Cross-estimation of Soil Moisture Using Thermal Infrared Images with Different Resolutions

Land use, land cover, and carbon dioxide emission reduction have been crucial topics among scholars considering the prevention of global warming in recent years. The application of satellite remote sensing technology and aerial images using unmanned aerial vehicles (UAV) equipped with sensors has become common in large-scale environmental monitoring. In this experimental study, we used satellite images and aerial images to quantitatively analyze the effect of soil moisture on the regional thermal environment. Empirical equations of soil linearity, temperature vegetation dryness index (TVDI), and apparent thermal inertia (ATI), among other parameters, were compared. The study area was farmland in Hsinchu, Taiwan. Land surface temperature (LST) was measured using a multiplatform infrared sensor, and actual soil moisture was measured using a soil moisture meter on-site. The parameters for estimating the moisture of surface soil (i.e., land surface temperature, normalized difference vegetation index (NDVI), images of thermal inertia, and empirical equations of soil moisture) were obtained using a geographical information system (GIS). Finally, the soil moisture estimations were compared with on-site soil moisture measurements. Regression analysis was employed to compare the correlation between satellite and aerial data to establish a surface-soil moisture estimation model in areas of different land covers. The results indicated that the soil moisture estimated using the empirical equation of soil linearity was closest to that in actual measurements, followed by the estimations based on the temperature vegetation dryness index and apparent thermal inertia.