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A comparative study in quantifcation of maize evapotranspiration for Iranian maize farm using SEBAL and METRIC-1 EEFLux algorithms

Kamyab Abolfazl Delavari, Mokhtari Shahroo, Jafarinia Reza

Acta Geophysica

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2022

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Vol. 70, no 1

319--332

EN

Evapotranspiration (ET) is one of the key components of the hydrological cycle, and its accurate estimation is very important in agricultural usages. In this study, actual daily ET (ETa) from the Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration/Earth Engine Evapotranspiration Flux (EEFLux) algorithms were used to compare the relative performance of the algorithms for the Landsat 8 images during the maize growth period. The results indicated that ETa was low at the beginning of the growing season and then came up to the middle of the growing season and then decreased due to decreasing temperature as well as changes in maize cover. The EEFLux algorithm has estimated about 7.71% of daily ET more than the SEBAL algorithm at the Arak maize farm. The results of performance evaluation showed that root mean squared error (RMSE), Nash–Sutclife coefficient of efficiency (NSE), percent bias error (PBIAS), and coefficient of determination (R2 ) criteria were obtained 0.711, 0.807, 7.398, and 0.885, respectively, based on the EEFLux algorithm and for SEBAL algorithm were equal to 1.046, 0.582, 15.080, and 0.793, respectively. According to the Taylor diagrams and observed data (lysimeter data), the EEFLux algorithm was closer to measured ETa values and had a higher correlation and a less standard deviation than the SEBAL algorithm. Therefore, the EEFLux algorithm had better estimation than the SEBAL algorithm.

2

When the heat is on: urbanization and land surface temperature in Guwahati, India

Chetia Sourav, Saikia Anup, Basumatary Manjil, Sahariah Dhrubajyoti

Acta Geophysica

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2020

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Vol. 68, no. 3

891--901

EN

The study examines the efects of urbanization on land surface temperature (LST) in Guwahati, a city in India using satellite data. Landsat images were utilized for LST retrieval, land-use land-cover (LULC) classifcation and the normalized diference built-up index mapping. Surface Energy Balance Algorithms for Land and support vector machine methods were used in the study. Results showed that the city has gone through massive changes in its LULC pattern with a high degree of urbanization during the period 1992–2015. The built-up area (BUA) increased to 87.8 km2 in 2015 from 11.6 km2 in 1992 while vegetation decreased from 143.3 to 76.6 km2 . Open spaces and water bodies decreased from 14.5 to 5 km2 and to 6.6 km2 from 6.7 km2 , respectively. Conversely, an increasing trend of LST was observed. The mean LST which was 18.5 °C in 1992 rose to 29.03 °C in 2015. Linear regression used in quantifying the relationship between urbanization and LST showed a positive relationship between LST and BUAs in the city.

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Spatiotemporal evolution of land surface temperature of Lake Oubeira catchment, northeastern Algeria

Rezzag Bara Chouaib, Djidel Mohamed, Medjani Fethi, Labar Sofiane

Journal of Water and Land Development

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2019

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no. 43

151--157

EN

The difficulties of access and detailed measurements of land surface temperature (LST) and water surface temperature (WST) especially in wetlands made the use of remote sensing data as one of the sources and techniques to estimate many climate elements including surface temperature and surface emissivity (ɛ). This study aims to estimate the surface temperature of the wetland of Lake Oubeira located in northeastern Algeria and their spatiotemporal evolution in both land and water. Landsat OLI-TIRS images in two dates (April and September 2016) obtained from the USGS have been used in this work, and forms the basis of a series of operations to obtain the final LST: development of the normalized difference vegetation index (NDVI), conversion of the digital number (DN) of the thermal infrared band (TIR) into spectral radiance as well as the calculation of the effective luminosity temperature of the sensor from the spectral radiation and surface emissivity (ɛ). The results show that the LST varies in space and time (from 16 to 31°C in April and from 24 to 41°C in September). This implies that the absorption of the equilibrium temperature at land cover depends on the optical properties of the surface, which are essentially determined by its water content, colour and morphology. At the same time, the water surface is the lowest land cover temperature, which also has a spatial variation (from 19 to 25°C in April and from 26 to 34.5°C in September) induced by atmospheric temperature, wind direction and speed and the depth of the lake.

PL

Trudności w dostępie do badanych obszarów i w wykonaniu szczegółowych pomiarów temperatury powierzchni ziemi (LST) i wody (WST), szczególnie w środowiskach podmokłych, sprawiają, że dane pozyskane metodą teledetekcji są źródłem informacji do szacowania elementów klimatu takich jak temperatura powierzchni i emisyjność (ɛ). Celem przedstawionych badań było określenie temperatury powierzchni siedlisk podmokłych jeziora Oubeira w północnowschodniej Algierii i jej zmian zarówno na ziemi, jak i w wodzie. W pracy wykorzystano obrazy Landsat OLI-TIRS z dwóch terminów (kwiecień i wrzesień 2016 r.) uzyskane z United States Geological Survey (USGS). Stanowiły one podstawę działań zmierzających do uzyskania końcowej temperatury LST – uzyskanie znormalizowanego różnicowego wskaźnika wegetacji (NDVI), konwersję numeru DN termicznego pasma podczerwieni (TIR) do spektralnej radiancji oraz obliczenie efektywnej temperatury barwowej sensora ze spektralnego promieniowania i emisyjności powierzchniowej (ɛ). Wyniki wskazują, że LST zmienia się w czasie i w przestrzeni (od 16 do 31°C w kwietniu i od 24 do 41°C we wrześniu). Oznacza to, że absorpcja temperatury przez pokrycie powierzchni ziemi zależy od optycznych właściwości powierzchni, które są określone przez zawartość wody, barwę i morfologię. Najniższą temperaturę wykazuje powierzchnia wody. Temperatura wody także ulega zmianom (od 19 do 25°C w kwietniu i od 26 do 34.5°C we wrześniu), wywołanym przez temperaturę powietrza, kierunek i prędkość wiatru oraz głębokość jeziora.