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The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

文献类型：期刊论文

作者Sun, Rui2,3; Chen, Shaohui2; Su, Hongbo2,4; Mi, Chunrong1,3; Jin, Ning5
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期2019-11-01
卷号8期号:11页码:17
关键词support vector machine classification accuracy remote sensing random forest
DOI10.3390/ijgi8110502
通讯作者Chen, Shaohui(chensh@igsnrr.ac.cn)
英文摘要Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.
WOS关键词LAND-COVER CLASSIFICATION ; SUPPORT VECTOR MACHINE ; SPATIAL-DISTRIBUTION ; SURFACE REFLECTANCE ; RECENT PROGRESSES ; PATTERNS ; AREA ; INTEGRATION ; FEATURES ; CHINA
资助项目Natural Science Fund of China[41671368] ; Natural Science Fund of China[41371348] ; Second Tibetan Plateau Scientific Expedition and Research Program[2019QZKK1003] ; Strategic Priority Research Program A of the Chinese Academy of Sciences[XDA20010301]
WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000502272600036
出版者MDPI
资助机构Natural Science Fund of China ; Second Tibetan Plateau Scientific Expedition and Research Program ; Strategic Priority Research Program A of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/130693]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Shaohui
作者单位1.Chinese Acad Sci, Inst Zool, Key Lab Anim Ecol & Conservat Biol, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Florida Atlantic Univ, Dept Civil Environm & Geomat Engn, Boca Raton, FL 33431 USA
5.Shanxi Inst Energy, Dept Resources & Environm, Jinzhong 030600, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Rui,Chen, Shaohui,Su, Hongbo,et al. The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(11):17.
APA Sun, Rui,Chen, Shaohui,Su, Hongbo,Mi, Chunrong,&Jin, Ning.(2019).The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(11),17.
MLA Sun, Rui,et al."The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.11(2019):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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