Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

Authors

Robson Argolo dos Santos, Federal University of Viçosa
Everardo Chartuni Mantovani, Federal University of Viçosa
Elpídio Inácio Fernandes-Filho, Federal University of Viçosa
Roberto Filgueiras, Federal University of Viçosa
Rodrigo Dal Sasso Lourenço, Federal University of Viçosa
Vinícius Bof Bufon, Brazilian Agricultural Research Corporation (EMBRAPA) Cerrados
Christopher M. U. Neale, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

9-17-2022

Citation

dos Santos, R.A.; Mantovani, E.C.; Fernandes-Filho, E.I.; Filgueiras, R.; Lourenço, R.D.S.; Bufon, V.B.; Neale, C.M.U. Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms. Atmosphere 2022, 13, 1518. https://doi.org/ 10.3390/atmos13091518

Comments

Open access

Abstract

The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ET_rF (Evapotranspiration Fraction) of sugar can crop using the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model with data from the Sentinel-2 satellites constellation. In order to achieve this goal, images from the MSI sensor (MultiSpectral Instrument) from the Sentinel-2 and the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors from the Landsat-8 were acquired nearly at the same time between the years 2018 and 2020 for sugar cane crops. Images from OLI and TIR sensors were intended to calculate ETrF through METRIC (target variable), while for the MSI sensor images, the explanatory variables were extracted in two approaches, using 10 m (approach 1) and 20 m (approach 2) spatial resolution. The results showed that the algorithms were able to identify patterns in the MSI sensor data to predict the ET_rF of the METRIC model. For approach 1, the best predictions were XgbLinear (R² = 0.80; RMSE = 0.15) and XgbTree (R² = 0.80; RMSE = 0.15). For approach 2, the algorithm that demonstrated superiority was the XgbLinear (R² = 0.91; RMSE = 0.10), respectively. Thus, it became evident that machine learning algorithms, when applied to the MSI sensor, were able to estimate the ET_rF in a simpler way than the one that involves energy balance with the thermal band used in the METRIC model.