Alavi, Mohammad; Albaji, Mohammad 
ORCID: https://orcid.org/0000-0002-5483-5834; Golabi, Mona; Ali Naseri, Abd et Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356
(2024).
Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models.
Journal of Hydrology
, vol. 629
.
p. 130605.
DOI: 10.1016/j.jhydrol.2023.130605.
Résumé
Rapid and accurate crop evapotranspiration (ETc) estimation is essential for water resource management of irrigated lands. Nevertheless, due to the significant uncertainties in the inputs of conventional ETc equations and the inaccessibility of their proper spatial and temporal distribution values, precision agriculture requires a more accurate evaluation of ETc using efficient methods. Combining remote sensing (RS) data and machine learning (ML) techniques has provided a considerable capacity for estimating ETc, which can address these challenges. This study aims to develop a framework for ETc estimation based on the four most popular tree-based ML algorithms, namely M5-pruned (M5P), random forest regression (RFR), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). For this purpose, multisource RS data and meteorological and ground measurements (Meteo-GM) were used during three growing seasons (2018–2021) in irrigated croplands of the Sugarcane & By-Products Development Company in Khuzestan, southwest Iran. Since the unavailability of optical sensors, e.g., Landsat-8 data, in cloudy conditions restricts their enduring application, an alternative approach employing the Sentinel-1 Synthetic Aperture Radar (SAR) data for ETc estimation was also proposed. A set of 23 scenarios of various unique combinations of the available parameters was developed to ensure that a proper decision could be made for ETc estimation under any conditions. Eventually, the contribution of the input variables to estimate ETc was evaluated using the Sobol sensitivity analysis. The results showed that the RFR algorithm provided the most accurate ETc estimation in all scenarios, followed by the XGBoost, GBRT, and M5P algorithms (R2 = 0.92–0.99, RMSE = 2.02–0.32 mm d-1). The Meteo-GM models’ performances were improved by incorporating optical and thermal infrared (TIR) (R2 = 0.99, RMSE = 0.32 mm d-1) and SAR (R2 = 0.98, RMSE = 0.65 mm d-1) RS data. In addition, Sobol’s sensitivity analysis revealed that maximum temperature, wind speed, pan evaporation, leaf area index, moisture stress index, and inverse dual-pol diagonal distance were the most influential input variables in the ETc estimation. This study can be used as a reference to simplify the ETc calculations using a novel application of ML as a helpful basis for precision irrigation management from the point to the regional scale in any weather conditions.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | crop evapotranspiration; multisource remote sensing data; machine learning; random forest; XGBoost; sobol analyze |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 31 juill. 2024 20:03 |
| Dernière modification: | 31 juill. 2024 20:03 |
| URI: | https://espace.inrs.ca/id/eprint/15483 |
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