Inversion model of surface bare soil temperature and water content based on UAV thermal infrared remote sensing

Abstract

Soil moisture content is important to agricultural monitoring, ecological restoration and landslide warning. Traditional methods cannot monitor soil moisture content in large area, high precision and continuous data. Unmanned aerial vehicle (UAV) remote sensing monitoring has enabled wide-scale water content monitoring, improving the spatial resolution accuracy to centimeter level and the temporal resolution accuracy to minute level, which makes UAV remote sensing favorable for soil moisture content monitoring. In this paper, to achieve rapid and accurate inversion data of bare surface soil moisture content via UAV thermal infrared remote sensing, UAV is used to obtain a visible light map of the experiment area and a thermal infrared radiation map at 9:00 am, 1:00 pm, and 6:00 pm in a given day. Gray value of thermal infrared radiation is based on Planck’s blackbody radiation law and quadratic polynomial fitting to invert temperature data; the ground soil temperature and humidity sensor provides sample areas of the measured temperature and soil moisture content. The linear relationship between gray value of thermal infrared radiation, brightness temperature, measured temperature, and soil moisture content was obtained by analyzing the data. The experimental results show that there is a good linear relationship between the brightness temperature and water content, and the brightness t temperature R2 at 6 PM is better than the measured one. Finally, a model for calculating the surface bare soil moisture content based on brightness temperature is established. RMSE and RPD were used to verify the accuracy of the inversion model. The model accuracy verification results show that the inversion model at 6 pm has a good accuracy, which verifies the feasibility and accuracy of unmanned aerial vehicle thermal infrared remote sensing monitoring of surface bare soil moisture content.