Open AccessAn operational remote sensing algorithm of land surface evaporation
Author(s) -
Nishida Kenlo,
Nemani Ramakrishna R.,
Running Steven W.,
Glassy Joseph M.
Publication year - 2003
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2002jd002062
Subject(s) - environmental science , evapotranspiration , vegetation (pathology) , remote sensing , satellite , available energy , energy balance , terrain , algorithm , latent heat , meteorology , computer science , energy (signal processing) , mathematics , geology , ecology , geography , statistics , medicine , pathology , aerospace engineering , engineering , biology
Partitioning of solar energy at the Earth surface has significant implications in climate dynamics, hydrology, and ecology. Consequently, spatial mapping of energy partitioning from satellite remote sensing data has been an active research area for over two decades. We developed an algorithm for estimating evaporation fraction (EF), expressed as a ratio of actual evapotranspiration (ET) to the available energy (sum of ET and sensible heat flux), from satellite data. The algorithm is a simple two‐source model of ET. We characterize a landscape as a mixture of bare soil and vegetation and thus we estimate EF as a mixture of EF of bare soil and EF of vegetation. In the estimation of EF of vegetation, we use the complementary relationship of the actual and the potential ET for the formulation of EF. In that, we use the canopy conductance model for describing vegetation physiology. On the other hand, we use “VI‐ T s ” (vegetation index‐surface temperature) diagram for estimation of EF of bare soil. As operational production of EF globally is our goal, the algorithm is primarily driven by remote sensing data but flexible enough to ingest ancillary data when available. We validated EF from this prototype algorithm using NOAA/AVHRR data with actual observations of EF at AmeriFlux stations (standard error ≅ 0.17 and R 2 ≅ 0.71). Global distribution of EF every 8 days will be operationally produced by this algorithm using the data of MODIS on EOS‐PM (Aqua) satellite.