基于无人机热成像的棉花根域土壤水分含量估测研究OACSTPCD

In order to explore the inversion law of soil moisture content in the cotton rhizosphere based on the thermal infrared UAV imageing,this study took cotton at the flowering and boll stage as research object,and used plot water stress control experiment and model simulation to obtain the leaf water content and the relative soil water content(RSWC)of each soil layer(0-10,10-20,20-30,30-40,40-50 cm)in the rhizosphere under different drought stress days(2nd,10th and 32nd days after irrigation)at the flowering and boll stage of cotton,and thermal infrared image data of UAV on the 2nd,10th and 32nd days of drought stress.The difference temperature index(DTI),ratio temperature index(RTI),normalized difference temperature index(NDTI)were constructed by combining cotton canopy temperature with leaf temperature and atmospheric temperature,and the indexes(canopy temperature,canopy-high temperature difference,canopy-average temperature difference,DTI2,DTI3,NDTI2,NDTI3)with a correlation of more than 0.6 with RSWC were selected to fit and verify the moisture content of different soil layers.The results showed that:1)With the increase of drought stress days,the moisture content of cotton leaves showed a trend of first increasing and then decreasing,and the relative soil moisture content gradually decreased;2)The precision of the temperature index constructed by canopy temperature with leaf temperature and atmospheric temperature to fit the rhizophere soil water content was better than that of the single use of canopy temperature.The overall trend NDTI3＞DTI3＞DTI2＞NDTI2＞canopy-average temperature difference＞canopy-high temperature difference＞canopy temperature;3)The multiple linear regression model had a high estimation accuracy for the soil moisture content of cotton rhizophere,and the prediction accuracy of 10-20 cm rhizophere soil layer on the 2nd day of drought stress was the highest(R2 is 0.600,RMSE is 0.388);The prediction accuracy of 20-30 cm rhizophere soil layer on the 10th day is the highest(R2 is 0.721,RMSE is 0.267);and the prediction accuracy of 30-40 cm rhizophere soil layer on the 32nd day is the highest(R2 is 0.918,RMSE is 0.068).In conclusion,the cotton canopy temperature obtained by UAV thermal image combined with leaf temperature and atmospheric temperature can be used to fit and estimate soil moisture content in crop root domain,which has practical significance for the development of water-saving irrigation and other soil water regulation and management technologies.