Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

作     者：Jiantao Liu Quanlong Feng Jianhua Gong Jieping Zhou Jianming Liang Yi Li 

作者机构：State Key Laboratory of Remote Sensing ScienceInstitute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of SciencesBeijingPeople’s Republic of China Beijing Piesat Information Technology Co.Ltd.BeijingPeople’s Republic of China Zhejiang-CAS Application Center for GeoinformaticsJiashanPeople’s Republic of China School of Life SciencesArizona State UniversityTempeAZUSA 

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8期

页      面：783-802页

核心收录：

学科分类：0708[理学-地球物理学] 09[农学] 0835[工学-软件工程] 0901[农学-作物学] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the European Space Agency and National Remote Sensing Centre of China Dragon 3 Program[grant number 10668],the National Natural Science Foundation of China[grant number 41471341] ‘135’Strategy Planning of the Institute of Remote Sensing and Digital Earth,CAS[grant number Y3SG1500CX] 

主　　题：Winter wheat random forest phenology Landsat-8 GF-1 

摘      要：Wheat is a major staple food crop in *** and cost-effective wheat mapping is exceedingly critical for food production management,food security warnings,and food trade policy-making in *** reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping,we present a novel approach that combines a random forest(RF)classifier with multi-sensor and multi-temporal image *** study aims to(1)determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping,(2)to find out whether the proposed approach can provide improved performance over the traditional classifiers,and(3)examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed *** wheat mapping experiments were conducted in Boxing *** experimental results suggest that the proposed method can achieve good performance,with an overall accuracy of 92.9%and a kappa coefficient(κ)of *** winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%.The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification *** conclude that the proposed approach can provide accurate delineation of winter wheat areas.