B004
Advances in Remotely Sensed Image Fusion: Methods and Applications

Session ID#: 83727

Session Description:
Several hundred Earth observing satellites are currently in orbit. Sensors aboard these orbiters collect imagery and measurements across a variety of spectral wavelengths, and at many different spatial and temporal resolutions. These datasets are often highly complementary, but the vast majority of applications utilize a single sensor's record. Multisensor fusion approaches are capable of generating synthetic observations that exploit complementary aspects of two or more sensors, thereby overcoming single-sensor limitations. This session highlights progress in remotely sensed data fusion. We invite submissions documenting advances in image fusion algorithms, as well as applications.
Index Terms:

0439 Ecosystems, structure and dynamics [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1632 Land cover change [GLOBAL CHANGE]
1910 Data assimilation, integration and fusion [INFORMATICS]

Primary Convener:  Josh Gray, North Carolina State University Raleigh, Raleigh, NC, United States
Convener:  Feng Gao, USDA-Agricultural Research Service Beltsville, Hydrology and Remote Sensing, Beltsville, MD, United States
Primary Liaison:  Josh Gray, North Carolina State University Raleigh, Raleigh, NC, United States
 
Harmonization and inter-calibration of data streams from UAVs, CubeSats, Sentinel-2, and Landsat 8 (Invited) (497522)
Rasmus Houborg, Planet Labs, San Francisco, CA, United States, Matthew McCabe, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia and Ignacio Zuleta, Planet Labs, San Francisco, United States
 
An object-based strategy for improving the accuracy of spatiotemporal satellite imagery fusion for vegetation mapping applications (498642)
Hongcan Guan1, Yanjun Su1, Tianyu Hu1, Jin Chen2 and Qinghua Guo1, (1)Institute of Botany, Chinese Academy of Sciences, Beijing, China, (2)Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing, China
 
Scaling up lidar-derived forest structure with data fusion in a megadiverse country (503106)
Juan Miguel Requena-Mullor, Boise State University, Department of Biological Sciences, Boise, United States and Timothy Trevor Caughlin, Boise State University, Department of Biological Science, Boise, United States
 
A simple efficient non-parametric regression based strategy to fuse spatial-temporal information, for gap filling, cloud/cloud shadow recovery, and relative radiometric normalization (513773)
Wenxia Gan, Wuhan Institute of Technology, Wuhan, China, Jing Geng, Beijing Institute of Technology, Beijing, China and Xiaobin Guan, Wuhan University, Wuhan, China
 
Monitoring canopy photosynthesis in high spatial and temporal resolution using CubeSat imagery (514573)
Juwon Kong1, Youngryel Ryu2,3, Rasmus Houborg4 and Minseok Kang1,5, (1)Seoul National University, Seoul, South Korea, (2)Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, South Korea, (3)Seoul National University, Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul, South Korea, (4)Planet Labs, San Francisco, United States, (5)National Center for AgroMeteorology, Seoul, South Korea
 
An Evaluation of the Archive Continuation of MODIS with VIIRS and their Consistency with Landsat for Data Fusion Techniques. (520918)
Ethan James Heck, University of Oklahoma Norman Campus, Geography and Environmental Sustainability, Norman, OK, United States, Kirsten de Beurs, University of Oklahoma Norman Campus, Norman, OK, United States and Braden Owsley, University of Oklahoma, Geography and Environmental Sustainability, Norman, OK, United States
 
Fusing Multi-sensor Data for Crop Monitoring in Regions with Complex Cropping System (540721)
Jun Lu and Tao He, Wuhan University, Wuhan, China
 
Harmonized Landsat / Sentinel-2 (HLS) Data for Land Science (542341)
Junchang Ju1, Jeffrey G Masek2, Zhangshi Yin3, Sergii Skakun4, Jean-Claude Roger1, Eric Vermote5, Jennifer L Dungan6, Christopher Owen Justice7 and Martin Claverie8, (1)University of Maryland College Park, College Park, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)Science Systems and Applications, Inc., Lanham, MD, United States, (4)University of Maryland College Park, College Park, United States, (5)NASA Goddard Space Flight Center, Terrestrial Information Systems Laboratory, Greenbelt, MD, United States, (6)NASA Ames Research Center, Moffett Field, CA, United States, (7)University of Maryland, Geographical Sciences, College Park, MD, United States, (8)Univ. Catholique de Louvain, Boursier, Belgium
 
Imputation of HLS image time series for low-latency agricultural applications (546289)
Eli K Melaas1, Elisabeth Baldo1 and Bobby H Braswell Jr2, (1)Indigo Ag, Boston, MA, United States, (2)Indigo Ag, GeoInnovation, Boston, MA, United States
 
Satellite Data Fusion of Multiple Observed XCO2 using Compressive Sensing and Deep Learning (556268)
Milton Halem1, Sai Sree Laya Chukkapalli2, Samit Shivadekar1 and Phuong Nguyen3, (1)University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States, (2)University Of Maryland Baltimore County, Computer Science and Electrical Engineering, Baltimore, MD, United States, (3)University Of Maryland Baltimore County, Halethorpe, MD, United States
 
New opportunities for developing high spatial resolution land products derived from gap free Landsat reflectance time series in Google Earth Engine (575064)
Alvaro Moreno1, Emma Izquierdo2, Marco P Maneta3, Gustau Camps-Valls4, Nathaniel Robinson5, Jordi Muñoz6, Nicholas Clinton7, Fernando Sedano8 and Steven W Running1, (1)University of Montana, Numerical Terradynamic Simulation Group (NTSG), Missoula, MT, United States, (2)BOKU University of Natural Resources and Life Sciences, Institute of Surveying, Remote Sensing and Land Information, Vienna, Austria, (3)University of Montana, Geosciences, Missoula, MT, United States, (4)Universitat de València, Image Processing Laboratory (IPL), Burjassot, Spain, (5)Panthera, New York, MT, United States, (6)University of Valencia, Image Processing Laboratory (IPL), Burjassot, Spain, (7)Google Inc., Mountain View, CA, United States, (8)University of Maryland, Washington, DC, United States
 
Multi-Source Remote Sensing Data Fusion for Detecting Changes in Shrub Cover in Semi-Arid Regions (583806)
Rubaya Pervin, Scott M Robeson, Samapriya Roy and Natasha Macbean, Indiana University Bloomington, Bloomington, IN, United States
 
Fusion of Sentinel-2 and Sentinel-3 Imagery via Deep Learning (592000)
Rohit Mukherjee, Ohio State University Main Campus, Columbus, OH, United States and Desheng Liu, The Ohio State University, Department of Geography, Columbus, OH, United States
 
Monitoring tree height, loss, and gain in South America using Lidar and Landsat data for 1985-2018. (615029)
Andres Hernandez-Serna1, Matthew Hansen2, Peter Potapov2 and Viviana Zalles2, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland, College Park, MD, United States
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