Estimating Daily Global Evapotranspiration Using Penman–Monteith Equation and Remotely Sensed Land Surface Temperature

Abstract

Daily evapotranspiration (ET) is modeled globally for the period 2000-2013 based on the Penman-Monteith equation with radiation and vapor pressures derived using remotely sensed Land Surface Temperature (LST) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites. The ET for a given land area is based on four surface conditions: wet/dry and vegetated/non-vegetated. For each, the ET resistance terms are based on land cover, leaf area index (LAI) and literature values. The vegetated/non-vegetated fractions of the land surface are estimated using land cover, LAI, a simplified version of the Beer-Lambert law for describing light transition through vegetation and newly derived light extension coefficients for each MODIS land cover type. The wet/dry fractions of the land surface are nonlinear functions of LST derived humidity calibrated using in-situ ET measurements. Results are compared to in-situ measurements (average of the root mean squared errors and mean absolute errors for 39 sites are 0.81 mm day −1 and 0.59 mm day −1 , respectively) and the MODIS ET product, MOD16, (mean bias during 2001-2013 is −0.2 mm day −1 ). Although the mean global difference between MOD16 and ET estimates is only 0.2 mm day −1 , local temperature derived vapor pressures are the likely contributor to differences, especially in energy and water limited regions. The intended application for the presented model is simulating ET based on long-term climate forecasts (e.g., using only minimum, maximum and mean daily or monthly temperatures).

Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

“…The Global Soil Dataset for use in Earth system models (GSDE) was used to estimate saturated hydraulic conductivity and saturated moisture content. (Raoufi and Beighley, 2017). For the historical (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) and future climate simulations (2081-2100), downscaled precipitation and temperature from 10 climate models (please refer to Pierce et al, 2014, andPierce et al ,2015, for model details) in the Coupled Model Inter-Comparison Project, Phase 5, (CMIP5) (Taylor et al, 2012) for two emission scenarios, RCP 4.5 and RCP 8.5 (Moss et al, 2010), were used.…”

“…Some studies have been performed to investigate uncertainties mentioned above at both variable scales (for example, Wilby and Harris, 2006;Vetter et al, 2015;Valentina et al, 2017;Kay et al, 2009;Eisner et al, 2017;Su et al, 2017;Schewe et al, 2014;Hagemann et al, 2013;Asadieh and Krakauer, 2017;Chegwidden et al, 2019;Hattermann et al, 2018;Addor et al, 2014;Vidal et al, 2016;Giuntoli et al, 2018;Alder and Hostetler, 2019). Most previous studies treated hydrologic models as a whole package.…”

A multidisciplinary coastal vulnerability assessment for local government focused on ecosystems, Santa Barbara area, California

“…Subsurface runoff is estimated as a function of soil moisture and saturation hydraulic conductivity. Potential evapotranspiration (PET) was used to quantify evaporation from land surface and transpiration through vegetation, which was estimated using Priestley and Taylor method (Priestley and Taylor, 1972) with the Food and Agriculture Organization of the United Nations (FAO) limited climate data approximations (Raoufi and Beighley, 2017). After the runoff excess was generated from each grid, it is transported over hillslopes using a kinematic approximation approach; after the runoff reaches channels, diffusion wave routing is used to simulate the hydraulics of channel flow.…”

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

“…Therefore, the LST is a key parameter for the physical description of the surface energy and water balance processes at the local to global scale [1,2]. The potential of this crucial parameter has been repeatedly demonstrated in various thermal infrared-based studies and applications, such as evapotranspiration [3,4], hydrological modelling [5], vegetation monitoring [6], 'urban heat island and urban development' [7][8][9], climate change and weather conditions [1,10], agriculture [3,11], and the monitoring of land use changes in wetlands [12]. In agricultural applications, precision farming has increasingly required thermal remote sensing techniques to detect water-stressed crops [3,13,14], plant diseases [13,15], and for irrigation management [13,14].…”