Machine Learning and Proximal Sensing for Predicting Evapotranspiration of Agricultural Systems [* Cross-Reference *]

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D. Drewry; James Cross; Sana Zeeshan Shirazi; Srishti Gaur; Kanishka Mallick; Guler (Rojda) Aslan-Sungur; Andy VanLoocke, 2026, "Machine Learning and Proximal Sensing for Predicting Evapotranspiration of Agricultural Systems [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.DI4EOY, LIST Open Repository, V1

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Description	Machine learning methods provide a powerful basis for developing flexible, non-parametric models of complex phenomena and have demonstrated strong predictive capabilities across many areas of the physical sciences generally and the earth sciences specifically. While machine learning methods have been demonstrated to be flexible predictive tools capable of integrating diverse data streams, they present significant challenges in terms of interpretability and generalizability. This is especially true in the context of ecohydrological or biophysical systems, where the objective is often to develop a better understanding of the underlying system rather than exclusively improve predictive performance. There is a growing recognition that interpretability, physical consistency, and data complexity are key challenges in the successful adoption of machine learning methodologies. Here we evaluate the application of machine learning methods to produce models for land-atmosphere water vapor exchange across a set of diverse agricultural systems. Specific focus is placed on the use of environmental and proximal sensing information to develop simple and effective models of evapotranspiration using both machine learning and hybrid modeling approaches that leverage the advantages of machine learning and biophysical simulation. Emphasis is placed on parsimonious model development and interpretability of model performance. (2026-03-13) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu26-2168 for the original and latest version of the publication* (2026-07-01)
Subject	Earth and Environmental Sciences; Physics
Keyword	Interpretability, Leverage (statistics), Context (archaeology), Evapotranspiration, Predictive modelling, Set (abstract data type), Supervised learning
License/Data Use Agreement	CC BY 4.0


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Citation Metadata

Persistent Identifier	perma:LIST.DI4EOY
Publication Date	2026-07-06
Title	Machine Learning and Proximal Sensing for Predicting Evapotranspiration of Agricultural Systems [* Cross-Reference *]
Other Identifier	https://doi.org/10.5194/egusphere-egu26-2168 OpenAlex ID: https://openalex.org/W7135213127
Author	(The Ohio State University)https://orcid.org/0000-0003-2593-7599 James Cross (The Ohio State University) (The Ohio State University)https://orcid.org/0000-0003-4389-3358 (University of Michigan)https://orcid.org/0000-0002-5361-4474 Kanishka Mallick (Luxembourg Institute of Science and Technology) Guler (Rojda) Aslan-Sungur (Iowa State University) (Iowa State University)https://orcid.org/0000-0002-7516-8479
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	Machine learning methods provide a powerful basis for developing flexible, non-parametric models of complex phenomena and have demonstrated strong predictive capabilities across many areas of the physical sciences generally and the earth sciences specifically. While machine learning methods have been demonstrated to be flexible predictive tools capable of integrating diverse data streams, they present significant challenges in terms of interpretability and generalizability. This is especially true in the context of ecohydrological or biophysical systems, where the objective is often to develop a better understanding of the underlying system rather than exclusively improve predictive performance. There is a growing recognition that interpretability, physical consistency, and data complexity are key challenges in the successful adoption of machine learning methodologies. Here we evaluate the application of machine learning methods to produce models for land-atmosphere water vapor exchange across a set of diverse agricultural systems. Specific focus is placed on the use of environmental and proximal sensing information to develop simple and effective models of evapotranspiration using both machine learning and hybrid modeling approaches that leverage the advantages of machine learning and biophysical simulation. Emphasis is placed on parsimonious model development and interpretability of model performance. (2026-03-13) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu26-2168 for the original and latest version of the publication* (2026-07-01)
Subject	Earth and Environmental Sciences; Physics
Keyword	Interpretability Leverage (statistics) Context (archaeology) Evapotranspiration Predictive modelling Set (abstract data type) Supervised learning
Topic Classification	Plant Water Relations and Carbon Dynamics Hydrological Forecasting Using AI Soil Moisture and Remote Sensing
Deposit Date	2026-03-13
Data Type	Article

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