B045
Science and Applications Enabled by Remote Sensing Data Fusion and Advanced Analytics

Session ID#: 162133

Session Description:
Remote sensing data from various platforms at different spatial-temporal resolutions and spectral bands brings both challenges and opportunities for high spatial resolution and near-real-time intelligence. Recent developments in data fusion techniques with advances in analytical methods in time series analysis, object detection classification and more, has empowered numerous new science and applications in many different fields. In addition to traditional methods, machine learning and deep learning models are being adopted to revolutionize deriving intelligence from multiple sensor types and complex data. We have witnessed continuous successes in crop monitoring, yield prediction, land cover and land use change detection, forest health assessment, urban/suburban characterization, and more. This session will focus on showcasing methodologies and applications for the use of data fusion and advanced analytics. We invite you to share findings and discuss how science and technology advancements will contribute to sustainable agriculture, forest, natural resources, and urban systems.
Index Terms:

0416 Biogeophysics [BIOGEOSCIENCES]
0439 Ecosystems, structure and dynamics [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1632 Land cover change [GLOBAL CHANGE]

Primary Convener:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States
Conveners:  Martha Anderson, USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, United States and Xiaoyuan Yang, Microsoft, San Jose, United States
Student/Early Career Convener:  Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, United States
 
Assessing the Impact of Spatial and Spectral Resolution of Spectral Data on Retrieval Accuracy of Sorghum and Corn Nitrogen Content (1045698)
Kianoosh Hassani1, Hamed Gholizadeh1, Saleh Taghvaeian2, Victoria A Natalie3, Jonathan Carpenter4 and Jamey Jacob5, (1)Oklahoma State University, Geography, Stillwater, United States, (2)Oklahoma State University, Biosystems and Agricultural Engineering, Stillwater, United States, (3)Oklahoma State University, Unmanned Systems Research Institute, Stillwater, United States, (4)Oklahoma State University, Plant and Soil Sciences, Stillwater, United States, (5)Oklahoma State University, Unmanned Systems Research Institute, Stillwater, OK, United States
 
A Novel Phenology Guided Deep Learning Model for Within-Season Field-Level Crop Mapping (1064198)
Zijun Yang1, Chunyuan Diao1 and Feng Gao2, (1)University of Illinois at Urbana-Champaign, Department of Geography & Geographic Information Science, Urbana, United States, (2)USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, United States
 
Towards Improving the Uncertainty Estimates for Gap-Filled Planet Fusion Surface Reflectance Using SAR (1067651)
Julie Nutini and Rasmus Houborg, Planet Labs, San Francisco, United States
 
Missing data gap-filling framework for Optical Satellite Imageries focused on Surface Water Extent (1078030)
Alexis Declaro and Shinjiro Kanae, Tokyo Institute of Technology, Department of Civil and Environmental Engineering, Tokyo, Japan
 
Progression of Early Blight Infestation in Potato from Imaging Spectroscopy (1081704)
Olee Hoi Ying Lam1, Amanda J Gevens2, Brendan Heberlein1, William B Hills1 and Philip A Townsend1, (1)University of Wisconsin-Madison, Department of Forest and Wildlife Ecology, Madison, United States, (2)University of Wisconsin-Madison, Department of Plant Pathology, Madison, United States
 
Transformer Networks for Cloud and Cloud Shadow detection in Landsat-8 and Sentinel-2 Imagery (1083019)
Zilong Zhong1, Youngryel Ryu2, Juwon Kong3, Junghee Lee4, Yulin Yan5, Ryoungseob Kwon6, Tackang Yang6 and Huaize Feng7, (1)Seoul National University, Research Institute of Agriculture and Life Sciences, Seoul, South Korea, (2)Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, South Korea, (3)Interdisciplinary Program in Landscape Architecture, Seoul National University, Seoul, South Korea, (4)Seoul National University, Research Institute of Agriculture and Life Sciences, Seoul, Korea, Republic of (South), (5)Seoul National University, Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul, Korea, Republic of (South), (6)Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, Korea, Republic of (South), (7)Seoul National University, Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul, South Korea
 
