Mapping of cropland,cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

作     者：Aqil Tariq Jianguo Yan Alexandre S.Gagnon Mobushir Riaz Khan Faisal Mumtaz Aqil Tariq;Jianguo Yan;Alexandre S. Gagnon;Mobushir Riaz Khan;Faisal Mumtaz

作者机构：State Key Laboratory of Information Engineering in SurveyingMapping and Remote SensingWuhan UniversityWuhanChina School of Biological and Environmental SciencesLiverpool John Moores UniversityLiverpoolUK School of Environmental ScienceCharles Sturt UniversityAlburyAustralia State Key Laboratory of Remote Sensing SciencesAerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences(UCAS)BeijingChina 

出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)

年 卷 期：2023年第26卷第3期

页      面：302-320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 authors wish to thank the editors and anonymous reviewers for their valuable comments and helpful suggestions. The authors would like to thank Stephen C. McClure for his enthusiastic support and valuable suggestions during the review of the manuscript 

主　　题：Sentinel-2 Random Forest cropland crop types cropping patterns Decision Tree Classifier 

摘      要：Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely ***,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral *** study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study *** methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)*** best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML *** methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping *** results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county *** results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.