Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions.

Rajib, Adnan; Kim, I Luk; Golden, Heather E; Lane, Charles R; Kumar, Sujay V; Yu, Zhiqiang; Jeyalakshmi, Saranya

Rajib, Adnan; Kim, I Luk; Golden, Heather E; Lane, Charles R; Kumar, Sujay V; Yu, Zhiqiang; Jeyalakshmi, Saranya.

Afiliación

Rajib A; Department of Environmental Engineering, Frank H. Dotterweich College of Engineering, Texas A&M University, 917W Ave B, Kingsville, TX 78363, USA.
Kim IL; Rosen Center for Advanced Computing, Purdue University, West Lafayette, IN 47907, USA.
Golden HE; U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45220, USA.
Lane CR; U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45220, USA.
Kumar SV; NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, MD 20771, USA.
Yu Z; Civica Infrastructure Inc., Vaughan, ON L6A 4P5, Canada.
Jeyalakshmi S; Department of Civil and Environmental Engineering, University of Windsor, ON N9B 3P4, Canada.

Remote Sens (Basel) ; 12(13): 2148, 2020 Jul 04.

Article en En | MEDLINE | ID: mdl-33425378

Palabras clave

LAI; SWAT; data assimilation; ecohydrology; hydrologic modeling; remote sensing

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Remote Sens (Basel) Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

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