Super Resolution of Historic Landsat Imagery Using a GAN model with CubeSat Constellation Imagery (1085332)
Juwon Kong1,2, Youngryel Ryu3,4, Sungchan Jeong3,5, Zilong Zhong6, Wonseok Choi1,2, Jongmin Kim7 and Rasmus Houborg8, (1)Seoul National University, Integrated Major in Smart City Global Convergence, Seoul, Korea, Republic of (South), (2)Seoul National University, Interdisciplinary Program in Landscape Architecture, Seoul, Korea, Republic of (South), (3)Seoul National University, Interdisciplinary Program in Landscape Architecture, Seoul, South Korea, (4)Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, South Korea, (5)Seoul National University, Integrated Major in Smart City Global Convergence, Seoul, South Korea, (6)Seoul National University, Research Institute of Agriculture and Life Sciences, Seoul, South Korea, (7)University of Virginia, Department of Environmental Sciences, Charlottesville, VA, United States, (8)Planet Labs, San Francisco, United States
 
Detecting Frequent Harvest of Alfalfa with Spatio-temporal Fused Data (1090469)
Wenqi Liu, Oklahoma State University Main Campus, Geography, Stillwater, OK, United States, Haoxuan Yang, Tongji University, College of Surveying and Geo-Informatics, Shanghai, China, Yuting Zhou, Oklahoma State University, Geography, Stillwater, OK, United States, Shengfang Ma, Oklahoma State University Main Campus, Department of Computer Science, Stillwater, United States, K. Colton Flynn, USDA ARS, Grassland Soil and Water Research Laboratory, Temple, TX, United States and Pradeep Wagle, USDA-ARS, Grazinglands Research Laboratory, El Reno, United States
 
Maritime Surveillance Using SAR Satellite Images: A Multitask Deep Learning Approach (1092669)
Peder Heiselberg, DTU National Space Institute, København Ø, Denmark and Kristian Soerensen, Technical University of Denmark, Kgs. Lyngby, Denmark
 
Estimation of Maize Silage Yield and Quality Using UAV-Based Multi-Source Data (1093735)
Jiahao Fan1, Jing Zhou1, Natalia de Leon2 and Zhou Zhang1, (1)University of Wisconsin-Madison, Biological Systems Engineering, Madison, United States, (2)University of Wisconsin–Madison, Department of Agronomy, Madison, United States
 
Convolutional, recurrent or attention neural networks? Recommendations for large area Landsat time series classification (1094020)
Hankui Zhang, South Dakota State University, Department of Geography and Geospatial Sciences, and Geospatial Sciences Center of Excellence (GSCE), Brookings, United States and Dong Luo, South Dakota State University, Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, Brookings, United States
 
TSF: A Time Series Fusion model for generating regularly-spaced, cloud-/gap-free synthetic Landsat images. (1099750)
Jia Tang1, Yanchen Bo2, Chenyu Wang1 and YuSha Zhang1, (1)Beijing Normal University, Faculty of Geographical Science, Beijing, China, (2)Beijing Normal University, Beijing, China
 
Mapping Understory Change in Deciduous Forest Using Sentinel-2 Time Series Analysis (1110350)
Mari Cullerton, University of Connecticut, Groton, CT, United States, Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, United States and Chadwick Rittenhouse, University of Connecticut, Department of Natural Resources and the Environment, Storrs, CT, United States
 
Crop Type and Yield Mapping Using Long-term Satellite Observations, Weather and Field Data (Invited) (1117218)
Xiao-Peng Song, Haijun Li, Peter Potapov and Matthew Hansen, University of Maryland, Department of Geographical Sciences, College Park, United States
 
A Spatiotemporal Data Fusion Framework for Creating 1-km Hourly Land Surface Temperature (1122723)
Tao Zhang, Iowa State University, Dept. of Geological & Atmospheric Sciences, Ames, IA, United States, Yuyu Zhou, Iowa State University, Dept. of Geological & Atmospheric Sciences, Ames, United States, Zhengyuan Zhu, Iowa State University, Department of Statistics, Ames, IA, United States and Yoon Bae Jun, Iowa State University, Department of Statistics, Ames, United States
 
Combined Multi-source Optical Satellite Time-series For Land Change Detection (1141154)
Chia Che Chang1, Pin Hsuan LI2 and Kuo-Hsin Tseng2, (1)National Central University, Kanagawa, Japan, (2)National Central University, Taoyuan, Taiwan
 
