Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables

Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin and Chai Hoon Koo

Agricultural Water Management, 2025, vol. 315, issue C

Abstract: Effective water resources management and irrigation scheduling for agricultural sector highly depend on the precise estimation of reference evapotranspiration, ETo. This study aims to develop ETo estimation models using deep learning algorithms with remote sensing variables as the input variables at Pulau Langkawi and Kuantan stations, located in Peninsular Malaysia. Support vector regressor (SVR) was found to satisfactorily estimate the daytime land surface temperature (LST) using a set of significant variables including meteorological and remote sensing variables. It was then used along with downward shortwave radiation and surface reflectance bands to estimate ETo. Both long short-term memory (LSTM) and gated recurrent unit (GRU) showed their equivalent capability in estimating ETo and achieved the highest R2 of 0.695 and 0.796, respectively. The proposed hybrid deep learning models, combined model of convolutional neural network (CNN) with LSTM and GRU, respectively, achieved higher accuracy compared to individual models. They managed to improve the accuracy of the prediction in most of the cases, with the highest R2 = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. It was shown that the incorporation of surface reflectance bands and auxiliary variables (day length, Julian day and solar zenith angle) enhanced the performance of the models. This study provides valuable insights into deep learning algorithms and further confirms the potential of remote sensing variables as an alternative data source for ETo estimation.

Keywords: MODIS aqua; Long short-term memory; Gated recurrent unit; Downward shortwave radiation; Satellite; Land surface temperature (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377425002483
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:315:y:2025:i:c:s0378377425002483

DOI: 10.1016/j.agwat.2025.109534

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().