Evapotranspiration monitoring based on thermal infrared data over agricultural landscapes: comparison of a simple energy budget model and a SVAT model

Abstract. The overall purpose of our work is to take advantage of Thermal Infra-Red (TIR) imagery to estimate landscape evapotranspiration fluxes over agricultural areas, relying on two approaches of increasing complexity and input data needs: a Surface Energy Balance (SEB) model, TSEB, used directly at the landscape scale with TIR forcing, and the aggregation of a Soil-Vegetation-Atmosphere Transfer (SVAT) model, SEtHyS, run at high resolution (≃100 m) and constrained by assimilation of TIR data. Within this preliminary study, models skills are compared thanks to large in situ database covering different crops, stress and climate conditions. Domains of validity are assessed and the possible loss of performance resulting from inaccurate but realistic inputs (forcing and model parameters) due to scaling effects are quantified. The in situ data set came from 3 experiments carried out in southern France and in Morocco. On average, models provide half-hourly averaged estimations of latent heat flux (LE) with a RMSE of around 55 W m−2 for TSEB and 47 W m−2 for SEtHyS, and estimations of sensible heat flux (H) with a RMSE of around 29 W m−2 for TSEB and 38 W m−2 for SEtHyS. TSEB has been shown to be more flexible and requires one single set of parameters but lead to low performances on rising vegetation and stressed conditions. An in-depth study on the Priestley-Taylor key parameter highlights its marked diurnal cycle and the need to adjust its value to improve flux partition between sensible and latent heat fluxes (1.5 and 1.25 for south-western France and Morocco, respectively). Optimal values of 1.8 to 2 were hilighted under cloudy conditions, which is of particular interest with the emergence of low altitude drone acquisition. SEtHyS is valid in more cases while it required a finer parameters tuning and a better knowledge of surface and vegetation. This study participates to lay the ground for exploring the complementarities between instantaneous and continuous dynamic evapotranspiration mapping monitored with TIR data.

[1]  Martha C. Anderson,et al.  Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship , 2003 .

[2]  Wade T. Crow,et al.  An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling , 2011 .

[3]  Claire Marais-Sicre,et al.  Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation , 2013 .

[4]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[5]  Eric F. Wood,et al.  A soil‐vegetation‐atmosphere transfer scheme for modeling spatially variable water and energy balance processes , 1997 .

[6]  Paul D. Colaizzi,et al.  TWO-SOURCE ENERGY BALANCE MODEL TO CALCULATE E, T, AND ET: COMPARISON OF PRIESTLEY-TAYLOR AND PENMAN-MONTEITH FORMULATIONS AND TWO TIME SCALING METHODS , 2014 .

[7]  Peter M. Lafleur,et al.  Application of an energy combination model for evaporation from sparse canopies. , 1990 .

[8]  P. Wetzel,et al.  Evapotranspiration from Nonuniform Surfaces: A First Approach for Short-Term Numerical Weather Prediction , 1988 .

[9]  Yasushi Yamaguchi,et al.  Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area , 2006 .

[10]  A. Meijerink,et al.  Surface energy balance using satellite data for the water balance of a traditional irrigation—wetland system in SW Iran , 2005 .

[11]  Catherine Ottlé,et al.  An improved SVAT model calibration strategy based on the optimisation of surface temperature temporal dynamics , 2007 .

[12]  Shuichi Rokugawa,et al.  A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images , 1998, IEEE Trans. Geosci. Remote. Sens..

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

[14]  K. Taylor Summarizing multiple aspects of model performance in a single diagram , 2001 .

[15]  J. Ross The radiation regime and architecture of plant stands , 1981, Tasks for vegetation sciences 3.

[16]  J. V. Soares,et al.  Differential Bare Field Drainage Properties From Airborne Microwave Observations , 1986 .

[17]  H. Douville Validation and sensitivity of the global hydrologic budget in stand-alone simulations with the ISBA land-surface scheme , 1998 .

[18]  Matthew F. McCabe,et al.  Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors , 2006 .

[19]  Florence Habets,et al.  Analysis of Near-Surface Atmospheric Variables: Validation of the SAFRAN Analysis over France , 2008 .

[20]  P. Milly Climate, soil water storage, and the average annual water balance , 1994 .

[21]  Catherine Ottlé,et al.  Contribution of Thermal Infrared Remote Sensing Data in Multiobjective Calibration of a Dual-Source SVAT Model , 2006 .

[22]  Gururaj Hunsigi,et al.  Irrigation and drainage , 2009 .

[23]  J. V. Soares,et al.  Estimation of bare soil evaporation from airborne measurements , 1988 .

[24]  A. Chehbouni,et al.  Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices , 2006 .

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

[26]  Damian Barrett,et al.  On the efficacy of combining thermal and microwave satellite data as observational constraints for root-zone soil moisture estimation. , 2009 .

[27]  Ü. Rannik,et al.  Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology , 2000 .

[28]  S. J. Birks,et al.  Terrestrial water fluxes dominated by transpiration , 2013, Nature.

[29]  Guillaume Bigeard Estimation spatialisée de l'évapotranspiration à l'aide de données infra-rouge thermique multi-résolutions , 2014 .