Image Pre-Processing and Shadow Detection for Building Extraction from High-Resolution Satellite Images (1148144)
Dolonchapa Prabhakar, Indian Institute of Technology Roorkee, Roorkee, India and Pradeep Kumar Garg, Indian Institute of Technology Roorkee, Department of Civil Engineering, Roorkee, India
 
Improved subtle change detection using Landsat and Sentinel-2 data fusion: A study of spongy moth outbreaks in New England forests (1149337)
Kexin Song, University of Connecticut, Department of Natural Resources and the Environment, Groton, CT, United States and Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, United States
 
Corn yield prediction: from sub-field to regional level (1161039)
Matteo Ziliani, Boonyarit Changaival and Albert Abelló, Hydrosat S.à r.l., Luxembourg, Luxembourg
 
Ship Classification in Optical Imagery using Deep Learning Models with Comparison Between Sentinel-2 and PlanetScope (1167585)
Hasse Bülow Pedersen, DTU National Space Institute, København Ø, Denmark and Kristian Soerensen, Technical University of Denmark, Kgs. Lyngby, Denmark
 
Tree-level Almond Yield Prediction from High-Resolution Aerial Images with Deep Learning (1172839)
Minmeng Tang1, Dennis Sadowski2, Chen Peng2, Stavros George Vougioukas2, Brandon Klever1, Sat Darshan S. Khalsa3, Patrick Brown4 and Yufang Jin1, (1)University of California Davis, Land, Air and Water Resources, Davis, United States, (2)University of California Davis, Biological and Agricultural Engineering, Davis, United States, (3)University of California Davis, Plant Sciences, Davis, United States, (4)University of California Davis, Department of Plant Sciences, Davis, United States
 
Detecting and Localizing Anomalous Radio Frequency Interference in Satellite Images using Unsupervised Deep Learning (1174605)
Kristian Soerensen, Technical University of Denmark, Kgs. Lyngby, Denmark and Peder Heiselberg, DTU National Space Institute, København Ø, Denmark
 
Modeling Miscanthus yield using multi-temporal optical and SAR imagery (1174979)
Paul R Adler1, Timothy M. Riley Jr1, Peder Olsen2, Matthew W Myers3 and Arif Masrur4, (1)USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, PA, United States, (2)Microsoft Research, Redmond, United States, (3)USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, United States, (4)Pennsylvania State University Main Campus, Geography, University Park, PA, United States
 
A Machine Learning Model Framework to Detect Farm Management Events from Remote Sensing Time Series Data (1181406)
Damien J Sulla-Menashe, Bobby H Braswell Jr, Maggie Connell, Douglas Bolton, Eli Melaas and Jordan Graesser, Indigo Ag, Boston, United States
 
In Season Oat Yield Prediction Using UAV-based Multispectral Imagery and Automated Machine Learning (1193470)
Maitiniyazi Maimaitijiang, South Dakota State University, Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, Brookings, United States, Hankui Zhang, South Dakotat State University, Department of Geography and Geospatial Sciences, and Geospatial Sciences Center of Excellence (GSCE), Brookings, United States, Bruce Millett, South Dakota State University, Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, Brookings, SD, United States and Melanie Caffé, South Dakota State Unviersity, Department of Agronomy, Horticulture and Plant Science, Brookings, SD, United States
 
Bale Grazing and Sacrificed Pastures Monitoring Using Integration of Sentinel Satellite Images and Machine Learning Techniques (1196243)
Milad Vahidi, Ph.D. student, School of Plant and Environmental Sciences (SPES), Blacksburg, United States, Sanaz Shafian, Assistant Professor, School of Plant and Environmental Sciences (SPES), Blacksburg, United States, Summer Thomas, Ph.D. student, School of Plant and Environmental sciences (SPES), Blacksburg, United States and Rory Maguire, Professor, School of Plant and Environmental Sciences (SPES), Blacksburg, United States
 
Potential of Pre-harvest WorldView-3 Satellite data in Predicting Soybean Yield and Compositions Using Machine Learning Algorithms (1198143)
Kamila Dilmurat1, Vasit Sagan1, Haireti Alifu and Felix Fritschi3, (1)Geospatial Institute, Saint Louis University, Department of Earth and Atmospheric Sciences, Saint Louis, United States, (2)University of Missouri, Plant Sciences, Columbia, MO, United States
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