Sensitivity of evapotranspiration retrievals from the METRIC processing algorithm to improved radiometric resolution of Landsat 8 thermal data and to calibration bias in Landsat 7 and 8 surface temperature

Kiliç, Ayşe; Allen, Richard G.; Trezza, Ricardo; Ratcliffe, Ian; Kamble, Baburao; Robison, Clarence W.; Ozturk, Doruk

doi:10.1016/j.rse.2016.07.011

Cited by 48 publications

(20 citation statements)

References 21 publications

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“…2017, 9, 436 3 of 21 [27,28]. Regional scale ET mapping has also been done in the Texas High plains [29], in the mountainous areas of northern Portugal [30], and in the oasis area in Heibe River Basin in China [31], and the results showed reasonable accuracy.…”

Section: Study Areamentioning

confidence: 96%

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Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ET o ) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ET r ). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009-2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R 2 ) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R 2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009-2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (R n ), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management.

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Section: Study Areamentioning

confidence: 96%

“…METRIC estimates the near surface temperature gradient (dT) as an indexed function of radiometric surface temperature, and thus does not rely on accurate estimate of land surface temperature from thermal imagery [17,26,27]. It also uses hourly reference ET to auto-calibrate the sensible heat calculation for each satellite image.…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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“…TIR measurements across multiple bands (>4) ensure that land surface temperature and emissivity are retrieved to within 1 K accuracy (assuming a precision of 0.3 K); this allows ET estimates to be within 10% relative error from land surface temperature uncertainty [ Hook et al ., ; Blonquist et al ., ; Cammalleri et al ., ; Hulley et al ., ; Fisher et al ., ]. Measurements should be acquired at high spatial resolutions (10–100 m) and high temporal resolutions (daily, diurnal), as warranted above [ Allen et al ., ; Chen et al ., ; Allen et al ., ; Anderson et al ., ; Kilic et al ., ].…”

Section: Path Forwardmentioning

confidence: 99%

The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources

Fisher

Melton

Middleton

et al. 2017

Water Resources Research

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405

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The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space‐based perspective, necessary to advance them.

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“…A major goal of thermal remote sensing is the estimation of the true (kinetic) surface temperature due to its usefulness in diverse applications such as modeling energy balance [1][2][3][4][5][6] , vegetation conditions 7,8 , environmental stressors effects, and climate change [9][10][11] . The importance of thermal remote sensing in agriculture has been demonstrated at scales from global to regional and from farm to sub-plant, the latter scale being addressed by infrared temperature sensors 12 and microbolometer cameras 13,14 .…”

Section: Introductionmentioning

confidence: 99%

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

Torres‐Rua

Aboutalebi

Wright

et al. 2019

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV

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Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of highresolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99-0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health Index), and complex models such as energy balance-based models (e.g. evapotranspiration) could benefit from spatial estimates of surface emissivity for true or kinetic temperature mapping. In that regard, this work presents an analysis of the spectral characteristics of a microbolometer camera with regard to emissivity, along with a methodology to infer thermal emissivity spatially based on the spectral 1 *

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Sensitivity of evapotranspiration retrievals from the METRIC processing algorithm to improved radiometric resolution of Landsat 8 thermal data and to calibration bias in Landsat 7 and 8 surface temperature

Cited by 48 publications

References 21 publications

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

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