B008
Advances in upscaling networked observations to regions and the globe using machine learning and modeling

Session ID#: 160427

Session Description:
A wide variety of in-situ ecological measurements across regions and the globe are now available from various networks of observation. For example, networks such as AmeriFlux, NEON, and FLUXNET provide continuous measurements of carbon, water, and energy exchange and other ecological proccesses at numerous sites across regions, continents, or the globe. Spectral networks link optical measurements (e.g., vegetation indices, solar-induced fluorescence) with flux sampling for diverse ecosystems. Forest inventory networks (e.g., FIA, ENFIN) offer data on forest ecosystem structure and functions for forest plots across nations. PhenoCam uses digital cameras to track vegetation phenology around the world. Research networks such as Drought-Net, Nutrient Network, and PrecipNet coordinate field manipulation experiments in drought/precipitation manipulation and nitrogen addition. Findings achieved at these sites or plots do not necessarily apply at other locations across the globe, and thereby it is essential to upscale these networked observations to broad spatial scales.

 

We invite submissions that upscale networked observations to regions or the globe using machine learning, terrestrial biosphere/Earth system modeling, hybrid methods, and/or model-data fusion techniques. Of particular interest are also studies that shed new light on land-atmosphere interactions, carbon, water, or energy cycling, ecosystem functions and structure, impacts of climate change and disturbances, climate adaptation and mitigation, and management at regional, continental, or global scales. Synergistic studies based on observations from different networks are also welcomed.

Co-Organized:
  • GC - Global Environmental Change
Index Terms:

0426 Biosphere/atmosphere interactions [BIOGEOSCIENCES]
0466 Modeling [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1630 Impacts of global change [GLOBAL CHANGE]

Primary Convener:  Jingfeng Xiao, University of New Hampshire Main Campus, Durham, NH, United States
Conveners:  Stefan Metzger, NEON, Battelle, Boulder, CO, United States and Yiqi Luo, Northern Arizona University, Center for Ecosystem Sciences and Society (ECOSS), Flagstaff, AZ, United States
 
Integrating networked observations and remote sensing into a CONUS-scale carbon cycle reanalysis and forecasting system (Invited) (1045993)
Michael Dietze1, Dongchen Zhang2, Qianyu Li3, Alexis Helgeson2, Joshua Bowers4, Courtney Leung5 and Shawn Serbin3, (1)Boston University, Earth and Environment, Boston, United States, (2)Boston University, Boston, MA, United States, (3)Brookhaven National Laboratory, Environmental and Climate Sciences Department, Upton, NY, United States, (4)University of Wisconsin - Oshkosh, Oshkosh, United States, (5)University of Chicago, Chicago, United States
 
Opportunities and challenges in evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence (1064030)
Rui Cheng1, Troy Magney2, Erica Orcutt2, Zoe Pierrat3, Philipp Koehler4, David R Bowling5, Marion Syndonia Bret-Harte6, Eugenie Susanne Euskirchen6, Martin Jung7, Hideki Kobayashi8, Adrian V Rocha9, Oliver Sonnentag10, Sophia Walther7, Donatella Zona11 and Christian Frankenberg12, (1)California Institute of Technology, Pasadena, United States, (2)University of California Davis, Department of Plant Sciences, Davis, CA, United States, (3)University of California Los Angeles, Los Angeles, CA, United States, (4)EUMETSAT, Darmstadt, Germany, (5)University of Utah, School of Biological Sciences, Salt Lake City, UT, United States, (6)University of Alaska Fairbanks, Fairbanks, United States, (7)Max Planck Institute for Biogeochemistry, Jena, Germany, (8)JAMSTEC Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan, (9)Univ of Notre Dame, Notre Dame, IN, United States, (10)Université de Montréal, Département de géographie, Montréal, QC, Canada, (11)San Diego State University, San Diego, CA, United States, (12)NASA Jet Propulsion Laboratory, Pasadena, United States
 
Upscaling soil organic carbon measurements at the continental scale by multivariate clustering analysis and machine learning (1066343)
Zhuonan Wang, Emory University, Department of Environmental Sciences, Atlanta, GA, United States, Debjani Sihi, Emory University, Atlanta, United States, Jitendra Kumar, Oak Ridge National Laboratory, Oak Ridge, United States, Samantha R Weintraub-Leff, University of Colorado at Boulder, Boulder, United States, Kathe Todd-BROWN, University of Florida, Ft Walton Beach, United States and Umakant Mishra, Sandia National Laboratories, Computational Biology and Biophysics, Albuquerque, NM, United States
 
Spatial patterns and driving factors of aboveground and belowground biomass over the eastern Eurasian steppe (1076803)
Lei Ding1, Zhenwang Li2, Beibei Shen3, Xu Wang3, Dawei Xu3, Ruirui Yan3, Yuchun Yan3, Xiaoping Xin3, Jingfeng Xiao4, Ming Li5 and Ping Wang5, (1)Zhejiang University, Hangzhou, China, (2)Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China, (3)CAAS Chinese Academy of Agricultural Sciences, Beijing, China, (4)University of New Hampshire, Durham, United States, (5)Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing, China
 
