B086
Surface-Atmosphere Interactions: Scaling Fluxes with Remote Sensing
Session ID#: 251074
Session Description:
As flux tower and remote sensing (RS) communities converge through shared goals in resource management and environmental monitoring, the need for scale-aware data integration has never been more urgent. This session builds on recent outcomes from the AmeriFlux–NEON–CarbonDew workshop series “Remote Sensing and Fluxes Upscaling for Real-world Impact” and will focus on advancing methods and applications that link eddy covariance fluxes with RS observations across spatial and temporal scales. We welcome contributions including, but not limited to: (i) Flux attribution and upscaling using RS; (ii) Ground-truthing and cross-validation across RS, flux towers, and landscapes; (iii) Advances in process modelling, machine learning and AI (or their combination) for integrating flux and RS data; (iv) Novel RS techniques (e.g., hyperspectral, thermal, lidar) in flux science; (v) Applications in agriculture, forestry, urban systems, and land management, and; (vi) Benchmark datasets, shared tools, and collaborative platforms.
Co-Sponsor(s):
Index Terms:
0315 Biosphere/atmosphere interactions [ATMOSPHERIC COMPOSITION AND STRUCTURE]
0414 Biogeochemical cycles, processes, and modeling [BIOGEOSCIENCES]
0426 Biosphere/atmosphere interactions [BIOGEOSCIENCES]
0428 Carbon cycling [BIOGEOSCIENCES]
Primary Convener: Stefan Metzger, University of Wisconsin Madison, Dept of Atmospheric and Oceanic Sciences, Madison, United States
Conveners: Leila Constanza Hernandez Rodriguez, Lawrence Berkeley National Laboratory, Berkeley, United States, Nicola Falco, Lawrence Berkeley National Laboratory, Berkeley, CA, United States and David Durden, National Ecological Observatory Network, Battelle, Boulder, United States
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Isaya Kisekka, University of California Davis, Land Air and Water Resource/ Biological and Agricultural Engineering, Davis, United States, Ali Ghorbanpour, University of California Davis, LAWR, Davis, CA, United States and Srinivasa Rao Peddinti, University of California Davis, Davis, CA, United States
Functional Diversity from Imaging Spectroscopy Enhances Within-Season GPP Stability Across U.S. Ecosystems (1936233)
Ruqi Yang1, Ting Zheng1, Shuwen Liu1, Kyle R Kovach1, Ankur R Desai2 and Philip A Townsend1, (1)University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, United States, (2)University of Wisconsin Madison, Atmospheric and Oceanic Sciences, Madison, United States
Estimating Cropland Carbon Fluxes and the Role of Agricultural Conservation Practices Using Remote Sensing and Machine Learning (1946772)
Xian Wang1, Haoran Liu2, Siddhanth Hegde3, Kimberly Novick4, Mallory Barnes3, Arthur Endsley5 and Kesondra Key4, (1)Indiana University Bloomington, Bloomington, IN, United States, (2)University of Wisconsin Madison, Forest and Wildlife Ecology, Madison, WI, United States, (3)Indiana University Bloomington, Bloomington, United States, (4)Indiana University, Paul H. O’Neill School of Public and Environmental Affairs, Bloomington, United States, (5)University of Montana, Numerical Terradynamic Simulation Group, Missoula, United States
Variations in canopy physical and functional traits drive the variations of the gross primary productivity in a northern temperate forest (1959773)
Ting Zheng1, Zhiwei Ye2, Jacob A May3, Brian Butterworth4, Bethany Blakely5, Christian G Andresen3, Ankur R Desai5 and Philip A Townsend6, (1)University of Wisconsin Madison, Madison, WI, United States, (2)University of Wisconsin-Madison, Department of Forest and Wildlife Ecology, Madison, United States, (3)University of Wisconsin Madison, Geography, Madison, WI, United States, (4)Cooperative Institute for Research in Environmental Sciences, CU Boulder, Boulder, United States, (5)University of Wisconsin Madison, Atmospheric and Oceanic Sciences, Madison, United States, (6)University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, United States
A machine learning approach to improve the spatiotemporal resolution of actual evapotranspiration estimates by upscaling eddy covariance tower measurements to the field scale (1974277)
Ali Dadkhah1, Donna M Rizzo2, Paul R Adler3, Arman Ahmadi4, Noah B. Beckage5, Patrick J Clemins6, Luis A Garcia1, Matthew W Myers3, George F Pinder1, Shaurya Swami7 and Asim Zia8, (1)University of Vermont, Department of Civil and Environmental Engineering, Burlington, VT, United States, (2)University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, (3)USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, United States, (4)University of California, Davis, Biological and Agricultural Engineering, Davis, United States, (5)University of Vermont, Burlington, United States, (6)University of Vermont, Department of Computer Science, Burlington, VT, United States, (7)University of Vermont, Department of Computer Science, Burlington, United States, (8)University of Vermont, Department of Community Development and Applied Economics, Department of Computer Science, Burlington, VT, United States
Enhancing Forest Flux Predictions with Foliar Traits (1988535)
Bethany Blakely1, Ting Zheng2, Kyle R Kovach2, Sadegh Ranjbar, PhD Candidate3, Paul C Stoy4, Philip A Townsend2 and Ankur R Desai1, (1)University of Wisconsin Madison, Atmospheric and Oceanic Sciences, Madison, United States, (2)University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, United States, (3)University of Wisconsin Madison, Biological Systems Engineering, Madison, WI, United States, (4)University of Wisconsin-Madison, Madison, Biological Systems Engineering, Madison, United States
