Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery
作者机构：State Key Laboratory of Water Resources and Hydropower Engineering ScienceWuhan UniversityWuhanPeople’s Republic of China College of AgricultureYangtze UniversityJingZhouPeople’s Republic of China Center for Spatial Information Science and Systems(CSISS)George Mason University(GMU)FairfaxVAUSA Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China State Key Laboratory of Information Engineering in SurveyingMapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China Jingzhou Agricultural Meteorologic Trial StationJingzhouPeople’s Republic of China
出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)
年 卷 期：2018年第11卷第12期
页 面：1219-1240页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the Key Program of National Natural Science Foundation of China[grant numbers 51339004 and 51209163]
主 题：Crop type classification multi-temporal satellite images HJ-1 CCD
摘 要：Crop type data are an important piece of information for many applications in *** crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather *** this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)*** masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal *** increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD *** phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey *** used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy *** results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.
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