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Using cross-entropy method simulates spatial distribution of rice in Northeast China

【Author in Chinese】 唐鹏钦杨鹏陈仲新李正国游良志刘珍环吴文斌姚艳敏

【Author】 Tang Pengqin 1,2 , Yang Peng 2 , Chen Zhongxin 1,2 , Li Zhengguo 1,2 , You Liangzhi 3 , Liu Zhenhuan 4 , Wu Wenbin 1,2 , Yao Yanmin 1,2 (1. The key Lab of Agro-informatics, Ministry of Agriculture, Beijing 100081, China; 2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 3. International Food Policy Research Institute (IFPRI), Washington DC, 20006, USA; 4. Geography and Planning School of Sun Yat-sen University, Guangzhou, 510275, China )

【Institution】 农业部农业信息技术重点实验室中国农业科学院农业资源与农业区划研究所International Food Policy Research Institute (IFPRI)中山大学地理科学与规划学院

【Abstract in Chinese】 作物时空分布变化是农业研究的重要内容。近30a来,东北地区水稻种植面积显著增加,为探讨东北地区水稻时空变化特征,进一步丰富和完善作物空间分布信息获取方法,研究作物空间分布对包括气候变化在内的多种影响因素的响应关系,该研究综合80年代以来的作物面积与产量统计数据、耕地数据、农业灌溉数据以及作物生长适宜性分布等多源数据,利用基于交叉信息熵原理的作物空间分配模型(spatial production allocation model,SPAM)构建了针对中国作物分布特点的SPAM-China模型,模拟了中国东北地区1980-2008年像元尺度上水稻空间分布信息。结果表明,模拟结果能较好地反映出东北地区水稻主要种植区域,近30a东北地区水稻种植时空变化特征显著,水稻种植区域向北向东扩展,种植重心北移了约1.76个纬度,中北部地区水稻种植面积增加且趋势明显,南部地区变化趋势不显著。

【Abstract】 The crop’s spatial-temporal pattern is critical to many agricultural studies. In the last 30 years, the rice area in Northeast China increased significantly, and has become one of the major rice producing regions in China. To explore the rice spatial-temporal change characteristics and enrich the crop spatial distribution information acquisition methods, this study combined such multi-source datasets as agricultural statistics data, cropland data, irrigation map and crop suitability, and uses a crop spatial distribution model SPAM (Spatial Production Allocation Model) which has been based on the cross-entropy theory and applied successfully in Brazil and Africa. We further modified the model and applied it to China, and named it SPAM-China. This model mainly has four modules that include a data consistency processing module which makes the multisource data a better spatial match, an agricultural statistical data input module, and a multisource data discrimination module that uses GAMS to optimize the procedure and improve the efficiency of the model, and a result output module. With the SPAM-China model, the study simulates the rice spatial distribution of Northeast China on the pixel scale in the past 30 years (1980-2008) and obtains four rice distribution maps. The result shows that the model has a better capacity to simulate rice spatial distribution, and can reflect the main rice cultivation region and temp-spatial change characteristics. In the last 30 years, rice spatial distribution change characteristics were very significant. The rice cultivation area obviously increased, and the cultivation region constantly expanded northeastward. Rice cultivation gravity was northward about 1.76 latitude (140 km) in the nearest 30 years, but the rice cultivation gravity of 2008 is southward about 0.23 latitude (16km) than that of 2000. The rice area rose sharply and has a distinct upward trend in the middle and north of this area, where the north region has the most significant increasing trend. Sanjiang plain and Songnen plain are the main factors for rice increasing in the north region. However, in the south of the region, the rice area change shows no significant trend. On the pixel scale, the rice area of the most pixels has a significant increasing trend and only a few has a decreasing trend in the south of Northeast China. The increasing pixels mainly lie in the middle of Jilin province, south and east of Heilongjiang province. Compared with the remote sensing interpretation crop pattern of Northeast China based on the moderate-resolution satellite MODIS in 2009, this model’s simulation results are validated and prove that the SPAM-China model has a better capacity to simulate the rice spatial distribution of Northeast China on the pixel scale. On the pixel scale, the spatial distribution consistency pixels mainly lie on the traditional rice cultivation region and Sanjiang Plain. Although there is some discrepancy in a few regions, these pixels have a good spatial distribution consistency occupying a greater advantage in pixel amount and rice area. It accounts for 77.20% of the total rice area and 59.57% of the whole rice pixels for SPAM-China and accounts for 91.69% of the rice area and 73.09% of the whole rice pixels for the remote sensing interpretation result. Although the SPAM-China model can better simulate the crop spatial distribution, it must consider the multi-source data consistency, data update, statistics data accuracy, and agricultural and geographical characteristics of study region because these factors can reflect, to a large extent, the simulation accuracy. At the same time, the simulation spatial resolution promotion is also the main direction to improve the model.

【Fund】 全球变化研究国家重大科学研究计划项目(2010CB951502);国家自然科学基金项目(41171328);中央级公益性科研院所专项资金(2013-16);农业部农业信息技术重点实验室开放课题(2012010)共同资助
  • 【Source】 农业工程学报 ,Transactions of the Chinese Society of Agricultural Engineering , Editorial E-mail ,2013(17)
  • 【CLC code】S511;TP79
  • 【Downloads】1931
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