Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information | Zendy

Rui Gao | Zendy; Alfonso F. TorresRua | Zendy; Ayman Nassar | Zendy; Joseph G. Alfieri | Zendy; Mahyar Aboutalebi | Zendy; Lawrence E. Hipps | Zendy; Nicolas B. Ortiz | Zendy; Andrew J. McElrone | Zendy; Calvin Coopmans | Zendy; William P. Kustas | Zendy; William A. White | Zendy; L. McKee | Zendy; María Mar Alsina | Zendy; Nick Dokoozlian | Zendy; Luis Sánchez | Zendy; John H. Prueger | Zendy; Héctor Nieto | Zendy; Nurit Agam | Zendy

Open Access

Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information

Author(s) -

Rui Gao,

Alfonso F. TorresRua,

Ayman Nassar,

Joseph G. Alfieri,

Mahyar Aboutalebi,

Lawrence E. Hipps,

Nicolas B. Ortiz,

Andrew J. McElrone,

Calvin Coopmans,

William P. Kustas,

William A. White,

L. McKee,

María Mar Alsina,

Nick Dokoozlian,

Luis Sánchez,

John H. Prueger,

Héctor Nieto,

Nurit Agam

Publication year - 2021

Publication title -

pubmed central

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1117/12.2586259

Subject(s) - evapotranspiration , leaf area index , environmental science , eddy covariance , transpiration , energy balance , water balance , remote sensing , irrigation scheduling , computer science , soil science , ecosystem , soil water , ecology , botany , photosynthesis , geotechnical engineering , engineering , biology , geology

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