| 基于Google Earth Engine和NDVI时序差异指数的作物种植区提取 |
| |
| 引用本文： | 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 |
| |
| 作者姓名： | 姜伊兰 陈保旺 黄玉芳 崔佳琪 郭宇龙 |
| |
| 作者单位： | 1.河南农业大学资源与环境学院,郑州 4500022.河南省土地整治与生态重建工程技术研究中心,郑州 450002 |
| |
| 基金项目： | 国家自然科学基金项目(41701422);河南省重点研发与推广专项(科技攻关)(192102310251) |
| |
| 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...
|
| 关 键 词： | Sentinel-2 NDVI 时间序列 物候特征 Google Earth Engine 作物提取 精度对比 杞县 |
| 收稿时间： | 2020-06-08 |
Crop Planting Area Extraction based on Google Earth Engine and NDVI Time Series Difference Index |
| |
| 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]. Geo-information Science, 2021, 23(5): 938-947. DOI: 10.12082/dqxxkx.2021.200291 |
| |
| Authors: | JIANG Yilan CHEN Baowang HUANG Yufang CUI Jiaqi GUO Yulong |
| |
| Affiliation: | 1. College of Resources and Environment , Henan Agricultural University, Zhengzhou 450002, China2. Henan Engineering Research Center of Land Consolidation and Ecological Restoration, Zhengzhou 450002, China |
| |
| Abstract: | In order to improve the efficiency of remote sensing monitoring of crop planting and expand applications of remote sensing data, a method of crop planting area extraction based on NDVI time series difference index is proposed. With the development of remote sensing and cloud computing technologies, Google Earth Engine, as a global-scale geospatial analysis cloud platform, overcomes the disadvantages of traditional single-machine computing and brings new opportunities for rapid remote sensing classification. In this study, taking Qi County in Henan province as the study area, the NDVI time series difference index of different crops is constructed according to the characteristics of time series NDVI curve of each crop to extract crop planting information and distinguish different crop types using multi-temporal Sentinel-2 images in 2019-2020 based on the Google Earth Engine platform. The extraction accuracy is verified and compared with other existing methods. The results show that the NDVI time series difference index is based on crop phenology information and developed using GEE's high-performance computing capability, which forms a framework for rapid crop planting information extraction and has obvious advantages over traditional local computing. The winter wheat and garlic planting areas in Qi County have obvious spatial variation. The winter wheat planting areas are mainly concentrated in the northwest and southern rural residential areas of the study area. While the garlic in Qi County is mainly concentrated in the central and northeastern part of the study area due to the needs of transportation. Compared with other methods using support vector machine and maximum likelihood, the overall accuracy of crop planting area extraction using the NDVI time series difference index reaches 83.72%, and the Kappa coefficient is 0.67. The overall accuracy and the Kappa coefficient are 10.02% and 0.21 respectively higher than the maximum likelihood method, and are 4.18% and 0.09 respectively higher than the support vector machine method, which indicates that our method can extract crop planting information with high efficiency and high accuracy. We develop an efficient and accurate monitoring method for regional crop planting information extraction and expand the application of remote sensing data in the agricultural field, which has significant value for future agricultural applications. |
| |
| Keywords: | Sentinel-2 NDVI time-series phenological characteristics Google Earth Engine crop extraction accuracy comparison Qi County |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《地球信息科学学报》浏览原始摘要信息 |
|
点击此处可从《地球信息科学学报》下载全文 |
|
