Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models

Abstract. Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using surface energy balance (SEB) and soil-water balance models. Both modelling approaches use remotely sensed data to estimate evapotranspiration fluxes in a spatially distributed way. The first approach exploits visible (VIS), near-infrared (NIR) and thermal (TIR) observations to solve the surface energy balance equation whereas the soil-water balance model uses only VIS-NIR data to detect the spatial variability of crop parameters. Considering that the study area is characterized by typical spatially sparse Mediterranean vegetation, i.e. olive, citrus and vineyards, alternating bare soil and canopy, we focused the attention on the main conceptual differences between one-source and two-sources energy balance models. Two different models have been tested: the widely used one-source SEBAL model, where soil and vegetation are considered as the sole source (mostly appropriate in the case of uniform vegetation coverage) and the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared vs. the outputs of the agro-hydrological SWAP model, which was applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. Remote sensing data in the VIS and NIR spectral ranges have been used to infer spatially distributed vegetation parameters needed to set up the upper boundary condition of SWAP. Actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference to be used for energy balance models accuracy assessment. Airborne hyperspectral data acquired during a NERC (Natural Environment Research Council, UK) campaign in 2005 have been used. The results of this investigation seem to prove a slightly better agreement between SWAP and TSEB for some fields of the study area. Further investigations are programmed in order to confirm these indications.

[1]  Z. Li,et al.  Temperature-independent spectral indices in thermal infrared bands , 1990 .

[2]  Junguo Liu,et al.  A GIS-based tool for modelling large-scale crop-water relations , 2009, Environ. Model. Softw..

[3]  Robert J. Gurney,et al.  The theoretical relationship between foliage temperature and canopy resistance in sparse crops , 1990 .

[4]  G. L. Ashcroft,et al.  Physical edaphology: The physics of irrigated and nonirrigated soils , 1972 .

[5]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[6]  R. Ragab,et al.  A holistic generic integrated approach for irrigation, crop and field management: the SALTMED model , 2002, Environ. Model. Softw..

[7]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature [Agric. For. Meteorol., 77 (1995) 263–293]☆ , 1996 .

[8]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[9]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[10]  J. Qi,et al.  A three component model to estimate sensible heat flux over sparse shrubs in Nevada , 1997 .

[11]  Mario Minacapilli,et al.  A semi-empirical approach for surface soil water content estimation from radar data without a-priori information on surface roughness , 2006 .

[12]  G. Campbell,et al.  An Introduction to Environmental Biophysics , 1977 .

[13]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[14]  Kurtis J. Thome,et al.  Vicarious Radiometric Calibrations of EOS Sensors , 1996 .

[15]  Feike J. Leij,et al.  The RETC code for quantifying the hydraulic functions of unsaturated soils , 1992 .

[16]  R. Crago,et al.  Conservation and variability of the evaporative fraction during the daytime , 1996 .

[17]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[18]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[19]  J. Clevers.  Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture , 1989 .

[20]  H. E. Jobson.  Evaporation Into the Atmosphere: Theory, History, and Applications , 1982 .

[21]  Wim G.M. Bastiaanssen,et al.  Twenty-five years modeling irrigated and drained soils: State of the art , 2007 .

[22]  Wim G.M. Bastiaanssen,et al.  Distributed agro-hydrological modeling of an irrigation system in western Turkey , 2000 .

[23]  Guido D'Urso,et al.  Regional application of one-dimensional water flow models for irrigation management , 1999 .

[24]  J. Goudriaan,et al.  Crop Micrometeorology: A Simulation Study , 1977 .

[25]  Warren J. Busscher,et al.  Simulation of Field Water Use and Crop Yield , 1980 .

[26]  Wilfried Brutsaert,et al.  Regional surface fluxes from satellite-derived surface temperatures (AVHRR) and radiosonde profiles , 1992 .

[27]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[28]  Joop G Kroes,et al.  User's guide of SWAP version 2.0 : Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment , 1997 .

[29]  Mario Minacapilli,et al.  A distributed agro-hydrological model for irrigation water demand assessment , 2008 .

[30]  C. M. U. Nealeb,et al.  Upscaling ground observations of vegetation water content , canopy height , and leaf area index during SMEX 02 using aircraft and Landsat imagery , 2004 .

[31]  Di Long,et al.  Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT , 2008 .

[32]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[33]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

[34]  L. A. Richards.  Capillary conduction of liquids through porous mediums , 1931 .

[35]  Z. Su.  The Surface Energy Balance System ( SEBS ) for estimation of turbulent heat fluxes , 2002 .

[36]  Wilfried Brutsaert,et al.  Evaporation into the atmosphere : theory, history, and applications , 1982 .

[37]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[38]  W. L. Powers.  Physical Edaphology: The Physics of Irrigated and Non-irrigated Soils , 1974 .

[39]  Mario Minacapilli,et al.  Actual evapotranspiration estimation by means of airborne and satellite remote sensing data , 2006, SPIE Remote Sensing.

[40]  Jetse D. Kalma,et al.  Estimating evaporation from pasture using infrared thermometry: evaluation of a one-layer resistance model. , 1990 .

[41]  G. D. Urso.  Simulation and management of on-demand irrigation systems: a combined agrohydrological and remote sensing approach , 2001 .

[42]  Anju Gaur,et al.  Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment , 2008 .

[43]  Joop G Kroes,et al.  Integrated modelling of the soil–water–atmosphere–plant system using the model SWAP 2·0 an overview of theory and an application , 2000 .

[44]  Martha C. Anderson,et al.  Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery , 2004 .

[45]  J. Doorenbos,et al.  Yield response to water , 1979 .

[46]  Gert A. Schultz,et al.  Remote Sensing in Hydrology and Water Management , 2000 .

[47]  R. Crippen.  Calculating the vegetation index faster , 1990 .

[48]  J. Bouma,et al.  Soil structure assessment. , 1986 .

[49]  J. Wallace,et al.  Evaporation from sparse crops‐an energy combination theory , 2007 .

[50]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[51]  J. Norman,et al.  Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover , 1999 .

[52]  M. Minacapilli,et al.  Evaluating actual evapotranspiration by means of multi-platform remote sensing data : a case study in Sicily , 2007 .

[53]  Wade T. Crow,et al.  Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model , 2008 .

[54]  W. Brutsaert.  Evaporation into the atmosphere , 1982 .

[55]  A. French,et al.  Obtaining surface energy fluxes from remotely sensed data , 2005 .

[56]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[57]  William P. Kustas,et al.  Utility of Radiometric–aerodynamic Temperature Relations for Heat Flux Estimation , 2007 .