Machine learning enabled quantification of global soil inorganic carbon storage. (Invited) (1082837)
Yuanyuan Huang, CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia and Xiaodong Song, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China
 
KoSpec: a hyperspectral remote sensing network for monitoring canopy structure and functions across various ecosystems in Korea (1100623)
Sangjun Lee, Seoul National University, Research Institute of Agriculture and Life Sciences, Seoul, Korea, Republic of (South), Jeongho Lee, Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University, Seoul, South Korea, Jongmin Kim, Seoul National University, Interdisciplinary Program in Landscape Architecture, Seoul, South Korea, Minshung Kim, Seoul National University, Research Institute of Agriculture and Life Sciences, Gwanak-gu, South Korea and Youngryel Ryu, Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, South Korea
 
Quantifying the global representativeness of foliar trait observations: current state and future potential (1115203)
Benjamin Dechant1, Ryan Pavlick2, Jens Kattge3, Fabian D Schneider4, Ethan E Butler5, Alvaro Moreno6, Pedro J. Leitão7, Peter van Bodegom8, Helena Vallicrosa9, Teja Kattenborn7, Ning Dong10, Holly Croft11, Francesco Sabatini12, Coline Boonman13, Hannes Feilhauer7, Peter B. Reich14, Iain Coli Prentice10, Ian Wright15, Sandra Milena Duran16, Josep Penuelas17, Markus Reichstein18, Karin Rebel19, Jordi Sardans20, Jesús Aguirre-Gutiérrez21, Jeannine Cavender-Bares22 and Philip A Townsend23, (1)German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany, (2)Jet Propulsion Laboratory, Pasadena, United States, (3)Max Planck Institute for Biogeochemistry, Jena, Germany, (4)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (5)Harvard University, Cambridge, MA, United States, (6)Image Processing Laboratory, Universitat de València, Paterna, Spain, (7)University of Leipzig, Leipzig, Germany, (8)Leiden University, Institute of Environmental Sciences (CML), Leiden, Netherlands, (9)Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, United States, (10)Imperial College London, London, United Kingdom, (11)University of Toronto, Toronto, ON, Canada, (12)University of Bologna, Bologna, Italy, (13)Aarhus University, Aarhus C, Denmark, (14)University of Minnesota Twin Cities, Forest Resources, Saint Paul, MN, United States, (15)University of Western Sydney, Penrith, Australia, (16)University of Minnesota Twin Cities, Minneapolis, United States, (17)CREAF, Cerdanyola Del Valle, Spain, (18)Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Jena, Germany, (19)University Utrecht, Utrecht, Netherlands, (20)CREAF, Cerdanyola del Valles, Spain, (21)University of Oxford, ECI, Oxford, United Kingdom, (22)University of Minnesota, Dept. of Ecology, Evolution and Behavior, Saint Paul, MN, United States, (23)University of Wisconsin Madison, Forest and Wildlife Ecology, Madison, United States
 
Statewide Mapping of California’s Forest Stands using Synthetic Tree Data (1130935)
Taylor Brown, Ashley Cale, Nikki Inglis, Adriano Matos, Johanson Onyegbula, Gunner Stone, Erin J Hanan and Jonathan A Greenberg, University of Nevada Reno, Natural Resources and Environmental Science, Reno, United States
 
Network-scale Data Integration to Improve Earth-System Modeling (1145721)
David Durden1, Danica L. Lombardozzi2, William R Wieder3, Negin Sobhani4, Dawn Lenz1, Jim Edwards2, Ed Ayres5, Samantha R Weintraub-Leff6, Brian Dobbins2, Michael SanClements7 and Gordon B Bonan2, (1)National Ecological Observatory Network, Boulder, CO, United States, (2)National Center for Atmospheric Research, Boulder, CO, United States, (3)Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, United States, (4)NCAR | University of Iowa, Iowa City, IA, United States, (5)Battelle, National Ecological Observatory Network, Boulder, CO, United States, (6)University of Colorado at Boulder, Boulder, United States, (7)National Ecological Observatory Network (NEON), Battelle, Boulder, United States
 
Upscaling Forest Type Maps Using Machine Learning with a Combination of FIA Field Plot, G-LiHT, and Sentinel-II data to Produce a Wall-to-Wall Map in the Tanana Unit of Interior Alaska. (1148335)
Pratima K C, University of Washington Seattle Campus, School of Environmental and Forest Sciences, Seattle, WA, United States, Hans-Erik Andersen, USDA Forest Service Pacific Northwest Research Station, Seattle, WA, United States, Bruce Cook, NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, United States and Van R Kane, University of Washington Seattle Campus, School of Environmental and Forest Sciences, Seattle, United States
 
