K
Kerry Cawse-Nicholson
Researcher at California Institute of Technology
Publications - 52
Citations - 1029
Kerry Cawse-Nicholson is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Lidar & Hyperspectral imaging. The author has an hindex of 14, co-authored 42 publications receiving 519 citations. Previous affiliations of Kerry Cawse-Nicholson include Rochester Institute of Technology & University of the Witwatersrand.
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Journal ArticleDOI
ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station
Joshua B. Fisher,Brian Lee,A. J. Purdy,Gregory Halverson,Matthew B. Dohlen,Kerry Cawse-Nicholson,Audrey Wang,Ray G. Anderson,B. Aragon,M. Altaf Arain,Dennis D. Baldocchi,John M. Baker,Hélène Barral,Carl J. Bernacchi,Carl J. Bernacchi,Christian Bernhofer,Sébastien C. Biraud,Gil Bohrer,Nathaniel A. Brunsell,Bernard Cappelaere,Saulo Castro-Contreras,Junghwa Chun,Bryan J. Conrad,Edoardo Cremonese,Jérôme Demarty,Ankur R. Desai,Anne De Ligne,Lenka Foltýnová,Michael L. Goulden,Timothy J. Griffis,Thomas Grünwald,Mark S. Johnson,Minseok Kang,D. Kelbe,Natalia Kowalska,Jong Hwan Lim,I. Mainassara,Matthew F. McCabe,Justine E. C. Missik,Binayak P. Mohanty,Caitlin E. Moore,Laura Morillas,Ross Morrison,J. William Munger,Gabriela Posse,Andrew D. Richardson,Eric S. Russell,Youngryel Ryu,Arturo Sanchez-Azofeifa,Marius Schmidt,Efrat Schwartz,Iain Sharp,Ladislav Šigut,Yao Tang,Glynn Hulley,Martha C. Anderson,Christopher Hain,Andrew N. French,Eric F. Wood,Simon J. Hook +59 more
TL;DR: In 2018, the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station by the National Aeronautics and Space Administration (NASA).
Journal ArticleDOI
NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms
Kerry Cawse-Nicholson,Philip A. Townsend,David S. Schimel,Ali M. Assiri,Pamela L. Blake,Maria Fabrizia Buongiorno,Petya K. E. Campbell,Nimrod Carmon,Kimberly A. Casey,Rosa Elvira Correa-Pabón,Kyla M. Dahlin,Hamid Dashti,Philip E. Dennison,Heidi M. Dierssen,Adam Erickson,Joshua B. Fisher,Robert Frouin,Charles K. Gatebe,Hamed Gholizadeh,Michelle M. Gierach,Nancy F. Glenn,Nancy F. Glenn,James A. Goodman,Daniel M. Griffith,Daniel M. Griffith,Liane S. Guild,Christopher R. Hakkenberg,Eric J. Hochberg,Thomas R. H. Holmes,Chuanmin Hu,Glynn Hulley,Karl F. Huemmrich,Karl F. Huemmrich,Raphael M. Kudela,Raymond F. Kokaly,Christine Lee,Roberta E. Martin,Charles E. Miller,Wesley J. Moses,Frank E. Muller-Karger,Joseph D. Ortiz,D. B. Otis,Nima Pahlevan,T. H. Painter,Ryan Pavlick,Ben Poulter,Yi Qi,Vincent Realmuto,Dar A. Roberts,Michael E. Schaepman,Fabian D. Schneider,Florian M. Schwandner,Shawn P. Serbin,Alexey N. Shiklomanov,E. Natasha Stavros,E. Natasha Stavros,David R. Thompson,J. L. Torres-Perez,Kevin R. Turpie,Kevin R. Turpie,Maria Tzortziou,Maria Tzortziou,Susan L. Ustin,Qian Yu,Yusri Yusup,Qingyuan Zhang,Qingyuan Zhang +66 more
TL;DR: The 2017-2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a "designated targeted observable" (DO) as discussed by the authors.
Journal ArticleDOI
Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales
Martha C. Anderson,Yang Yang,Jie Xue,Kyle Knipper,Yun Yang,Yun Yang,Feng Gao,Christopher Hain,William P. Kustas,Kerry Cawse-Nicholson,Glynn Hulley,Joshua B. Fisher,Joseph G. Alfieri,Tilden P. Meyers,John H. Prueger,Dennis D. Baldocchi,Camilo Rey-Sanchez +16 more
TL;DR: For optimal utility in agricultural water management applications, frequent thermal imaging (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET).
Journal ArticleDOI
Marker-Free Registration of Forest Terrestrial Laser Scanner Data Pairs With Embedded Confidence Metrics
TL;DR: This study quantifies the RMSE of the proposed marker-free registration approach, assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and informs optimal sample spacing considerations for TLS data collection in New England forests.
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Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory
TL;DR: A new method for determining the intrinsic dimension of a hyperspectral image using recent advances in random matrix theory is discussed, entirely unsupervised, free from any user-determined parameters and allows spectrally correlated noise in the data.