Evapotranspiration and Surface Energy Fluxes Estimation Using the Landsat-7 Enhanced Thematic Mapper Plus Image over a Semiarid Agrosystem in the North-West of Algeria

Nehal, Laounia; Hamimed, Abderrahmane; Khaldi, A.; Souidi, Zahira; Zaagane, Mansour

doi:10.1590/0102-7786324016

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…EEFlux is a fully automated framework for METRIC that operates on the Google Earth Engine platform to derive RS-based ET estimates using the METRIC algorithm [52]. The application deploys Landsat's thermal and shortwave bands to derive the ET a from the albedo, vegetation index, land surface temperature, and other surface parameters [53]. The images in the METRIC-EEFlux were internally calibrated from the alfalfa ET o , using gridded weather data to retrieve the fraction of the ET o (ET o F).…”

Section: Actual Evapotranspiration From Metricmentioning

confidence: 99%

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

2023

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Rice culture is one of the most important crops in the world, being the most consumed cereal grain (755 million tons in 2020). Since rice is usually produced under flooding conditions and water performs several essential functions for the crop, estimating its water needs is essential. Remote sensing techniques have shown effectiveness in estimating and monitoring the water use in crop fields. An estimation from satellite data is a challenge, but could be very useful, in order to spatialize local estimates and operationalize production models. This study intended to derive an approach to estimate the actual crop evapotranspiration (ETa) in rice paddies from a temporal series of satellite images. The experimental data were obtained in the Lis Valley Irrigation District (central coast of Portugal), during the 2019 to 2021 rice growing seasons. The average seasonal ETa (FAO56) resulted 586 ± 23 mm and the water productivity (WP) was 0.47 ± 0.03 kg m−3. Good correlations were found between the crop coefficients (Kc) proposed by FAO and the NDVI evolution in the control rice fields, with R2 ranging between 0.71 and 0.82 for stages II+III (development + middle) and between 0.76 and 0.82 for stage IV (late). The results from the derived RS-assisted method were compared to the ETa values obtained from the surface energy balance model METRIC, showing an average estimation error of ±0.8 mm d−1, with a negligible bias. The findings in this work are promising and show the potential of the RS-assisted method for monitoring ETa and water productivity, capturing the local and seasonal variability in rice growing, and then predicting the rice yield, being a useful and free tool available to farmers.

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Section: Actual Evapotranspiration From Metricmentioning

confidence: 99%

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

2023

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“…5). The impact of soil heat flow G on available energy (R n -G) remains low, which means that model inaccuracies do not have an impact on energy balance modelling and everything on the latent heat flux (LE) [NEHAL et al 2017].…”

Section: Soil Heat Fluxmentioning

confidence: 99%

Journal of Water and Land Development

Oualid,

Hamimed,

Khaldi

2022

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Optimal estimation of water balance components at the local and regional scales is essential for many applications such as integrated water resources management, hydrogeological modelling and irrigation scheduling. Evapotranspiration is a very important component of the hydrological cycle at the soil surface, particularly in arid and semi-arid lands. Mapping evapotranspiration at high resolution with internalised calibration (METRIC), trapezoid interpolation model (TIM), two-source energy balance (TSEB), and soil-plant-atmosphere and remote sensing evapotranspiration (SPARSE) models were applied using Landsat 8 images for four dates during 2014-2015 and meteorological data. Surface energy maps were then generated. Latent heat flux estimated by four models was then compared and evaluated with those measured by applying the method of Bowen ratio for the various days. In warm periods with high water stress differences and with important surface temperature differences, METRIC proves to be the most robust with the root-mean-square error (RMSE) less than 40 W•m -2 . However, during the periods with no significant surface temperature and soil humidity differences, SPARSE model is superior with the RMSE of 35 W•m -2 . The results of TIM are close to METRIC, since both models are sensitive to the difference in surface temperature. However, SPARSE remains reliable with the RMSE of 55 W•m -2 unlike TSEB, which has a large deviation from the other models. On the other hand, during the days when the temperature difference is small, SPARSE and TSEB are superior, with a clear advantage of SPARSE serial version, where temperature differences are less important.

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“…The Earth engine evapotranspiration flux (EEFlux) model operates on the Google Earth Engine platform to derive RS-based ET estimates using the METRIC algorithm [49]. The application deploys Landsat's thermal and shortwave bands to derive actual ET (ET a ) from albedo, vegetation, and other surface parameters [50,51]. Images in METRIC-EEFlux were internally calibrated from the alfalfa reference ET (ET o ) using gridded weather data to retrieve the fraction of ET o (ET o F).…”

Section: Mapping Evapotranspiration At High Resolution With Internalimentioning

confidence: 99%

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Nisa

Khan

Govind³

et al. 2021

Agronomy

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Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r2 = 0.59SEBS, RMSE = 0.71 mm day−1; r2 = 0.65METRIC, RMSE = 1.13 mm day−1) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r2 = 0.08QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems.

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Evapotranspiration and Surface Energy Fluxes Estimation Using the Landsat-7 Enhanced Thematic Mapper Plus Image over a Semiarid Agrosystem in the North-West of Algeria

Cited by 6 publications

References 33 publications

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

Journal of Water and Land Development

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

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