[30]  Salah Er-Raki,et al.  Characterization of Evapotranspiration over Irrigated Crops in a Semi-arid Area (Marrakech, Morocco) Using an Energy Budget Model , 2013 .

[31]  Martin Claverie Estimation spatialisée de la biomasse et des besoins en eau des cultures à l'aide de données satellitales à hautes résolutions spatiale et temporelle : application aux agrosystèmes du sud-ouest de la France , 2012 .

[32]  Olivier Merlin,et al.  Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate , 2013 .

[33]  Thomas J. Jackson,et al.  Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape , 2006 .

[34]  Thomas J. Jackson,et al.  Utility of Remote Sensing–Based Two-Source Energy Balance Model under Low- and High-Vegetation Cover Conditions , 2005 .

[35]  J. Norman,et al.  Remote sensing of surface energy fluxes at 101‐m pixel resolutions , 2003 .

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

[37]  P. Béziat,et al.  Carbon balance of a three crop succession over two cropland sites in South West France , 2009 .

[38]  B. Séguin,et al.  Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches , 2005 .

[39]  M. Bierkens,et al.  Assimilation of remotely sensed latent heat flux in a distributed hydrological model , 2003 .

[40]  José A. Sobrino,et al.  An integrated modelling and remote sensing approach for hydrological study in arid and semi‐arid regions: the SUDMED Programme , 2008 .

[41]  William P. Kustas,et al.  Effect of remote sensing spatial resolution on interpreting tower-based flux observations , 2006 .

[42]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[43]  Claire Marais-Sicre,et al.  Sensitivity of TerraSAR-X, RADARSAT-2 and ALOS satellite radar data to crop variables , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[44]  Salah Er-Raki,et al.  Performance of the two-source energy budget (TSEB) model for the monitoring of evapotranspiration over irrigated annual crops in North Africa , 2017 .

[45]  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 .

[46]  A. S. Thom,et al.  Momentum, mass and heat exchange of vegetation , 1972 .

[47]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[48]  Matthew F. McCabe,et al.  Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA) , 2005 .

[49]  T. Schmugge,et al.  Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX , 2004 .

[50]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[51]  W. Kustas,et al.  Intercomparison of Spatially Distributed Models for Predicting Surface Energy Flux Patterns during SMACEX , 2005 .

[52]  J. Schmetz,et al.  Supplement to An Introduction to Meteosat Second Generation (MSG) , 2002 .

[53]  A. French Scaling of surface energy fluxes using remotely sensed data , 2001 .

[54]  William P. Kustas,et al.  An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX , 2009 .

[55]  Luis A. Bastidas,et al.  Constraining a physically based Soil‐Vegetation‐Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach , 2005 .

[56]  Albert Olioso,et al.  The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat , 2015 .

[57]  M. Claverie,et al.  Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data , 2012 .

[58]  W. Oechel,et al.  Energy balance closure at FLUXNET sites , 2002 .

[59]  C. Bhumralkar Numerical Experiments on the Computation of Ground Surface Temperature in an Atmospheric General Circulation Model , 1975 .

[60]  T. Carlson An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

[61]  S. Goetz,et al.  Satellite remote sensing of surface energy balance : success, failures, and unresolved issues in FIFE , 1992 .

[62]  José A. Sobrino,et al.  Estimation of the Spatially Distributed Surface Energy Budget for AgriSAR 2006, Part I: Remote Sensing Model Intercomparison , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[63]  B. Ritter,et al.  A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations , 1992 .

[64]  Keith Beven,et al.  Conditioning a multiple‐patch SVAT Model using uncertain time‐space estimates of latent heat fluxes as inferred from remotely sensed data , 1999 .

[65]  D. Vidal-Madjar,et al.  Evaporation From a Bare Soil Evaluated Using a Soil Water Transfer Model and Remotely Sensed Surface Soil Moisture Data , 1984 .

[66]  Lionel Jarlan,et al.  Assessment of reference evapotranspiration methods in semi-arid regions: can weather forecast data be used as alternate of ground meteorological parameters? , 2010 .

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

[68]  Isabel F. Trigo,et al.  Incoming Solar and Infrared Radiation Derived from METEOSAT: Impact on the Modeled Land Water and Energy Budget over France , 2012 .

[69]  J. Famiglietti,et al.  Multiscale modeling of spatially variable water and energy balance processes , 1994 .

[70]  Christophe François,et al.  The potential of directional radiometric temperatures for monitoring soil and leaf temperature and soil moisture status , 2002 .

[71]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[72]  F. Chapin,et al.  A Comparative Approach to Regional Variation in Surface Fluxes Using Mobile Eddy Correlation Towers , 1997 .

[73]  J. T. Ball,et al.  AN ANALYSIS OF STOMATAL CONDUCTANCE , 1988 .

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

[75]  William P. Kustas,et al.  An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes , 2007 .

[76]  A. Verhoef,et al.  A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide , 1997 .

[77]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[78]  H. V. Guptaf,et al.  Using a multiobjective approach to retrieve information on surface properties used in a SVAT model , 2004 .

[79]  Gérard Dedieu,et al.  The MISTIGRI thermal infrared project: scientific objectives and mission specifications , 2013 .