首页|基于多源遥感数据与模型对比的冬小麦土壤含水量区域监测研究

基于多源遥感数据与模型对比的冬小麦土壤含水量区域监测研究

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实时、精准的土壤水分含量监测是农业用水管理的基础,探究冬小麦土壤水分反演的最优模型对于提高农业用水效率和可持续发展均具有重要的意义.本研究以河南省鹤壁市浚县冬小麦种植区域的土壤水分含量为研究对象,采用无人机遥感数据、卫星遥感数据、田间采样数据,分别运用温度植被干旱指数模型、水云模型和改进的水云模型3种方法,进行土壤含水量反演对比分析与最优模型选择.结果表明,3种方法中10cm深度的反演精度均高于20cm,且R2均大于0.4.其中采用改进的水云模型方法在10 cm深度的R2为0.7055、RMSE为0.0209,20 cm深度的R2为0.5069、RMSE为0.0271,优于水云模型和温度植被干旱指数的反演效果.因此,改进的水云模型是一种适合用于麦田土壤水分反演的方法,它能够提供较高的反演精度.
Regional Monitoring of Soil Moisture Content in Winter Wheat Field Based on Multi-source Remote Sensing Data and Optimal Model Selection
Real-time and accurate monitoring of soil moisture content is the foundation of agricultural water management.Exploring the optimal model for soil moisture inversion in winter wheat is of great significance for improving agricultural water efficiency and sustainable development.This study took the soil moisture content in the winter wheat planting area of Jun County,Hebi City,Henan Province as the research object.Using unmanned aerial vehicle remote sensing data,satellite remote sensing data and field sampling data,three methods of temperature vegetation drought index model,water cloud model and improved water cloud model were used to perform comparative analysis of soil water content inversion and optimal model selection.The results showed that the inversion accuracy at a depth of 10 cm was higher than that in 20 cm in all three methods,and R2 was greater than 0.4.The use of an improved water cloud model method resulted in R2 of 0.7055 and RMSE of 0.0209 at a depth of 10 cm,R2 of 0.5069 and RMSE of 0.0271 at a depth of 20 cm,which was superior to the inversion effect of water cloud model and temperature vegetation drought index.This indicated that using the improved water cloud model method for wheat field soil water inversion was appropriate and had high inversion accuracy.

winter wheatmonitoring of soil moisture contentsoil moisture inversioninversion accuracyUAV remote sensingsatellite remote sensingtemperature vegetation drought index modelwater cloud model

吴东丽、刘聪、郭超凡、丁明明、吴苏、阙艳红、姜明梁、李雁

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中国气象局气象探测中心,北京 100081

中国气象局大气探测重点开放实验室,北京 100081

中国气象局气象探测工程技术研究中心,北京 100081

衢州学院,浙江衢州 324000

中国电子科技集团公司第二十七研究所,郑州 450047

河南中原光电测控技术有限公司,郑州 450047

中国农业科学院农田灌溉研究所,河南新乡 453002

中国气象科学研究院,北京 100081

中国气象局气象发展与规划院,北京 100081

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冬小麦 土壤水分含量监测 土壤水分反演 反演精度 无人机遥感 卫星遥感 温度植被干旱指数模型 水云模型

国家自然科学基金项目国家重点研发计划联合基金开放课题

421713032022YFC3090602U2021Z07

2024

中国农学通报
中国农学会

中国农学通报

CSTPCD
影响因子：0.891
ISSN：1000-6850
年,卷(期)：2024.40(25)
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