Traversing and Synthesizing Fragmented Ecological Observations across Global Networks and Databases (1158776)
Jitendra Kumar, Oak Ridge National Laboratory, Climate Change Science Institute, Environmental Science Division, Oak Ridge, TN, United States, Forrest M. Hoffman, Oak Ridge National Laboratory, Computational Sciences & Engineering Division and Climate Change Science Institute, Oak Ridge, TN, United States and William Walter Hargrove, USDA Forest Service, Vallejo, CA, United States
 
Leveraging the Power of the Process-based Models, Machine Learning and Multisource Data: a Knowledge-Guided Data Assimilation Framework for Improving In-season Agroecosystem Prediction (1159819)
Qi Yang1, Licheng LIU1, Junxiong Zhou2, Kaiyu Guan3 and Zhenong Jin2, (1)University of Minnesota-Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, (2)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, (3)University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States
 
Using Auto Machine Learning to Improve Gross Primary Production Upscaling (1169195)
Max Gaber1,2, Yanghui Kang1, Guy Schurgers3 and Trevor F Keenan1,4, (1)University of California, Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, United States, (2)University of Copenhagen, Department of Geosciences and Natural Resource Management, Copenhagen, Denmark, (3)University of Copenhagen, Department of Geosciences and Natural Resource Management, København K, Denmark, (4)Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, United States
 
Using Remote Sensing and GeoAI (Machine learning/Deep Learning) to Model Regional Agricultural Field Characteristics in the Southeastern Plains, USA (1176203)
Alisa Coffin1, Marguerite Madden2, Joseph Powell3, Andrea Albright4, Lynne Seymour3, Austin Stone5 and Timothy C Strickland6, (1)USDA-Agricultural Research Service, Tifton, GA, United States, (2)University of Georgia, Geography, Athens, GA, United States, (3)University of Georgia, Department of Statistics, Athens, United States, (4)University of Houston, Houston, United States, (5)NASA DEVELOP Program - SSAI, National Program Office, Hampton, VA, United States, (6)USDA-ARS, Southeast Watershed Research Unit, Tifton, GA, United States
 
Comparative Analysis of Machine Learning Algorithms for Habitat Suitability Modeling of Monsoon Migratory bird in Spatio-Temporal Domain (1176504)
Vishal Kumar, Indian Institute of Remote Sensing, Dehradun, India, Sameer Saran, Indian Institute of Remote Sensing, Geoinformatics Department, Dehradun, India and Jeganathan Chockalingam, Birla Institute of Technology, REMOTE SENSING, Mesra, India
 
Inferring Surface Energy Fluxes Using Drone Data Assimilation in Large Eddy Simulations (1176890)
Norbert Pirk1, Kristoffer Aalstad2, Sebastian Westermann3, Astrid Vatne4, Alouette van Hove4, Lena Merete Tallaksen5, Massimo Cassiani6 and Gabriel George Katul7, (1)Department of Geosciences, University of Oslo, Oslo, Oslo, NORWAY, (2)University of Oslo, Geosciences, Oslo, Norway, (3)University of Oslo (UiO), Department of Geosciences, Oslo, Norway, (4)University of Oslo, Department of Geosciences, Oslo, Norway, (5)University of Oslo, Oslo, Norway, (6)NILU, Oslo, Norway, (7)Nicholas School of the Environment, Duke University, Durham, NC, United States
 
Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge (1189298)
Beibei Shen1, Lei Ding2, Leichao Ma3, Zhenwang Li4, Alim Pulatov5, Zheenbek Kulenbekov6, Jiquan Chen7, Saltanat Mambetova8, Lulu Hou9, Dawei Xu1, Xu Wang1 and Xiaoping Xin1, (1)CAAS Chinese Academy of Agricultural Sciences, Beijing, China, (2)Zhejiang University, Hangzhou, China, (3)China Geological Survey, Beijing, China, (4)Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China, (5)EcoGIS center, National Research University “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” (TIIAME), Tashkent, Uzbekistan, (6)American University of Central Asia, Department of Applied Geology, Bishkek, Kyrgyzstan, (7)Michigan State University, Geography, Environment and Spatial Sciences, East Lansing, United States, (8)Earth and Environmental Sciences Department, University of Central Asia, Bishkek, Kyrgyzstan, (9)Chinese Academy of Agricultural Sciences, Beijing, China
 
Improve the performance of the Noah-MP model by hybridizing sequential data assimilation and machine learning (1192413)
Xinlei He, Beijing Normal University, Beijing, China, Shaomin Liu, Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing, China, Tongren Xu, Faculty of Geographical Science, Beijing Normal University, Beijing, China and Sayed M. Bateni, University of Hawaii at Manoa, Department of Civil and Environmental Engineering and Water Resources Research Center, Honolulu, United States
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