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

文献类型： 外文期刊

作者： XIA, Tian 1 ; WU, Wen-bin 2 ; ZHOU, Qing-bo 1 ; VERBURG, Peter H. 4 ; YANG, Peng 2 ; HU, Qiong 1 ; YE, Li-ming 5 ; ZHU, Xiao-juan 4 ;

作者机构： 1.Cent China Normal Univ, Hubei Prov Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China

3.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

4.Vrije Univ Amsterdam, Inst Environm Studies, NL-1085 Amsterdam, Netherlands

5.Univ Ghent, Dept Geol, B-9000 Ghent, Belgium

6.China Univ Polit Sci & Law, Commercial & Econ Law Sch, Beijing 100088, Peoples R China

关键词： crop planting pattern; spatialization; simulation; spatiotemporal change; remote sensing

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.384； 五年影响因子：4.021 ）

ISSN： 2095-3119

年卷期： 2022 年 21 卷 6 期

页码：

收录情况： SCI

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.

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