Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing

Abstract

Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ET a ) and meteorological data to project ET a over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ET a and measured ET a are in the range of 0.45-0.95, and root mean square errors are in the range of 10-30% of mean ET a values across biomes, similar to methods that use thermal infrared bands to estimate ET a and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational

Groundwater-dependent ecosystems: recent insights from satellite and field-based studies

“…phenology, LAI) and function (e.g. ET, gross primary productivity) has become increasingly sophisticated (Glenn et al, 2010;Yuan et al, 2010;Jung et al, 2011;Rossini et al, 2012;Kanniah et al, 2013;Ma et al, 2013;Nagler et al, 2013) and increasingly applied to realworld applications of water resources management (Scott et al, 2008;Glenn et al, 2010;Barron et al, 2014;Doody et al, 2014). Remote sensing (RS) provides a robust and spatially explicit means to assess not only vegetation structure and function but also relationships amongst these and climate variables.…”

“…R n is net radiation and G is soil heat flux. Differences in temperature between air temperature and canopy temperature have been used to estimate sensible heat flux (Glenn et al, 2010). Using the reasonable assumption that G averages out to zero over any single 24 h period and R n is either measured or derived from remote sensing data, then LE (that is, ET) can be calculated by difference.…”

“…Accurate and spatially distributed estimates of discharge through vegetation are difficult to obtain through field measurements. Recently, RS methods have been calibrated against Penman-Monteith estimates of ET (Glenn et al, 2010;Nagler et al, 2013;Doody et al, 2014), which requires only standard weather data (net radiation, wind speed and vapour pressure deficit) and thus increases the coverage of calibration sites. Because ET in GDEs is generally not limited by soil moisture when groundwater is of high quality (i.e.…”

“…If a riparian corridor is sufficiently wide, eddy covariance can be used to directly measure ET . Where the corridor is insufficiently wide, tree-scale sap flow techniques can be used (O'Grady et al, 2006;Goodrich et al, 2000b). Combinations of both methods (Moore et al, 2008;Oishi et al, 2008) can be used to partition transpiration from evapotranspiration (Scott et al, 2006a), thereby estimating the proportion of ET due to transpiration from groundwater with the condition that groundwater evaporation is negligible.…”

Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

“…Plant reflectance properties in the red and near infrared regions are especially recruited for differentiating soil, water and vegetation with vegetation indices (Glenn et al, 2008). Vegetation indices can be used to estimate vegetation water content (Cheng et al, 2006, based on gravimetric or leaf water content (Cheng et al, 2011, Cheng et al, 2013, equivalent water thickness (EWT; water depth/per pixel; knowledge of pixel area provides the volume estimate), changes which are detectable in the short wave infrared region (1.1-2.5 μm), and evapotranspiration processes by including temperature from thermal infrared measurements (Glenn et al, 2010, Nagler et al, 2005a, Nagler et al, 2005b, thus together, identifying agricultural regions receiving excess or insufficient water supplies. Plant physiology and thus reflectance properties respond differently depending upon the time of year, crop type, and management strategies.…”