Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum.

Autor: Costa TS; Department of Agricultural Engineering, Center of Agricultural Sciences, Federal University of Viçosa, Viçosa-MG, Brazil., Filgueiras R; Department of Agricultural Engineering, Center of Agricultural Sciences, Federal University of Viçosa, Viçosa-MG, Brazil., Dos Santos RA; Department of Agricultural Engineering, Center of Agricultural Sciences, Federal University of Viçosa, Viçosa-MG, Brazil., Cunha FFD; Department of Agricultural Engineering, Center of Agricultural Sciences, Federal University of Viçosa, Viçosa-MG, Brazil.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 May 11; Vol. 18 (5), pp. e0285535. Date of Electronic Publication: 2023 May 11 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0285535
Abstrakt: The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the models to estimate the actual evapotranspiration (ETr) in agricultural crops irrigated by center pivots. The spectral bands of Sentinel 2A and 2B satellite and vegetation indices formed the first scenario. The second scenario was formed by performing the normalized ratio procedure between bands (NRPB) and joining the variables applied in the first scenario. The models were generated to predict the ETf using six regression algorithms and then compared with ETf calculated by the Simple Algorithm For Evapotranspiration Retrieving (SAFER) algorithm, was considered as the standard. The results possible to select the best model, which in both scenarios was Cubist. Subsequently, ETf was estimated only for the center pivots present in the study area and the classification of land use and cover was accessed through the MapBiomas product. Land use was necessary to enable the calculation of ETr in each scenario, in the center pivots with sugarcane and soybean crops. ETr was estimated using two ETo approaches (EToBrazil and Hargreaves-Samani). It was found that the Hargreaves-Samani equation overestimated ETr with higher errors mainly for center pivots with sugarcane, where systematic error (MBE) ranged from 0.89 to 2.02 mm d-1. The EToBrazil product, on the other hand, presented statistical errors with MBE values ranging from 0.00 to 1.26 mm d-1 for both agricultural crops. Based on the results obtained, it is observed that the ETr can be monitored spatially and temporally without the use of the thermal band, which causes the estimation of this parameter to be performed with greater temporal frequency.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Costa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje