Evaluation of Multiple Methods for the Production of Continuous Evapotranspiration Estimates from TIR Remote Sensing

Continuous daily estimates of evapotranspiration (ET) spatially distributed at plot scale are required to monitor the water loss and manage crop irrigation needs. Remote sensing approaches in the thermal infrared (TIR) domain are relevant to assess actual ET and soil moisture status but due to lengthy return intervals and cloud cover, data acquisition is not continuous over time. This study aims to assess the performances of 6 commonly used as well as two new reference quantities including rainfall as an index of soil moisture availability to reconstruct seasonal ET from sparse estimates and as a function of the revisit frequency. In a first step, instantaneous in situ eddy-covariance flux tower data collected over multiple ecosystems and climatic areas were used as a proxy for perfect retrievals on satellite overpass dates. In a second step, instantaneous estimations at the time of satellite overpass were produced using the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) energy balance model in order to evaluate the errors concurrent to the use of an energy balance model simulating the instantaneous IRT products from the local surface temperature. Significant variability in the performances from site to site was observed particularly for long revisit frequencies over 8 days, suggesting that the revisit frequency necessary to achieve accurate estimates of ET via temporal upscaling needs to be fewer than 8 days whatever the reference quantity used. For shorter return interval, small differences among the interpolation techniques and reference quantities were found. At the seasonal scale, very simple methods using reference quantities such as the global radiation or clear sky radiation appeared relevant and robust against long revisit frequencies. For infra-seasonal studies targeting stress detection and irrigation management, taking the amount of precipitation into account seemed necessary, especially to avoid the underestimation of ET over cloudy days during a long period without data acquisitions.

[1]  Simon J. Hook,et al.  Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty - A Sensitivity Study Using AmeriFlux Data , 2019, Remote. Sens..

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

[3]  Wim G.M. Bastiaanssen,et al.  Satellite surveillance of evaporative depletion across the Indus Basin , 2002 .

[4]  Dara Entekhabi,et al.  Analysis of evaporative fraction diurnal behaviour , 2007 .

[5]  Albert Olioso,et al.  Evapotranspiration Estimation in the Sahel Using a New Ensemble-Contextual Method , 2020, Remote. Sens..

[6]  S. Idso,et al.  Wheat canopy temperature: A practical tool for evaluating water requirements , 1977 .

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

[8]  Ronglin Tang,et al.  Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation , 2013, Remote. Sens..

[9]  Gilles Boulet,et al.  Deriving daily evapotranspiration from remotely sensed instantaneous evaporative fraction over olive orchard in semi-arid Morocco , 2008 .

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

[11]  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 .

[12]  Angelika Bayer,et al.  Solar Engineering Of Thermal Processes , 2016 .

[13]  V. Dantec,et al.  Analysis of evapotranspiration components of a rainfed olive orchard during three contrasting years in a semi-arid climate , 2018 .

[14]  T. A. Black,et al.  On the temporal upscaling of evapotranspiration from instantaneous remote sensing measurements to 8-day mean daily-sums , 2012 .

[15]  Martha C. Anderson,et al.  A data fusion approach for mapping daily evapotranspiration at field scale , 2013 .

[16]  Tim R. McVicar,et al.  Correcting for systematic error in satellite-derived latent heat flux due to assumptions in temporal scaling: Assessment from flux tower observations , 2011 .

[17]  Glynn C. Hulley,et al.  New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California , 2019, Remote. Sens..

[18]  Albert Olioso,et al.  Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass , 2012 .

[19]  Ray D. Jackson,et al.  Estimation of Daily Evapotranspiration from one Time-of-Day Measurements , 1983 .

[20]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[21]  S. McNaughton,et al.  Serengeti Migratory Wildebeest: Facilitation of Energy Flow by Grazing , 1976, Science.

[22]  Lu Zhang,et al.  Evaluation of daily evapotranspiration estimates from instantaneous measurements , 1995 .

[23]  Eric Elguero,et al.  Examination of evaporative fraction diurnal behaviour using a soil-vegetation model coupled with a mixed-layer model , 1999 .

[24]  Albert Olioso,et al.  Evaluation of land surface model simulations of evapotranspiration over a 12-year crop succession: impact of soil hydraulic and vegetation properties , 2015 .

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

[26]  William P. Kustas,et al.  Effect of the revisit interval and temporal upscaling methods on the accuracy of remotely sensed evapotranspiration estimates , 2017 .

[27]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

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

[29]  Wilfried Brutsaert,et al.  Application of self‐preservation in the diurnal evolution of the surface energy budget to determine daily evaporation , 1992 .

[30]  J. Demarty,et al.  Building a field- and model-based climatology of surface energy and water cycles for dominant land cover types in the cultivated Sahel. Annual budgets and seasonality , 2020 .

[31]  Richard Crago,et al.  Hourly and daytime evapotranspiration from grassland using radiometric surface temperatures , 2004 .

[32]  Martha C. Anderson,et al.  Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation , 2009 .

[33]  J. A. Tolk,et al.  Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements , 2005 .

[34]  Albert Olioso,et al.  An empirical expression to relate aerodynamic and surface temperatures for use within single-source energy balance models , 2012 .

[35]  Albert Olioso,et al.  EVASPA (EVapotranspiration Assessment from SPAce) Tool: An overview☆ , 2013 .

[36]  Wilfried Brutsaert,et al.  Daytime evaporation and the self-preservation of the evaporative fraction and the Bowen ratio , 1996 .

[37]  Michael R. Raupach,et al.  Combination theory and equilibrium evaporation , 2001 .

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

[39]  Gérard Dedieu,et al.  The Indian-French Trishna Mission: Earth Observation in the Thermal Infrared with High Spatio-Temporal Resolution , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[40]  Claudia Notarnicola,et al.  High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[41]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[42]  William E. Nichols,et al.  Evaluation of the evaporative fraction for parameterization of the surface energy balance , 1993 .

[43]  Albert Olioso,et al.  Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates , 2018, Remote. Sens..

[44]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion , 2014 .

[45]  Shilong Piao,et al.  Temperature sensitivity of soil respiration in different ecosystems in China , 2009 .

[46]  Matthew F. McCabe,et al.  ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station , 2015, Water Resources Research.

[47]  Wim G.M. Bastiaanssen,et al.  Evaluation of the temporal variability of the evaporative fraction in a tropical watershed , 2004 .

[48]  Tim R. McVicar,et al.  Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias , 2012 .