摘要
以松嫩平原为研究对象,基于2000-2019年的MODIS时序影像数据,利用归一化差异植被指数(NDVI)及增强植被指数(EVI)构建农作物长势综合监测指标GI(Growth Index),应用差值模型通过与2000-2018年作物生长季长势的平均状况进行对比,评价分析松嫩平原2019年生长季不同月份的作物长势状况。结果表明,2019年松嫩平原作物长势基本呈现略好趋势,且西南部地区作物长势优于北部及东南部地区;2019年7、8月松嫩平原作物长势好于前期(2000-2018年),6月和10月长势较差;综合监测指标GI与单产之间的相关性优于单一的NDVI及EVI指标,说明GI监测结果准确度较高,较单一植被指标更能对大尺度作物长势进行有效评价,并能对作物产量进行及时预测预报。
Taking the Songnen Plain as the research object,a method for large-scale monitoring of crop growth was proposed based on the MODIS time series image data from 2000 to 2019,which can quickly obtain the crop growth information in the latest remote sensing images.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)were used to construct a comprehensive growth monitoring index GI(growth index).The difference model was used to evaluate and analyze the growth conditions of crops in different months of the growing season in 2019 in the Songnen Plain by comparing with the average growth conditions of crops in growing season from 2000 to 2018.The results showed that in 2019,the crop growth in the Songnen Plain was slightly better than that in the normal years,and the crop growth in the southwestern region was better than that in the northern and southeastern regions.In July and August of 2019,the growth of crops in the Songnen Plain was better than that in the previous period(2000-2018),and the crop growth in June and October was worse than that in the previous period.The correlation between comprehensive growth monitoring index(GI)and per unit yield was much better than that with the single NDVI and EVI index.This indicates that GI monitoring results have higher accuracy,and GI is more effective in evaluating large-scale crop growth than single vegetation index,and can be used to predict crop yield in time.
作者
赵鑫
那晓东
ZHAO Xin;NA Xiao-dong(Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions,Harbin Normal University,Harbin 150025,China)
出处
《地理与地理信息科学》
CSCD
北大核心
2022年第4期34-39,共6页
Geography and Geo-Information Science
基金
黑龙江省自然科学基金项目(YQ2020D005)。
