An operational remote sensing algorithm of land surface evaporation

Abstract

[1] 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 ffi 0.17 and R 2 ffi 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.

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

“…The net energy exchange is a function of environmental factors such as incoming radiation, which shows large diurnal changes responding to variations in the solar zenith angle and cloud coverage. To understand the linkage of ET fluxes to surface properties, some studies have used the evaporation fraction (EF) as an index for ET [Shuttleworth et al 1989, Jiang and Islam, 2003, Nishida et al 2003]:…”

“…However, the primary surface energy source is surface shortwave (SW) radiation, which can be inferred from satellite measurements with reasonable accuracy [Pinker et al, 2003]. [Nishida et al, 2003]. …”

“…Satellite remote sensing cannot measure surface turbulent flux exchanges directly. Researches are focused on parameterizations of the exchange processes, such as canopy resistance, that may be remotely sensed by satellites [Nishida et al 2003]. Most of existing satellite remote sensing techniques for ET estimations are based on measurements at visible and near-infrared wavelengths, such as the normalized difference vegetation index (NDVI), as the spectral measurements are highly correlated to the absorbed fraction of photosyntheticallyactive radiation (PAR) [Asrar et al 1984;Sellers 1985;Myneni et al 1995;Granger 2000;Jiang and Islam, 2003;Nishida et al, 2003].…”

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

“…The triangle is formed by plotting surface temperature against a VI as a proxy for vegetation cover properties. Commonly, this VI is the normalized difference vegetation index (NDVI), but also other variables such as LAI or fractional vegetation cover were applied in the past (Jiang and Islam 2003; Nishida 2003; Tomás et al 2014). The triangular form originates from the fact that the temperature range over bare soil and low vegetation cover is larger than for fully developed canopies.…”

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

“…MODIS and GOES are routinely used to monitor drought and surface moisture deficit in relation with climatological forcing at the continental scale (e.g. Nishida et al, 2003;Anderson et al, 2007;Stisen et al, 2008). On the other hand, the temporal frequency of 90 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiome-ter (ASTER) sensor is larger than 15 days.…”