About

Paul Stoy is a Professor in the Biological Systems Engineering department at the University of Wisconsin–Madison. He holds a B.A. in Zoology and Botany from the University of Wisconsin–Madison and a Ph.D. in Ecology from Duke University. His research focuses on surface-atmosphere exchange, ecosystem ecology, and natural resource management. Stoy has contributed to understanding ecosystem evaporation and transpiration, carbon exchange variability, and the impacts of deforestation on climate. His work includes investigating mechanisms of surface energy balance, methane flux in tropical peat forests, and the effects of land use changes on climate and ecosystem functions. He has been recognized as a Highly Cited Researcher and has received awards such as the Outstanding Editor Award from Biogeosciences and the Humboldt Research Fellowship.

Research topics

• Geology
• Environmental science
• Atmospheric sciences
• Geography
• Biology
• Ecology
• Meteorology
• Soil science
• Physics
• Economics
• Chemistry
• Climatology
• Botany
• Materials science
• Engineering
• Statistics
• Remote sensing
• Economic growth
• Physical geography
• Mathematics

Selected publications

• Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

  Nature Communications · 2026-01-07 · 1 citations

  articleOpen access

  Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C3 foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol-1 year-1, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm. Climate change is altering how plants balance carbon gain and water loss. This study maps global leaf-level water-use efficiency over the past two decades, showing it is highest in regions that are cold or dry, increasing worldwide, and strongly influenced by atmospheric dryness.

  PublisherDOI

• Event‐Specific Meteorological Drivers Shape Diverse Water‐Use Efficiency Trajectories Under Drought Propagation

  Global Change Biology · 2026-01-01

  article

  Drought propagation from meteorological to soil drought marks a critical phase in regulating vegetation water-carbon dynamics, yet the response trajectories of water-use efficiency (WUE) during these events remain poorly understood. Here, combining global flux tower observations with simulations from Earth System Models (ESMs), we quantified the spatiotemporal patterns of drought propagation characteristics, identified WUE response trajectories for characteristic-specific droughts, and investigated their dominant drivers. We found that 58% of soil droughts follow meteorological droughts. Most sites experience drought propagation events with increasing intensity, faster propagation, and shorter intervals. Among all identifiable trajectories, nonmonotonic patterns account for approximately 60%. WUE response trajectories generally follow continuous and nonmonotonic patterns, dominated by a rise-then-fall pattern. This pattern reflects a process in which vegetation functions regulated by stomatal behavior are initially stressed and then partially recover. Intra-site variability is extremely pronounced, mainly driven by event-specific thermal factors such as air temperature and net radiation. ESMs reproduce broad site-level prevalence of nonmonotonic patterns, but show discrepancies in the relative importance of drivers due to the coupling of different land surface models. These findings challenge the notion of fixed vegetation functional responses and highlight the dynamic variability of response trajectories in relation to event-specific characteristics, and provide concrete diagnostics to guide model improvements.

  PublisherDOI

• Remote Sensing and Fluxes Upscaling for Real-world Impact (Workshop Report)

  2025-01-01

  reportOpen access

  The "Remote Sensing and Fluxes Upscaling for Real-world Impact" workshop, held on July 9-10, 2024, at Lawrence Berkeley National Lab, was a collaborative effort led by the AmeriFlux Management Project, NEON, and the Carbon Dew Community of Practice. The event brought together over 200 registrants and approximately 100 attendees each day, including leading experts, researchers, and practitioners. The primary focus was on bridging the gap between cutting-edge research and practical applications in environmental monitoring by integrating remote sensing and flux data. Key themes included the importance of site-level measurements for validating remote sensing products, providing nature-based climate solutions, and addressing challenges such as instrument costs and the need for standardized methods. At the regional scale, discussions centered on addressing spatial heterogeneity and using high-resolution remote sensing and machine learning methods to enhance data interpretation. Global scale challenges included data consistency, gap filling, and accurate emission source identification, with opportunities for international collaboration and standardized practices to improve global carbon budget assessments. The workshop emphasized the critical need for integrating data across local, regional, and global scales through explicit scale-matching and developed a workflow for scaling flux data using "straight shot" and "explicit nesting" approaches. The event highlighted the importance of connecting scientific research with real-world applications in carbon, energy, and water management, ensuring that advancements translate into tangible societal benefits. These insights will guide future research, technology transfer, and collaboration, maximizing the potential of environmental fluxes to address real-world challenges.

