摘要
Due to differences in environmental factors,the phenology of the same crop is different every year,causing divergent performances of the classifier built by spectral or time-series features Here,we proposed a random forest classifier(RFC)based on an asymmetric double S curve model fitted by accumulated temperature(AT)and Vegetation Index(VI),which can be applied in different years without ground samples.We built AT and VI time series from Moderate Resolution Imaging Spectroradiometer 8-day composites of land surface temperatures and Sentinel-2 and Landsat-8,respectively.The RFC was trained by characteristics from the asymmetric double S curve.We prepared RFC by ground samples of 2018 and 2019 and then mapped crops of the same region in 2017.Results indicated that,compared with diverse VI-AT series,the overall accuracy based on universal normalized vegetation index(UNVI)was the best of all(2017:F1=0.91,2018:F1=0.92,2019:F1=0.91)and better than that based on the UNVI-TIME series(2017:F1=0.84,2018:F1=0.81,2019:F1=0.88).It proved that the classification features from the VI-AT series have smaller intra-class differences in 2017,2018,and 2019.
基金
partially supported by the National Natural Science Foundation of China[gran numbers 41830108 and 41971321)
Key Research Program of Frontier Sciences,CAS[grant number ZDBS-LY-DQC012]
Major Science and Technology Projects of XPCC[grant number 2018AA00402]
Innovation Team of XPCC’s Key Area[grant number 2018CB004]
Changping Huang was supported by Youth Innovation Promotion Association,CAS(grant number Y2021047).
作者简介
Corresponding author:Changping Huang,huangcp@aircas.ac.cn,Chinese Academy of Science,Aerospace Information Research Institute,Beijing 100094,People’s Republic of China,College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100094,People’s Republic of China。
