Research Projects

Research Summary: This study aims to improve crop water stress monitoring by developing a scaled variance method within a dual-source energy balance framework. This approach uses a moving kernel to determine relative variance in crop water stress indices derived from soil and canopy heat fluxes. By refining how water stress levels are mapped, the project seeks to provide updated tools for supporting sustainable agriculture and resource conservation efforts.

Masters Student: Jackson Ezzell

Project Duration: Nov/2024 to Mar/2025

Research Summary: This project develops a novel framework to bridge coarse-resolution ECOSTRESS water use efficiency (WUE) data (70 m) with high-resolution satellite imagery from Sentinel-2 (10 m) and PlanetScope (3 m). The approach uses statistical regression models and residual redistribution techniques to downscale ECOSTRESS WUE, enabling finer-scale mapping of crop and ecosystem water use efficiency.

By validating the framework with AmeriFlux tower observations and comparing against existing downscaling methods, the research advances our ability to monitor plant productivity and water use under spatio-temporal stress variability. The outcome will be a robust, open-source framework for accurately quantifying WUE at field to watershed scales, supporting improved water resources management and agricultural decision-making.

Masters Student: Samantha Peppel

Project Duration: Aug/2025 to May/2027

Research Summary: This project develops a hybrid remote sensing and micrometeorological approach to improve how surface resistance (rs) is represented in the Penman–Monteith evapotranspiration (ET) model . The research links flux tower observations with Sentinel-2 vegetation indices that capture canopy structure, greenness, and water content, enabling the derivation of key coefficients (a and b) from the Katerji–Perrier formulation.

By mapping these coefficients across space, the project advances the ability to generate spatially explicit estimates of rs and ET without relying solely on point-based calibration. The work includes rigorous validation across multiple AmeriFlux sites, uncertainty quantification, and comparisons with traditional Jarvis-type models. The outcome will be transferable methodologies and open workflows for improving ET mapping, with implications for irrigation management, drought monitoring, and ecohydrological modeling.