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

Regional Monitoring of Soil Moisture Content in Winter Wheat Field Based on Multi-source Remote Sensing Data and Optimal Model Selection

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10 cm深度的反演精度均高于20 cm,且R^(2)均大于0.4。其中采用改进的水云模型方法在10 cm深度的R^(2)为0.7055、RMSE为0.0209,20 cm深度的R^(2)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。

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 R^(2)was greater than 0.4.The use of an improved water cloud model method resulted in R^(2)of 0.7055 and RMSE of 0.0209 at a depth of 10 cm,R^(2)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.