摘要
土壤墒情是农业生产的重要参数,高效、精确地监测土壤墒情是保证农业生产安全的重要环节。在基于植被指数的土壤墒情监测方法中,如何根据地域和植物生育期的差异,选定最优的植被指数是问题的关键。利用2015年2-6月河南省中东部黄淮海平原冬小麦主产区的实测农田墒情数据和MODIS遥感数据,对归一化植被指数(NDVI)、增强型植被指数(EVI)、植被供水指数(VSWI)、温度植被干旱指数(TVDI)与土壤含水量进行相关性分析,对比不同植被指数对不同深度土壤含水量的响应,并分别建立四种植被指数与土壤墒情的回归模型,进行预测精度分析。结果表明:本研究中,TVDI与土壤墒情的相关性最好,预测精度最高;每一种指数皆与10~20 cm深度土壤墒情相关性最好,预测精度最高。因此,TVDI为最优响应指数,10~20 cm深度土壤为最优响应深度。
Soil moisture is an important parameter for agricultural production.Efficient and accurate monitoring of soil moisture is an important part of ensuring agricultural production safety.For the soil moisture monitoring methods based on vegetation index,how to select the optimal vegetation index according to the difference of region and plant growth period is the key problem.In this paper,using the measured farmland moisture data and MODIS remote sensing data of the main winter wheat producing areas in the Huang-Huai-Hai Plain in the central and eastern Henan Province from February to June 2015,the correlation between normalized difference vegetation index(NDVI),enhanced vegetation index(EVI),vegetation water supply index(VSWI),temperature vegetation dryness index(TVDI)and soil water content was analyzed,the responses of different vegetation indexes to soil moisture content at different depths was compared,and the regression models of four mulch indexes and soil moisture content were established to analyze the prediction accuracy.The results showed that:in this study,TVDI had the best correlation with soil moisture and the highest prediction accuracy.Each index had the best correlation with soil moisture at 10~20 cm depth and the highest prediction accuracy.Therefore,TVDI was the optimal response index,and the soil moisture at 10~20 cm depth was the optimal response depth.
作者
王金鑫
于百顺
李聪玲
姚静
WANG Jin-xin;YU Bai-shun;LI Cong-ling;YAO Jing(School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 454001, China)
出处
《节水灌溉》
北大核心
2020年第3期50-56,共7页
Water Saving Irrigation
基金
河南省科技攻关项目(182102210017)。
关键词
土壤墒情
植被指数
遥感监测
soil moisture
vegetation index
remote sensing monitoring
response
Henan province
