B071
Radiative Transfer over Land Surfaces: Observations, Modeling, Inversion, and Applications
Session ID#: 249781
Session Description:
Radiative transfer (RT) over land surfaces represents the intricate interplay between electromagnetic waves and Earth surface’s elements. It plays a pivotal role in physically interpreting remote sensing signals, quantitatively retrieving information on surface composition, structure and functioning, and determining surface energy balance and absorbed radiative energy for photosynthesis, snowmelt, etc. This session aims to bring together those interested in developing, improving, verifying and applying soil-snow-vegetation RT models from ultraviolet, optical (including multi-spectral, hyperspectral, LiDAR, and SIF), thermal, and microwave spectra across scales. Contributions are encouraged within broad themes: 1) field measurements and remote sensing observations; 2) forward, mechanistic modeling and understanding of RT processes; 3) quantitative retrievals of surface parameters; 4) utilization of ML/DL for RT emulation, acceleration and inversion; 5) integration and representation of RT processes in land-surface-models and Earth-system-models; 6) integrative use of diverse data and models for ecohydrological applications and Earth system sciences from the RT perspective.
Index Terms:
0439 Ecosystems, structure and dynamics [BIOGEOSCIENCES]
0466 Modeling [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
3359 Radiative processes [ATMOSPHERIC PROCESSES]
Primary Convener: Dalei Hao, Pacific Northwest National Laboratory, Atmospheric, Climate, & Earth Sciences Division, Richland, United States
Conveners: Yelu Zeng, CAU China Agricultural University, College of Land Science and Technology, Beijing, China, Min Chen, UW-Madison, Madison, United States and Tiangang Yin, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
Student/Early Career Convener: Haoran Liu, University of Wisconsin Madison, Forest and Wildlife Ecology, Madison, WI, United States
Improving Simplified Models of Radiation Interception and Evapotranspiration Commonly Used in Remote Sensing of Discontinuous Canopies (1880538)
María Alejandra Alejandra Ponce de León1, Matthew Roby2, Kyle Knipper2, Nicolas E Bambach3, William P Kustas4, Andrew J McElrone5 and Brian Bailey6, (1)University of California Davis, Department of Plant Sciences, Davis, CA, United States, (2)U. S. Department of Agriculture, Agricultural Research Service, Sustainable Agricultural Water Systems Research Unit, Davis, United States, (3)University of California Davis, Department of Viticulture and Enology, Davis, United States, (4)U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, United States, (5)United States Department of Agriculture, Agricultural Research Service, Crops Pathology and Genetics Research Unit, Davis, United States, (6)University of California Davis, Department of Plant Sciences, Davis, United States
A Clumped-Foliage Canopy Radiative Transfer Model for a Global Dynamic Terrestrial Ecosystem Model III: Diurnal and Seasonal Albedo (1893988)
Leila Mirzagholi1, Nancy Y Kiang1, Wenge Ni-Meister2, Wenze Yang2 and Andrew J Oliphant3, (1)NASA Goddard Institute for Space Studies, New York, United States, (2)CUNY Hunter College, New York, United States, (3)San Francisco State University, School of the Environment, San Francisco, CA, United States
Separating soil background signals from canopy reflectance using spectral invariant theory-based model for improved solar-induced chlorophyll fluorescence estimates (1907683)
Dongjin Ahn, Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, Korea, Republic of (South), Youngryel Ryu, Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, South Korea and Jeongho Lee, Seoul National University, Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul, South Korea
Synergy between Geostationary and Hyperspectral Remote Sensing: A Case Study with TEMPO Data (1927966)
Weile Wang, NASA/AMES Research Center, Moffett Field, United States, Hirofumi Hashimoto, NASA Ames Research Center, Moffett Field, CA, United States, Taejin Park, NASA Ames Research Center/Bay Area Environmental Research Institute, Moffett Field, United States, Yujie Wang, University of Maryland Baltimore County, Goddard Earth Sciences Technology and Research II, Baltimore, United States and Ian G Brosnan, NASA Ames Research Center, Earth Science Division, Moffett Field, CA, United States
