区域作物种植信息的高效准确监测。总体来看,该方法在一定程度上可拓展遥感数据在农业领域的应用范围,具有推广价值。
姜伊兰, 陈保旺, 黄玉芳, 崔佳琪, 郭宇龙. 基于Google Earth Engine和NDVI时序差异指数的作物种植区提取[J]. 地球信息科学学报, 2021, 23(5): 938-947.DOI:10.12082/dqxxkx.2021.200291
JIANG Yilan, CHEN Baowang, HUANG Yufang, CUI Jiaqi, GUO Yulong. Crop Planting Area Extraction based on Google Earth Engine and NDVI Time Series Difference Index[J]. Journal of Geo-information Science, 2021, 23(5): 938-947.DOI:10.12082/dqxxkx.2021.200291
表1
Sentinel-2数据参数
属性 | 中心波长/μm | 分辨率/m | |
---|---|---|---|
Band 1 | 气溶胶 | 0.443 | 60 |
Band 2 | 蓝 | 0.490 | 10 |
Band 3 | 绿 | 0.560 | 10 |
Band 4 | 红 | 0.665 | 10 |
Band 5 | 植被红边1 | 0.705 | 20 |
Band 6 | 植被红边2 | 0.740 | 20 |
Band 7 | 植被红边3 | 0.783 | 20 |
Band 8 | 近红外 | 0.842 | 10 |
Band 8A | 植被红边4 | 0.865 | 20 |
Band 9 | 水蒸气 | 0.945 | 60 |
Band 10 | 短波红外-卷云 | 1.375 | 60 |
Band 11 | 短波红外1 | 1.610 | 20 |
Band 12 | 短波红外2 | 2.190 | 20 |
QA60 | 云掩膜 | - | - |
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