Estimation and partitioning of actual daily evapotranspiration at an intensive olive grove using the STSEB model based on remote sensing

Häusler, Melanie; Conceição, Nuno; Tezza, Luca; Sánchez, Juan Manuel; Campagnolo, Manuel L.; Häusler, Andreas J.; Silva, João M. N.; Warneke, Thorsten; Heygster, Georg; Ferreira, Maria Isabel

doi:10.1016/j.agwat.2018.01.027

Cited by 19 publications

(6 citation statements)

References 51 publications

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“…Note the term FVC in this Equation ( 37) is a function of LAI and sensor viewing angle [44]. The original TSEB does not consider the weighting by the emissivities of each component, but some authors have claimed for its importance [132,159,160].…”

Section: Dual (Two)-source Energy Balance (Tseb) Modelmentioning

confidence: 99%

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

2022

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Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET) using remote sensing techniques was first considered. Numerous ET models have been proposed, validated and improved along these five decades, as the satellites and sensors onboard were enhanced. This study reviews most of the efforts in the progress towards providing a trustworthy value of ET by means of thermal remote sensing data. It starts with an in-depth reflection of the surface energy balance concept and of each of its terms, followed by the description of the approaches taken by remote sensing models to estimate ET from it in the last thirty years. This work also includes a chronological review of the modifications suggested by several researchers, as well as representative validations studies of such ET models. Present limitations of ET estimated with remote sensors onboard orbiting satellites, as well as at surface level, are raised. Current trends to face such limitations and a future perspective of the discipline are also exposed, for the reader’s inspiration.

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Section: Dual (Two)-source Energy Balance (Tseb) Modelmentioning

confidence: 99%

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

2022

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“…180 W m -2(Nassar et al, 2021); H un valor de 50 a 400 W m -2 (Acharya y Sharma, 2021) y LE de 0 a 650 W m -2(Nassar et al, 2021;Ortega- Farías et al, 2016). Sin embargoHäusler et al (2018) yNassar et al (2021), dan a conocer que los componentes de balance de energía alcanzan sus mayores valores al medio día, además, que los valores pueden variar de forma horaria en los meses de las estaciones del año (verano, primavera, otoño e invierno) y obteniendo mayores valores en la estación de verano para los cultivos, especialmente en el Olivo. En la Tabla 3, se muestra el promedio, la desviación estándar y la variación para la estación de invierno 2019 y verano 2020 del cultivo del Olivo con un incremento de 18, 56 y 71% para Rn, G y H respectivamente, no obstante.…”

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Estimación de la evapotranspiración con fines de riego en tiempo real de un olivar a partir de imágenes de un drone en zonas áridas, caso La Yarada, Tacna, Perú

Machaca-Pillaca

Pino-Vargas

Fernández

et al. 2022

Idesia

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En el presente estudio, se usa técnicas de teledetección mediante un Remotely Piloted Aircraft System (RPAS) para la estimación de la evapotranspiración del cultivo (ETc) en el olivo (Olea europaea L.) en la costa sur del Perú. El experimento tuvo como objetivo estimar la evapotranspiración del cultivo de olivo mediante el modelo METRIC, a partir de imágenes multiespectrales y térmicas tomadas desde un RPAS, previa calibración vicaria con diferentes tipos de cuerpos en campo y utilizando espectroradiómetro y radiómetro portátil. Se obtuvo valores medios para ETc entre 2,86 y 2,94 mm d -1 , 4,02 mm d -1 en invierno (floración) y 4,53 mm d -1 en verano (fructificación); y valores de ETrF, en el rango de 0,88 a 1,67 y 0,92 a 1,42 mm d -1 para invierno y verano respectivamente, habiendo diferencias significativas por estación. Los valores por encima de 1,0, con mayor variabilidad y ligeramente más altos en invierno, debido a un manejo de riego diario durante todo el año, para evitar tensiones abióticas debido a la salinidad del suelo.

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“…According to El-Shirbeny and Abdellatif (2017), Häusler et al (2018), Xiang et al (2020), and Abdelkhaliket al (2020, the ETo is calculated using on-site meteorological data and multiplied by the crop coefficient (K c ) to estimate crop evapotranspiration (ETc), which is then multiplied by the stress coefficient (K s ) to obtain actual evapotranspiration (ETa).…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration and Vegetation Cover Classifications Maps Based on Cloud Computing at the Arab Countries Scale

El-Shirbeny

Biradar²,

Amer³

et al. 2022

Earth Syst Environ

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According to the most recent Köppen–Geiger classification, Arab countries are divided into seven climate classes. Ground data availability is limited in developing countries, and ground meteorological data are scarce and concentrated in a few locations, rather than station maintenance capability being adequate for the responsibilities. The current study uses remote sensing and meteorological data to create regional classification maps of reference evapotranspiration (ETo), potential crop evapotranspiration, and vegetation cover in Arab countries from 2005 to 2020. The Stand-alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) was used to estimate ETo using satellite data from 2005 to 2020. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite data and used in the SARE model, in addition to elevation (E), Julian day (J), and Latitude (Lat). To validate the SARE model results, the FAO-Penman–Monteith model was applied to 35 ground meteorological stations distributed across Arab countries to cover all climate classes based on the most recent Köppen–Geiger climate classification. Google Earth Engine was used to create the classification. The statistical indices produced acceptable results, with average RMSE values ranging from 6.9 to 17.3 (mm/month), while correlation coefficient (r) and index of agreement (d) values are more significant than 0.9. To be included in the ETc calculation, the crop coefficient (Kc) was calculated using NDVI 250 m spatial resolution. The density of the vegetation cover is used to classify it (low to high). The average vegetation cover was calculated to be greater than 31.5 Mha. The minimum vegetation cover was 14.9 Mha, and the maximum vegetation cover was 49.2 Mha. 15.8 Mha can be cultivated without supplementary irrigation for at least one agricultural season, according to the rainfall classification map.

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Estimation and partitioning of actual daily evapotranspiration at an intensive olive grove using the STSEB model based on remote sensing

Cited by 19 publications

References 51 publications

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

Estimación de la evapotranspiración con fines de riego en tiempo real de un olivar a partir de imágenes de un drone en zonas áridas, caso La Yarada, Tacna, Perú

Evapotranspiration and Vegetation Cover Classifications Maps Based on Cloud Computing at the Arab Countries Scale

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