  PublisherOA PDFDOI

• Gross primary productivity research: ongoing trends and future trajectories

  Elsevier eBooks · 2025-01-01 · 1 citations

  book-chapter
  PublisherDOI

• A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest

  Agricultural and Forest Meteorology · 2025-12-28

  article
  PublisherDOI

• Near Real-Time Estimation of Daytime and Nighttime Evapotranspiration Using GOES-R Observations and Machine Learning Models

  2025-05-22 · 1 citations

  preprintOpen accessSenior author

  Evapotranspiration (ET) is a critical component of the water cycle, influencing climate, agriculture, and water resource management. However, most satellite-derived ET products are limited to daily or coarser temporal resolutions, despite the strong diurnal variability of ET processes. Existing satellite-based ET retrievals are largely restricted to daytime conditions, when nighttime ET is a small but often non-trivial flux. In this study, we introduce the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems ET (ALIVEET), a near real-time, five-minute ET estimation framework, leveraging geostationary satellite observations from the GOES-R Advanced Baseline Imager (ABI) and machine learning models under both clear and cloudy conditions. We test Gradient Boosting Regression (GBR) and Long Short-Term Memory (LSTM) models to assess their ability to estimate ET variations across the diurnal cycle. GBR captures daytime ET with an R2 of 0.74 (RMSE of 0.059 mm hh-1 equivalent to about 74 W m-2) while maintaining low computational cost. For nighttime ET, where R2 decreases by about 0.50 compared to daytime, LSTM models trained on time-series observations perform better, achieving an R² of 0.24 (RMSE of 0.014 mm hh-1) by leveraging temporal dependencies in land surface temperature (LST) and past ABI observations. Comparisons against daily ET estimates from the physically based ALEXI remote sensing model demonstrates good agreement but opportunities for improvement. This study demonstrates the potential of integrating machine learning with geostationary remote sensing to advance high-temporal-resolution ET estimation.

  PublisherOA PDFDOI

• Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine Learning

  Journal of Geophysical Research Machine Learning and Computation · 2025-04-25

  articleOpen accessSenior author

  Abstract Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near‐real‐time for both cloudy and clear sky conditions at a five‐minute resolution. We compared two machine learning (ML) models, Long Short‐Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top‐of‐atmosphere observations from the Advanced Baseline Imager (ABI) on the GOES‐16 satellite against observations from hundreds of observation sites for a five‐year period. Long Short‐Term Memory outperformed GBR, especially at coarser resolutions and under challenging conditions, with a clear sky R 2 of 0.96 (RMSE 2.31K) and a cloudy sky R 2 of 0.83 (RMSE 4.10K) across CONUS, based on 10‐repeat Leave‐One‐Out Cross‐Validation (LOOCV). GBR maintained high accuracy and ran 5.3 times faster, with only a 0.01–0.02 R 2 drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized more time information in cloudy conditions. A comparative analysis against the physically based ABI LST product showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data‐driven models for LST estimation and suggests potential pathways for integrating ML models to enhance the accuracy and coverage of LST products.

  PublisherOA PDFDOI

• Actual crop coefficients for cereal crops in Montana USA from eddy covariance observations

  Agricultural Water Management · 2025-05-22

  articleOpen accessSenior author

  Accurate quantification and derivation of crop coefficients (K c ) are essential for sustainable water management, especially in semi-arid agroecosystems facing water scarcity exacerbated by climate change. With the goal of creating a foundational local crop coefficient resource, we apply the FAO’s Penman-Monteith model to estimate evapotranspiration (ET) - evaporation from soils and non-stomatal surfaces, and transpiration from plants - and use eddy covariance and micrometeorological data to model actual K c (K c act ) for spring wheat, winter wheat, and barley in semiarid agricultural regions of Montana, USA where growth-stage based K c act has been infrequently reported. We used piecewise linear regression to calculate K c act during different stages of the growing season. K c act during the development stage ranged from 0.48 to 0.88 for flood-irrigated barley and non-irrigated wheat, peaked at most sites during the mid-stage (ranging from 0.28 to 0.69 for pivot-irrigated spring wheat), and linearly increased and decreased during the early and late phases, respectively. Variability in derived K c act was influenced by soil water content, vapor pressure deficit, and soil heat flux representing residual sensitivity to K c act arising from atmospheric and soil water limitations even in irrigated systems. We anticipate that the K c act values reported here will be useful and transferable for irrigation management in Montana and similar semi-arid climate regions. • Crop coefficients quantified for multiple Montana small grain fields. • Piecewise linear regression identified crop coefficient stages. • Actual crop coefficients (K c act ) ranged from 0.16 to 0.88 across crops and stages. • Hydrological variables impacted K c act , suggesting water stress in irrigated fields.

