Improving the remote sensing process of evapotranspiration in the SEBAL algorithm using meta-heuristic models

This paper investigated how the meta-heuristic models can be used to facilitate the estimation of evapotranspiration (ET) images. Focusing on estimating daily ET directly from received images of the electromagnetic bands of Landsat 8 satellite utilizing metaheuristic models, authors used daily ET images estimated by the SEBAL algorithm to calibrate and verify these models. The results of this research showed that the ANN model with DC and RMSE of 0.98 and 0.09025 mm/day, respectively, is more accurate compared to the ACO (with DC = 0.65 and RMSE = 1.45 mm/day) and PSO (with DC = 0.23 and RMSE = 1.60 mm/day) models in the verification stage in estimating daily ET images. The ACO model compared to the PSO model is more accurate in estimating ET images with DC of 0.65 and 0.23 in the verification step, respectively. While removing half of the training data, the accuracy of the PSO model surpasses the ACO model with DC of 0.85 and 0.80, respectively. Also, the ANN model is more accurate than the other two models in estimating ET, both when considering all the data and half of the training data (with DC = 0.98 and RMSE = 0.09 mm/day). Highlights A new methodology is proposed for more efficient ET estimation.; The applications of meta-heuristic models are evaluated in estimating ET.; Meta-heuristics models are calibrated and verified using remotely sensed data.; The most efficient meta-heuristic model in ET images estimation is the ANN model.; The proposed methodology improves the process of ET estimation in SEBAL.