The R Journal: article published in 2016, volume 8:2

water: Tools and Functions to Estimate Actual Evapotranspiration Using Land Surface Energy Balance Models in R PDF download
Guillermo Federico Olmedo, Samuel Ortega-Farías, Daniel de la Fuente-Sáiz, David Fonseca- Luego and Fernando Fuentes-Peñailillo , The R Journal (2016) 8:2, pages 352-369.

Abstract The crop water requirement is a key factor in the agricultural process. It is usually estimated throughout actual evapotranspiration (ETa ). This parameter is the key to develop irrigation strategies, to improve water use efficiency and to understand hydrological, climatic, and ecosystem processes. Currently, it is calculated with classical methods, which are difficult to extrapolate, or with land surface energy balance models (LSEB), such as METRIC and SEBAL, which are based on remote sensing data. This paper describes water, an open implementation of LSEB. The package provides several functions to estimate the parameters of the LSEB equation from satellite data and proposes a new object class to handle weather station data. One of the critical steps in METRIC is the selection of “cold” and “hot” pixels, which water solves with an automatic method. The water package can process a batch of satellite images and integrates most of the already published sub-models for METRIC. Although water implements METRIC, it will be expandable to SEBAL and others in the near future. Finally, two different procedures are demonstrated using data that is included in water package.

Received: 2016-04-30; online 2016-12-12
CRAN packages: raster, raster , CRAN Task Views implied by cited CRAN packages: Spatial, SpatioTemporal


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This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-051,
  author = {Guillermo Federico Olmedo and Samuel Ortega-Farías and
          Daniel de la Fuente-Sáiz and David Fonseca- Luego and
          Fernando Fuentes-Peñailillo},
  title = {{water: Tools and Functions to Estimate Actual
          Evapotranspiration Using Land Surface Energy Balance Models
          in R}},
  year = {2016},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2016/RJ-2016-051/index.html},
  pages = {352--369},
  volume = {8},
  number = {2}
}