Modeling evapotranspiration in a spring wheat from thermal radiometry: crop coefficients and E/T partitioning

Sánchez, Juan Manuel; López-Urrea, R.; Doña, Carolina; Caselles, Vicente; González-Piqueras, José; Niclòs, Raquel

doi:10.1007/s00271-015-0476-2

Cited by 33 publications

(11 citation statements)

References 40 publications

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“…Land Surface Temperature (LST) is a key magnitude in the characterization of the surface-atmosphere energy exchanges, evapotranspiration, meteorology, climatology and hydrology [1][2][3][4][5][6]. Remote sensing in the thermal infrared (TIR) allows the estimation of spatially distributed LST.…”

Section: Introductionmentioning

confidence: 99%

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

et al. 2018

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Monitoring Land Surface Temperature (LST) from satellite remote sensing requires an accurate correction of the atmospheric effects. Although thermal remote sensing techniques have advanced significantly over the past few decades, to date, single-band pixel-by-pixel atmospheric correction of full thermal images is unsolved. In this work, we introduce a new Single-Band Atmospheric Correction (SBAC) tool that provides pixel-by-pixel atmospheric correction parameters regardless of the pixel size. The SBAC tool uses National Centers of Environmental Prediction (NCEP) profiles as inputs and, as a novelty, it also accounts for pixel elevation through a Digital Elevation Model (DEM). Application of SBAC to 19 Landsat 7-ETM+ scenes shows the potential of the proposed pixel-by-pixel atmospheric correction to capture terrain orography or atmospheric variability within the scene. LST estimation yields negligible bias and an RMSE of ±1.6 K for the full dataset. The Landsat Atmospheric Correction Tool (ACT) is also considered for comparison. SBAC-ACT LST deviations are analyzed in terms of distance to the image center, surface elevation, and spatial distribution of the atmospheric water content. Differences within 3 K are observed. These results give us the first insight of the potential of SBAC for the operational pixel-by-pixel atmospheric correction of full thermal images. The SBAC tool is expected to help users of satellite single-channel thermal sensors to improve their LST estimates due to its simplicity and robustness.

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Section: Introductionmentioning

confidence: 99%

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

et al. 2018

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“…Refinements to the TSEB modeling parameterizations such as using Penman-Monteith formulation for estimating the vegetation temperature have been proposed (Colaizzi et al, 2012a), which can provide a more accurate partitioning between soil E and canopy T than in the original TSEB model, but requiring near-surface vapor pressure and knowledge of stomatal resistance makes it difficult to be applied operationally at large scales using satellite data. Although the TSEB model and refinements to it have been applied and evaluated under a wide variety of vegetation types, vegetation coverage, climates and spatial scales (Colaizzi et al, 2012a), there have been very few studies that have evaluated the TSEB model using the component soil and vegetation temperatures from the infrared radiometers (Colaizzi et al, 2012a;Sánchez et al 2008Sánchez et al , 2015 and also the partitioned fluxes E and T (Colaizzi et al, 2012a;Agam et al 2012). A major reason for the lack of such studies is the difficulty in obtaining reliable soil and vegetation temperatures (accounting for both shaded and sunlit soil and vegetation temperatures) and in measuring E and T that are representative at the micrometeorological scale (Colaizzi et al, 2012a;Song et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures

Song

Liu

Kustas

et al. 2016

Agricultural and Forest Meteorology

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Operational application of a remote sensing-based two source energy balance model (TSEB) to estimate evaportranspiration (ET) and the components evaporation (E), transpiration (T) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestly-Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The remote sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and thermal remote sensing data supplied by advanced scanning thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux (H), latent heat flux (LE), component LE from soil and canopy from the TSEB CT and original model (TSEB PT) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.

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“…In addition, there is great interest and parallel effort directed toward the development of improved crop coefficients, e.g., [58][59][60]. Regardless of these efforts, it is probable that improvements in ET delivery and crop coefficients will not be sufficient to overcome the limits on ET c -based irrigation management that result from declining water resources.…”

Section: Discussionmentioning

confidence: 99%

Irrigation Analysis Based on Long-Term Weather Data

Mahan

Lascano

2016

Agriculture

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Irrigation management is based upon delivery of water to a crop in the correct amount and time, and the crop's water need is determined by calculating evapotranspiration (ET) using weather data. In 1994, an ET-network was established in the Texas High Plains to manage irrigation on a regional scale. Though producers used the ET-network, by 2010 public access was discontinued. Why did producers allow a valuable irrigation-management tool to be eliminated? Our objective was to analyze the effect of declining well capacities on the usefulness of cotton ET (ET c ) for irrigation. Thirty years of daily ET c data were used to compare irrigation demand vs. irrigation responses at four locations, analyzed for multiple years and range of well capacities for three irrigation-intervals. Results indicated that when well capacities declined to the point that over-irrigation was not possible, the lower well capacities reduced the value of ET c in terms of the number of irrigations and total amount of water applied. At well capacities <1514 L·min −1 the fraction of irrigations for which ET c information was used to determine the irrigation amount was <35% across years and irrigation intervals. The value of an ET c -based irrigation may fall into disuse when irrigation-water supplies decline.

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Modeling evapotranspiration in a spring wheat from thermal radiometry: crop coefficients and E/T partitioning

Cited by 33 publications

References 40 publications

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures

Irrigation Analysis Based on Long-Term Weather Data

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