D. Drewry
Environmental Science · The Ohio State University
Publications
101
Citations
3,094
Est. group size
~1
Recurring co-author estimate
Active years
32
Publishing since 1995
D. Drewry's research focuses on understanding and predicting how plants and land surfaces exchange water, heat, and carbon with the atmosphere, using a combination of physical models and machine learning. Recent work applies remote and proximal sensing (e.g., drone-based thermal imaging, hyperspectral and near-infrared data) together with data-driven models to estimate quantities like evapotranspiration (water loss from soil and plants), stomatal conductance (how plants regulate water and gas exchange through leaf pores), and soil heat flux across agricultural and forest ecosystems. This work supports applications in agriculture, such as crop yield estimation, drought monitoring, and soil property assessment.
Publication output was steady in 2017-2019, dropped to near zero in 2020-2022, then picked up again from 2023 onward with a moderate, fairly consistent pace in recent years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Machine Learning and Proximal Sensing for Predicting Evapotranspiration of Agricultural Systems
2026
- A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing
Water Resources Research · 2026
- Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing
Illinois Data Bank · 2026
- Parsimonious models of root zone temperature in soilless substrates through ensemble machine learning
Smart Agricultural Technology · 2025
- Non-destructive prediction of nitrogen, iron and zinc content in diverse common bean seeds from a genebank using near-infrared spectroscopy
Food Chemistry Molecular Sciences · 2025
- Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation
Agricultural Water Management · 2025
- Ensemble machine learning for interpretable soil heat flux estimation
2025
- Explainable machine learning for predicting stomatal conductance across multiple plant functional types
Agricultural and Forest Meteorology · 2024
- Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model
Agronomy · 2024
- Ensemble machine learning for interpretable soil heat flux estimation
Ecological Informatics · 2024
- Non-invasive diagnosis of wheat stripe rust progression using hyperspectral reflectance
Frontiers in Plant Science · 2024
- Soil and Atmospheric Drought Explain the Biophysical Conductance Responses in Diagnostic and Prognostic Evaporation Models Over Two Contrasting European Forest Sites
Journal of Geophysical Research Biogeosciences · 2024
- Simulating the field-scale potential of natural variation in soybean leaf optical properties on carbon assimilation and water use
2023
- Desarrollo de métodos de análisis de espectroscopia y algoritmos de aprendizaje automático para la evaluación de algunas propiedades del suelo en Costa Rica
Agronomía Costarricense · 2020
- Remote Quantification of Land Surface Temperature and Evapotranspiration Using Thermal Infrared Observations from Unmanned Aerial Systems
2020
- AGU Fall Meeting Abstracts×8
- Water Resources Research×3
- AGUFM×3
- Remote Sensing of Environment×2
- Journal of Geophysical Research Biogeosciences×2
- Xing Li
Environmental Science · Purdue University West Lafayette
- Kim Novick
Environmental Science · Indiana University
- S. R. Saleska
Environmental Science · The Ohio State University
- Gil Bohrer
Environmental Science · The Ohio State University
- Mallory L. Barnes
Environmental Science · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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