Assessing soil moisture levels using visible UAV imagery and machine learning models

The estimation of soil moisture (SM) as an important variable in the hydrological cycle of nature is necessary for the optimal management of water and soil resources. One of the indirect methods to estimate SM is using visible imagery with unmanned aerial vehicles (UAVs). This study aims to evaluate the potential of visible UAV imagery for estimating SM in a bare soil field in Iran. In this study, M5 tree (M5P), random forest (RF), sequential minimal optimization regression (SMOreg), and multilayer perceptron (MLP) methods have been used for SM modeling from RGB (Red, Green and Blue) bands and brightness and intensity indices of aerial imagery. Three evaluation methods were used to assess the accuracy of the models, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Four different indices, including difference index (DI), ratio index (RI), normalized difference index (NDI), and perpendicular index (PI), were used to estimate SM. The green and red bands pair were found to be the optimal bands for SM estimation. The findings showed that the PI index provided the most accurate SM estimates (R2 = 0.51). The RF model predicted SM most accurately among the machine learning models tested (R2 = 0.67). However, all models underestimated SM content in high-moisture areas and overestimated it in low-moisture areas, with the MLP model showing the most significant overestimation. All the indices were saturated beyond 25% SM. In general, this study highlighted the potential of aerial RGB imagery and associated indices for assessing SM levels within bare soil fields. However, it should be noted that the use of individual bands and indices alone is not sufficient to make an accurate estimate of SM.