摘要
吉林省是中国重要的粮食主产区和商品粮生产基地,全面、客观地揭示吉林省近20年耕地生产力水平,对挖掘区域增产潜力,推动耕地资源可持续利用,保障区域粮食安全具有重要意义。本文基于Google Earth Engine(GEE)平台提取2000—2019年吉林省MODISEVI数据,结合变异系数和SenMann Kendall趋势检验,构建耕地生产力时空分析方法,研究吉林省20年耕地生产力时空变化、稳定性及变化趋势。结果表明:吉林省耕地生产力在20年间整体处于上升趋势,中西部耕地集中连片区生产力的变化趋势好于东部较为破碎耕地片区,西部耕地与东部耕地生产力差距逐渐缩小。吉林省东部山区的耕地生产力高于西部平原区,耕地生产力低值区位于城镇周边和西北部盐碱泡沼分布密集的低洼地带;高值区集中在河流附近耕地连片区域。不同地貌类型成因中,湖成地貌、风成地貌条件下耕地生产力低于流水地貌和火山熔岩地貌。耕地生产力稳定性呈东南高西北低的趋势,其中水田的生产力稳定性优于旱地和水浇地。利用时序遥感数据监测吉林省耕地生产力,可为高标准农田建设、耕地质量提升等工程的开展提供技术支撑。
The spatiotemporal change characteristics of cultivated land productivity(CLP)are essential to ensure regional food security and promote the sustainable use of cultivated land resources.Jilin Province is an important major grain production area and commodity grain production base in China.If the cultivated land productivity level of Jilin Province over the past 20 years can be both quickly and fully reflected,it would be helpful to discover the potential of increasing production of this area.Based on the hypothesis that the EVImax was a proxy of cultivated land productivity,the moderate resolution imaging spectroradiometer(MODIS)data with 250 m spatial resolution and 16 d temporal resolution in Google Earth Engine(GEE)platform was used to calculate CLP in Jilin Province.The coefficient of variation method was used to study the stability of cultivated land productivity.The level of CLP was explored by using the EVImax mean from 2000 to 2019,and the changing trend and amplitude of CLP was analyzed in study period by using both TheilSen median trend analysis and the MannKendall(MK)test.The result of the trend and stability were integrated to acquire the level of CLP.Using time series remote sensing data to monitor regional crop growth and yield levels can objectively assess the productivity of cultivated land,and provide technical support for farmland construction and improvement of cultivated land quality.The results showed that the overall change amplitude was dominated by a slightly increasing trend.In cultivated land concentrated area,the change trend of productivity was better than that of broken cultivated land area in the east.The area with improvement of CLP accounted for 95.66%,mainly distributed in the central and northwestern of Jilin.The area with degradation of CLP accounted for 4.34%,mainly distributed in the southeast of Jilin or the surrounding of cities.The CLP level was generally high in the southeast and low in the northwest in cultivated land in Jilin,China.The area with the lowest productivity was located in the northwest of Jilin.The low-value areas of CLP were located in low-lying areas around towns and in low-lying areas where saline-alkali marshes were distributed in the northwest.High-value areas of CLP were concentrated in contiguous areas of farmland near rivers.In terms of the genesis of different landform types,volcanic lava landforms had the highest productivity of cultivated land,followed with flowing water landforms,wind-formed landforms and lacustrine landforms.The stability of CLP showed a trend of higher in southeast and lower in northwest.The productivity stability of paddy field was better than that of dryland and irrigated land.Therefore,the results indicated that time series remote sensing data can be used to objectively monitor the CLP.Different strategies of cultivated land protection and productivity promotion should be implemented in Jilin Province,such as stabilizing the eastern grain-producing areas,increasing the central grain production core area,and focusing on the western grain production reserve areas.
作者
张超
陈婉铃
马佳妮
郧文聚
高璐璐
王欢
ZHANG Chao;CHEN Wanling;MA Jiani;YUN Wenju;GAO Lulu;WANG Huan(College of Land Science and Technology,China Agricultural University,Beijing 100083,China;Key Laboratory for Agricultural Land Quality Monitoring and Control,Ministry of Natural Resources,Beijing 100035,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第2期158-166,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2021YFD1500202)。
关键词
耕地生产力
增强型植被指数
时序分析
谷歌地球引擎
MODIS
cultivated land productivity
enhanced vegetation index
time series analysis
Google Earth Engine
MODIS