From statistics to grids:A two-level model to simulate crop pattern dynamics  

作　　者：XIA Tian WU Wen-bin ZHOU Qing-bo Peter HVERBURG YANG Peng HU Qiong YE Li-ming ZHU Xiao-juan 

机构地区：[1]Key Laboratory for Geographical Process Analysis&Simulation,Hubei Province/College of Urban&Environmental Science,Central China Normal University,Wuhan 430079,P.R.China [2]Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,P.R.China [3]Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,P.R.China [4]Institute for Environmental Studies,VU University Amsterdam,Amsterdam 1085,The Netherlands [5]Department of Geology,Ghent University,Ghent 9000,Belgium [6]Commercial and Economic Law School,China University of Political Science and Law,Beijing 100088,P.R.China

出　　处：《Journal of Integrative Agriculture》2022年第6期1786-1798,共13页农业科学学报（英文版）

基　　金：supported and financed by the National key Research and Development Program of China(2019YFA0607400);the Fundamental Research Funds for the Central Universities, China (CCNU19TS045)

摘　　要：Crop planting patterns are an important component of agricultural land systems.These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.However,the extent of these changes and their possible impacts on the environment,terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.To fill this gap,this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.This method features a two-level model that combines a land-use simulation and a crop pattern simulation.The output of the first level is the spatial distribution of the cropland,which is then used as the input for the second level,which allocates crop censuses to individual gridded cells according to certain rules.The method was tested using data from 2000 to 2019 from Heilongjiang Province,China,and was validated using remote sensing images.The results show that this method has high accuracy for crop area spatialization.Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

关 键 词：crop planting pattern SPATIALIZATION simulation spatiotemporal change remote sensing 

分 类 号：S126[农业科学—农业基础科学]