Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation
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 dur...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425003579 |
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| author | Srishti Gaur Guler Aslan-Sungur (Rojda) Andy VanLoocke Darren T. Drewry |
| author_facet | Srishti Gaur Guler Aslan-Sungur (Rojda) Andy VanLoocke Darren T. Drewry |
| author_sort | Srishti Gaur |
| collection | DOAJ |
| description | 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. Data-driven 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. |
| format | Article |
| id | doaj-art-252a1a1c7a694a32a6a9ca775a659059 |
| institution | DOAJ |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-252a1a1c7a694a32a6a9ca775a6590592025-08-20T02:46:09ZengElsevierAgricultural Water Management1873-22832025-08-0131710964310.1016/j.agwat.2025.109643Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimationSrishti Gaur0Guler Aslan-Sungur (Rojda)1Andy VanLoocke2Darren T. Drewry3Department of Food, Agricultural and Biological Engineering, Ohio State University, Columbus, OH, USADepartment of Agronomy, Iowa State University, Ames, IA, USADepartment of Agronomy, Iowa State University, Ames, IA, USADepartment of Food, Agricultural and Biological Engineering, Ohio State University, Columbus, OH, USA; Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA; Translational Data Analytics Institute, Ohio State University, Columbus, OH, USA; Corresponding author at: Department of Food, Agricultural, and Biological Engineering, Ohio State University, Columbus, OH, USA.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. Data-driven 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.http://www.sciencedirect.com/science/article/pii/S0378377425003579Proximal remote sensingExplainable machine learningLatent energy fluxEvapotranspirationSurface energy balanceVegetation biophysics |
| spellingShingle | Srishti Gaur Guler Aslan-Sungur (Rojda) Andy VanLoocke Darren T. Drewry Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation Agricultural Water Management Proximal remote sensing Explainable machine learning Latent energy flux Evapotranspiration Surface energy balance Vegetation biophysics |
| title | Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation |
| title_full | Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation |
| title_fullStr | Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation |
| title_full_unstemmed | Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation |
| title_short | Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation |
| title_sort | explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation |
| topic | Proximal remote sensing Explainable machine learning Latent energy flux Evapotranspiration Surface energy balance Vegetation biophysics |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425003579 |
| work_keys_str_mv | AT srishtigaur explainablemachinelearningtoquantifythevalueofproximalremotesensinginlatentenergyfluxestimation AT guleraslansungurrojda explainablemachinelearningtoquantifythevalueofproximalremotesensinginlatentenergyfluxestimation AT andyvanloocke explainablemachinelearningtoquantifythevalueofproximalremotesensinginlatentenergyfluxestimation AT darrentdrewry explainablemachinelearningtoquantifythevalueofproximalremotesensinginlatentenergyfluxestimation |