  PublisherDOI

• Symbolic regression for discovering vegetation indices: a case study using alfalfa biomass estimation

  2025-09-11

  preprintOpen access

  Predicting crop yield accurately is crucial for agricultural management. Traditional methods often rely on predefined vegetation indices derived from remote sensing data, which may not fully capture the complex relationships present in high dimensional hyperspectral datasets. We explored the potential of symbolic regression (SR), a machine learning technique that discovers best fit mathematical expressions from data, to develop a novel and interpretable model for predicting alfalfa biomass. Using hyperspectral reflectance data with 274 bands in the visible to near infrared, and corresponding alfalfa biomass measurements from 87 distinct sampling points, the PySR library was employed to search for optimal equations. Analyses revealed a trade-off between model complexity and accuracy. A moderately complex equation (complexity 22) involving key wavelengths in the red-edge (~779 nm) and near-infrared (~932 nm, ~952 nm, ~968 nm) regions, associated with chlorophyll content, canopy structure, and water content, achieved a coefficient of determination (r²) of approximately 0.71 on the training data. While demonstrating the capability of SR to uncover potentially meaningful relationships, the results also highlighted limitations such as systematic under-prediction at lower yields and potential feature saturation at higher yields. Selected models tended to include only three or four spectral bands, and a search for simpler expressions that follow normalized difference formulations revealed multiple options that further improved fit. SR may be useful for discovery in hyperspectral remote sensing but must be balanced against the simplicity and explainability of existing formulae.

  PublisherOA PDFDOI

• Limited Regulation of Canopy Water Use Efficiency by Stomatal Behavior Under Drought Propagation

  Global Change Biology · 2025-07-01 · 4 citations

  article

  Water use efficiency (WUE) is a critical ecosystem function and a key indicator of vegetation responses to drought, yet its temporal trajectories and underlying drivers during drought propagation remain insufficiently understood. Here, we examined the trajectories, interdependencies and drivers of multidimensional WUE metrics and their components (gross primary production (GPP), evapotranspiration, transpiration (T), and canopy conductance (Gc)) using a conceptual drought propagation framework. We found that even though the carbon assimilation efficiency per stomata increases during drought, the canopy-level WUE (represented by transpiration WUE (TWUE)) declines, indicating that stomatal regulation operates primarily at the leaf level and cannot offset the drought-induced reduction in WUE at the canopy scale. A stronger dependence on T and TWUE indicates that the water-carbon trade-off relationship of vegetation more inclines toward water transport than carbon assimilation. Gc fails to prevent the sharp decline in GPP during drought and has limited capacity to suppress T, as reflected by the reduction magnitude and the threshold (the turning point at which a component shifts from a normal to drought-responsive state). The primary drivers of the water-carbon relationship under drought propagation include vapor pressure deficit and hydraulic traits. Among plant functional types, grasslands show the strongest water-carbon fluxes in response to drought, whereas evergreen broadleaf forests exhibit the weakest response. These findings refine our comprehensive understanding of multidimensional ecosystem functional dynamics under drought propagation and enlighten how the physiological response of vegetation to drought affects the carbon and water cycles.

  PublisherDOI

Recent grants

• MSA: Rapid assessment of the carbon cycle consequences of ecosystem disturbances

  NSF · $300k · 2021–2024

• Scaling ecosystem function: Novel approaches from MaxEnt and Multiresolution

  NSF · $136k · 2011–2013

• CAREER: The role of ecosystem management on boundary layer development and precipitation in the Northern Plains

  NSF · $92k · 2020–2021

• Collaborative Research: Building Forest Management into Earth System Modeling: Scaling from Stand to Continent

  NSF · $115k · 2013–2017

• CAREER: The role of ecosystem management on boundary layer development and precipitation in the Northern Plains

  NSF · $498k · 2016–2020

Frequent coauthors

• Gabriel G. Katul

  Duke University

  88 shared

• Tobias Gerken

  74 shared

• Mario Siqueira

  Universidade de Brasília

  62 shared

• Amy M. Trowbridge

  University of Wisconsin–Madison

  53 shared

• Ankur R. Desai

  University of Wisconsin–Madison

  51 shared

• Zheng Fu

  Commissariat à l'Énergie Atomique et aux Énergies Alternatives

  50 shared

• Jehn‐Yih Juang

  National Taiwan University

  48 shared

• Kimberly A. Novick

  Indiana University Bloomington

  46 shared

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1982

• M.S., Computer Science

  University of California, Berkeley

  1978

• B.S., Computer Science

  University of California, Berkeley

  1975

Awards & honors

• Highly Cited Researcher (2018)
• Outstanding Editor Award, Biogeosciences
• Humboldt Research Fellowship, Alexander von Humboldt Foundat…
• Marie Curie Fellowship, European Union/European Commission