Synergetic retrieval of leaf area index and leaf chlorophyll content (1932799)
Jing Ming Chen1,2, Haoqiang Zhou2, Zixuan Zeng3, Mingzhu Xu2, Xingchang Wang4, Rong Shang2, Rong Wang2, Zhiqiang Cheng2, Yulin Yan2, Weimin Ju5 and HaoqingZhou, (1)University of Toronto, Department of Geography and Planning, Toronto, Canada, (2)Fujian Normal University, School of Geographical Sciences, Fuzhou, China, (3)Nanjing University, International Institute of Earth System Science, Nanjing, China, (4)Northeast University of Forestry of China, Harbin, China, (5)Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing, China
Reconstructing high-resolution satellite solar-induced chlorophyll fluorescence based on an end-to-end convolutional neural network with spatial redistribution (1951286)
Jiaochan Hu, Organization Not Listed, Washington, DC, United States; Dalian Maritime University, College of Environmental Science and Engineering, Dalian, China and Zihan Ma, Dalian Maritime University, Dalian, China
Unrevealing site-dependent relationship between Solar-induced chlorophyll Fluorescence and Gross Primary Productivity using the Terrestrial Ecosystem Carbon cycle simulator (1959373)
Haoran Liu1, Zoe Pierrat2, Hamid Dashti3 and Min Chen1, (1)University of Wisconsin Madison, Forest and Wildlife Ecology, Madison, WI, United States, (2)JPL/NASA/Caltech, Pasadena, United States, (3)University of Wisconsin-Madison, Department of Forest and Wildlife Ecology, Madison, United States
Global Retrieval of Harmonized Microwave Land Surface Emissivity Leveraging Multi-Sensor Measurements from GMI, AMSR2 and MWRIs (1961456)
Jiheng Hu1, Rui Li1, Peng Zhang2, Yu Wang1, Shengli Wu3, Letu Husi4 and Fuzhong Weng5, (1)University of Science and Technology of China, Hefei, China, (2)China Meteorological Administration, Meteorological Observation Center, Beijing, China, (3)China Meteorological Administration, National Satellite Meteorological Center, Beijing, China, (4)State Key Laboratory of Remote Sensing Science, The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China, (5)Chinese Academy of Meteorological Sciences, Earth System Modeling and Prediction Centre, Beijing, China
Integrating Multi-Angle SIF and Multi-Source Vegetation Indices in a Physically-Guided Deep Learning Framework for Soybean Yield Estimation (1986085)
Yongyuan Gao1, Ke Li2, Dalei Hao3, Yachang He2 and Yelu Zeng2, (1)CAU China Agricultural University, College of Land Science and Technology, Beijing, China, (2)China Agricultural University, Beijing, China, (3)Pacific Northwest National Laboratory, Atmospheric, Climate, & Earth Sciences Division, Richland, United States
Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy (1986386)
Yelu Zeng1, Yachang He1, Dalei Hao2 and Yifan Lu3, (1)China Agricultural University, Beijing, China, (2)Pacific Northwest National Laboratory, Atmospheric, Climate, & Earth Sciences Division, Richland, United States, (3)Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Unraveling Intracanopy Reflectance Heterogeneity with a Gaussian Mixture Model to Improve Leaf Chlorophyll Retrieval from UAV Hyperspectral Imagery (1995376)
Mr. Yangming Feng1, Rongpeng Zhang1, Zhixiong Li1, Yongyuan Gao2 and Shaozhong Kang1, (1)CAU China Agricultural University, Center for Agricultural Water Research in China, Beijing, China, (2)CAU China Agricultural University, College of Land Science and Technology, Beijing, China
Integrating Prior Knowledge with Deep Learning for Enhanced Wheat Yield Estimation Using Remote Sensing and Meteorological Data (1998021)
Ke Li1, Zhihong Li2, Yongyuan Gao3, Huiren Tian4 and Yelu Zeng3, (1)CAU China Agricultural University, Beijing, China, (2)China Agriculture Universuty, Beijing, China, (3)CAU China Agricultural University, College of Land Science and Technology, Beijing, China, (4)Xi'an University of Science and Technology, Xian, China
Estimating Hourly 2-km All-Sky Surface Downward Longwave Radiation from GOES-R Imagery Using Machine Learning (2000035)
Yamin Guo, University of Maryland, Department of Atmospheric and Oceanic Science, College Park, United States, Peng Yu, CISESS/ESSIC, University of Maryland, College Park, United States and Jing Wei, Earth System Science Interdisciplinary Center, University of Maryland, Department of Atmospheric and Oceanic Science, College Park, United States
