Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation

Aug 5, 2026

Proximal remote sensing can deliver key vegetation biophysical information for predicting land–atmosphere water and energy exchange, specifically latent energy (LE) flux. Traditionally, LE is estimated with process-based models that depend on vegetation parameters that vary through the growing season. This study evaluates machine learning (ML) models using meteorological and proximal sensing inputs at half-hourly resolution across multiple seasons. Results show a model with four environmental predictors plus two proximal sensing variables explains 88% of LE variability, with simpler predictor sets still capturing 81% and 77%, supporting robust estimation when eddy covariance is unavailable.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Latent energy flux and its role in surface energy balance

Measurement methods and remote sensing alternatives

Keywords and article overview

Proximal remote sensing and LE prediction using ML

Agricultural Water Management 317 (2025) 109643 Contents lists available at ScienceDirect Agricultural Water Management journal [homepage:](homepage: www.elsevier.com/locate/agwat)[ www.elsevier.com/locate/agwat](homepage: www.elsevier.com/locate/agwat) | Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation
Srishti Gaura, Guler Aslan-Sungur (Rojda) b , Andy VanLoockeb, Darren T. Drewry a,c,d,*
a Department of Food, Agricultural and Biological Engineering, Ohio State University, Columbus, OH, USA b Department of Agronomy, Iowa State University, Ames, IA, USA
c Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA d Translational Data Analytics Institute, Ohio State University, Columbus, OH, USA | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Proximal remote sensing Explainable machine learning Latent energy flux Evapotranspiration
Surface energy balance Vegetation biophysics | | Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Datadriven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations. | 1. Introduction Latent energy flux (LE) is a land surface process that plays a central role in the surface energy balance (Mallick et al., 2013; Anderson et al., 2011; Mallick et al., 2016). The physiological processes that control LE link the terrestrial water, carbon, and energy budgets (Drewry et al., 2010; Drewry et al., 2010; Mallick et al., 2013; Anderson et al., 2011; Mallick et al., 2016). LE is the energy flux equivalent of evapotranspiration (ET) and therefore defines crop water consumption in agricultural systems, making LE estimation essential for agricultural water management broadly. Example applications of LE estimation in managed systems include irrigation system design (Brombacher et al., 2022; Dela Cruz et al., 2020; Arif et al., 2022), crop water requirement monitoring (Li et al., 2010; Zhang et al., 2023), climate change impact assessmentson agriculture (Castellví and Snyder, 2010; Denich and Bradford, 2010; Gaur et al., 2022; Payero and Irmak, 2008) and characterizing genotypic and agronomic management variations in crop growth (Bai et al., 2024). LE can be measured using direct methods such as field lysimeters that use changes in above-ground mass to estimate water use (Castellví and Snyder, 2010; Denich and

FAQs

Why is latent energy (LE) estimation important for agricultural water management?

LE represents the energy flux equivalent of evapotranspiration (ET), which determines crop water consumption. Accurate LE estimation supports agricultural water management such as irrigation planning and monitoring.

What challenge do traditional process-based LE models face across the growing season?

Process-based models rely on vegetation parameters that change during crop development. This makes predictor stability and accurate parameterization difficult over time.

How effective are machine learning models with proximal remote sensing for predicting LE variability?

A model using four environmental predictors plus two proximal sensing variables captures 88% of LE variability. Using fewer predictors still achieves strong performance, reaching 81% and 77% with reduced input sets.

Aug 5, 2026

Upload by Seraphina