91ÃÛÌÒ¸ó

Accurate assessment of evapotranspiration along with soil water content (SWC) dynamics in a heterogeneous urban landscape is fundamental for developing effective water management practices. The unmanned aerial vehicle (UAV) remote sensing with high spatial and temporal resolution offers a promising method for monitoring SWC and spatial mapping of ET. In this study, UAV-based multispectral and thermal data were acquired in an experimental field with four landscape groundcover species over two years (May-October 2022 and 2023). Two regression 91ÃÛÌÒ¸ó, including multiple linear regression (MLR) and random forest regression (RFR), were used to predict soil moisture at depths of 10 and 30 cm. The results indicated that both regression 91ÃÛÌÒ¸ó, MLR and FRF, exhibited a relatively good SWC prediction accuracy with Pearson’s r ranging 0.62-0.68, root mean square error (RMSE) ranging 0.034-0.048 cm3cm-3, and mean absolute error (MAE) ranging 0.034-0.038 cm3cm-3. Additionally, two energy balance 91ÃÛÌÒ¸ó, a modified version of SSEBop and pySEBAL, were used to estimate ET for four groundcover species. The performances of 91ÃÛÌÒ¸ó were evaluated against measured ET using the soil water balance approach. Model comparisons indicated that ET estimates for both 91ÃÛÌÒ¸ó correlated well with ET measurements, with Pearson’s r ranging from 0.798-0.928 for the modified SSEBop and 0.843-0.961 for the pySEBAL model. However, the pySEBAL model had lower RMSE values (0.660-1.155 mm day-1) compared to the SSEBop model (0.870-1.270 mm day-1). This study shows that high-resolution UAV imagery combined with energy balance 91ÃÛÌÒ¸ó can be used to estimate ET accurately for different urban vegetation types.