Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in  Saudi Arabia using Landsat-8 images

Madugundu, Rangaswamy; Al‐Gaadi, Khalid A.; Tola, ElKamil; Hassaballa, Abdalhaleem A.; Patil, V. C.

doi:10.5194/hess-21-6135-2017

Cited by 49 publications

(38 citation statements)

References 59 publications

(65 reference statements)

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“…Moorhead and Gowda have similar unpublished reports for METRIC hourly comparisons. The NSE values of 0.65 and 0.76 for the dryland and irrigated surface temperatures, respectively, are viewed as a good match [27,31,43].…”

Section: Discussionmentioning

confidence: 99%

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Landsat Hourly Evapotranspiration Flux Assessment using Lysimeters for the Texas High Plains

Hashem

Engel

Bralts

et al. 2020

Water

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Evapotranspiration (ET) is one of the biggest data gaps in water management due to limited ET measurements, and further, spatial variability in ET is difficult to capture. Satellite-based ET estimation has great potential for water resources planning as it allows estimation of agricultural water use at field, landscape, and watershed scales. However, uncertainties with satellite data derived ET are a major concern. This study evaluates hourly satellite-based ET from 2001–2010 for the growing season (May–October) under irrigated and dryland conditions for both tall and short crops. The evaluation was conducted using observed ET from four large weighing lysimeters at the United States Department of Agriculture Agricultural Research Service (USDA-ARS) Conservation and Production Research Laboratory in Bushland, Texas. Hourly ET from satellite data were derived using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model. Performance statistics showed that satellite-based hourly estimates compared to lysimeter measurements provided good performance with an root-mean-square error(RMSE) of 0.14 mm, Nash–Sutcliffe efficiency (NSE) of 0.57, and R2 of 0.62 for ET for dryland crops, and RMSE of 0.16, NSE of 0.63, and R2 of 0.65 for irrigated crops. METRIC provided accurate hourly ET estimates that may be useful for irrigation scheduling and other water resources management purposes based on the hourly assessment.

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Section: Discussionmentioning

confidence: 99%

“…The Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model is a satellite image processing model that is used for ET estimation as a residual of the energy balance for various short crops, tall crops, trees, and forest [24][25][26][27][28][29][30][31][32] Equation 1.…”

Section: Image Analysismentioning

confidence: 99%

Landsat Hourly Evapotranspiration Flux Assessment using Lysimeters for the Texas High Plains

Hashem

Engel

Bralts

et al. 2020

Water

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“…Alfalfa and corn were the predominant crops under pivot (~800 m in diameter) irrigation during this period. While the corn cultivated at the farm is characterized by two distinct growing seasons, with planting dates in the spring and the fall, alfalfa is grown continuously as a multicut crop with a 30-40 day harvesting frequency [34]. During this time, the average daytime air temperature high varied from~45 • C during the summer months to~35 • C in October.…”

Section: Methodsmentioning

confidence: 99%

Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data

2018

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Constellations of CubeSats are emerging as a novel observational resource with the potential to overcome the spatiotemporal constraints of conventional single-sensor satellite missions. With a constellation of more than 170 active CubeSats, Planet has realized daily global imaging in the RGB and near-infrared (NIR) at~3 m resolution. While superior in terms of spatiotemporal resolution, the radiometric quality is not equivalent to that of larger conventional satellites. Variations in orbital configuration and sensor-specific spectral response functions represent an additional limitation. Here, we exploit a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) to optimize the utility and quality of very high-resolution CubeSat imaging. CESTEM represents a multipurpose data-driven scheme for radiometric normalization, phenology reconstruction, and spatiotemporal enhancement of biophysical properties via synergistic use of CubeSat, Landsat 8, and MODIS observations. Phenological reconstruction, based on original CubeSat Normalized Difference Vegetation Index (NDVI) data derived from top of atmosphere or surface reflectances, is shown to be susceptible to large uncertainties. In comparison, a CESTEM-corrected NDVI time series is able to clearly resolve several consecutive multicut alfalfa growing seasons over a six-month period, in addition to providing precise timing of key phenological transitions. CESTEM adopts a random forest machine-learning approach for producing Landsat-consistent leaf area index (LAI) at the CubeSat scale with a relative mean absolute difference on the order of 4-6%. The CubeSat-based LAI estimates highlight the spatial resolution advantage and capability to provide temporally consistent and time-critical insights into within-field vegetation dynamics, the rate of vegetation green-up, and the timing of harvesting events that are otherwise missed by 8-to 16-day Landsat imagery.

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“…Evapotranspiration (ET) is a crucial water flux for drought monitoring (Bhattarai et al, 2019;Gerhards et al, 2019;Mallick et al, 2014Mallick et al, , 2016Mallick et al, 2018), water resource management (Madugundu et al, 2017;Tasumi, 2019) and climate simulation (Littell et al, 2016;Molden et al, 2010) in the semi-arid ecosystems. A precise estimate of ET determines the crop water requirements, which subse-B.…”

Section: Introductionmentioning

confidence: 99%

An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

Hssaine

Merlin

Ezzahar

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Thermal-based two-source energy balance modeling is essential to estimate the land evapotranspiration (ET) in a wide range of spatial and temporal scales. However, the use of thermal-derived land surface temperature (LST) is not sufficient to simultaneously constrain both soil and vegetation flux components. Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by Ait Hssaine et al. (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and fc data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations. The approach is applied during a 4-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco. The field used was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural seasons, while it remained unploughed (as bare soil) during the 2015–2016 (B1) agricultural season. The classical TSEB model, which is driven only by LST and fc data, significantly overestimates latent heat fluxes (LE) and underestimates sensible heat fluxes (H) for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W m−2 for LE and −104, −71, −128 and −181 W m−2 for H, for S1, S2, S3 and B1, respectively. Meanwhile, when using TSEB-SM (SM and LST combined data), these errors are significantly reduced, resulting in mean bias values estimated as 39, 4, 7 and 62 W m−2 for LE and −10, 24, 7, and −59 W m−2 for H, for S1, S2, S3 and B1, respectively. Consequently, this finding confirms again the robustness of the TSEB-SM in estimating latent/sensible heat fluxes at a large scale by using readily available satellite data. In addition, the TSEB-SM approach has the original feature to allow for calibration of its main parameters (soil resistance and Priestley–Taylor coefficient) from satellite data uniquely, without relying either on in situ measurements or on a priori parameter values.

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Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images

Cited by 49 publications

References 59 publications

Landsat Hourly Evapotranspiration Flux Assessment using Lysimeters for the Texas High Plains

Landsat Hourly Evapotranspiration Flux Assessment using Lysimeters for the Texas High Plains

Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data

An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

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