An Improved UAV-Based ATI Method Incorporating Solar Radiation for Farm-Scale Bare Soil Moisture Measurement

The use of UAV-based remote sensing for soil moisture has developed rapidly in recent decades, with advantages such as high spatial resolution, flexible work arrangement, and ease of operation. In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal infrared data from UAV-based remote sensing, has been widely used for soil moisture estimation at the field scale. However, the ATI method may not perform well under inconsistent weather conditions due to inconsistency of the intensity of the soil surface energy input. In this study, an improvement of the ATI method (ATI-R), considering the variation in soil surface energy input, was developed by the incorporation of solar radiation measurements. The performances of the two methods were compared using field experiment data during multiple heating processes under various weather conditions. It showed that on consistently sunny days, both ATI-R and ATI methods obtained good correlations with the volumetric water contents (VWC) (R2ATI-R = 0.775, RMSEATI-R = 0.023 cm3·cm−3 and R2ATI = 0.778, RMSEATI = 0.018 cm3·cm−3) on cloudy or a combination of sunny and cloudy days as long as there were significant soil-heating processes despite the different energy input intensities; the ATI-R method could perform better than the ATI method (cloudy: R2ATI-R = 0.565, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.156, RMSEATI = 0.033 cm3·cm−3; combined: R2ATI-R = 0.673, RMSEATI-R = 0.028 cm3·cm−3 and R2ATI = 0.310, RMSEATI = 0.032 cm3·cm−3); and on overcast days, both the ATI-R and ATI methods could not perform satisfactorily (R2ATI-R = 0.027, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.027, RMSEATI = 0.031 cm3·cm−3). The results indicate that supplemental solar radiation data could effectively expand applications of the ATI method, especially for inconsistent weather conditions.