Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C0 to 32.59 C0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C0 to 32.59 C0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C0 to 5 C0

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Soil moisture, surface temperature, and vegetation are variables that play an important role in our environment which in turn increases the demand for accurate estimation of certain geophysical parameters such as weather, flooding, and land classification. However, for accurate Land Surface Temperature (LST) estimation, remotely sensed data of key environmental forms were considered and applied in this research. The goal of this study was to apply a suitable algorithm for LST estimation from the Landsat-8 dataset that gives a great accuracy when compared with in-situ observations.Spatial and temporal Landsat-8 data were acquired which provided the analytical structure for linking specific data successfully due to fine resolutions. The data were then applied to determine brightness temperatures, vegetation cover, and surface emissivity which demonstrated the effectiveness of the Split-Window Algorithm as an optimum method for LST retrieval from satellite.The results show temperature variation over a long period of time can be used in observing varying temperature values based on terrain i.e. High temperatures in fully built up areas and low temperatures in the well-vegetated regions. Finally, accurate LST estimation is important for land classification, energy budget estimations as well as agricultural production.Keywords: Emissivity, Landsat, Land Surface Temperature, Split-Window, Vegetatio

Estimación De La Temperatura De La Superficie Terrestre De La Ciudad De Srinagar, India Utilizando Datos De Landsat 8

Land surface tempreature (LST) is a critical parameter for the study of biosphere, cryosphere and climate change.. Thermal infrared remote sensing data can be used to measure Land Surface Temperature (LST). It will measure the energy exiting the Earth's surface and record the apparent temperature of the surface. It is now possible to measure LST due to the advent of satellite imagery and digital image processing applications. The LST for Srinagar city was calculated using the Split Window algorithm (SW) and Landsat-8 (Path-149 and Row-36) Thermal Infrared Sensor (TIRS) data with a resolution of 100m. . Emissivity was calculated using the Normalized Differential Vegetation Index (NDVI) proportion of vegetation methodology, with bands 4 and 5 (30 m resolution) from the Operational Land Imager (OLI). Surface temperatures were found to be higher in central  regions and lower in heavily vegetated areas. The LST derived using the SW algorithm was more efficient and precise since it used both OLI and TIRS bandsLa temperatura de la superficie terrestre (LST) es un parámetro crítico para el estudio de la biosfera, la criosfera y el cambio climático. Los datos de teledetección infrarroja térmica se pueden utilizar para medir la temperatura de la superficie terrestre (LST). Medirá la energía que sale de la superficie de la Tierra y registrará la temperatura aparente de la superficie. Ahora es posible medir LST debido a la llegada de imágenes de satélite y aplicaciones de procesamiento de imágenes digitales. El LST para la ciudad de Srinagar se calculó utilizando el algoritmo de ventana dividida (SW) y los datos del sensor infrarrojo térmico (TIRS) Landsat-8 (Path-149 y Row-36) con una resolución de 100 m. . La emisividad se calculó utilizando la metodología de proporción de vegetación del NDVI, con las bandas 4 y 5 (resolución de 30 m) del Operational Land Imager (OLI). Se encontró que las temperaturas de la superficie eran más altas en las regiones centrales y más bajas en las áreas densamente vegetadas. El LST derivado usando el algoritmo SW fue más eficiente y preciso ya que usó bandas OLI y TIRS

Evapotranspiration Retrieval Using S-SEBI Model with Landsat-8 Split-Window Land Surface Temperature Products over Two European Agricultural Crops

Crop evapotranspiration (ET) is a key variable within the global hydrological cycle to account for the irrigation scheduling, water budgeting, and planning of the water resources associated with irrigation in croplands. Remote sensing techniques provide geophysical information at a large spatial scale and over a relatively long time series, and even make possible the retrieval of ET at high spatiotemporal resolutions. The present short study analyzed the daily ET maps generated with the S-SEBI model, adapted to Landsat-8 retrieved land surface temperatures and broadband albedos, at two different crop sites for two consecutive years (2017-2018). Maps of land surface temperatures were determined using Landsat-8 Collection 2 data, after applying the split-window (SW) algorithm proposed for the operational SW product, which will be implemented in the future Collection 3. Preliminary results showed a good agreement with ground reference data for the main surface energy balance fluxes Rn and LE, and for daily ET values, with RMSEs around 50 W/m2 and 0.9 mm/d, respectively, and high correlation coefficient (R2 = 0.72-0.91). The acceptable uncertainties observed when comparing with local ground data were reaffirmed after the regional (spatial resolution of 9 km) comparison with reanalysis data obtained from ERA5-Land model, showing a StDev of 0.9 mm/d, RMSE = 1.1 mm/d, MAE = 0.9 mm/d, and MBE = −0.3 mm/d. This short communication tries to show some preliminary findings in the framework of the ongoing Tool4Extreme research project, in which one of the main objectives is the understanding and characterization of the hydrological cycle in the Mediterranean region, since it is key to improve the management of water resources in the context of climate change effects

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Determination of land surface temperature using Landsat 8 images: Comparative study of algorithms on the city of Granada

[EN] The use of satellite images has become, in recent decades, one of the most common ways to determine the Land Surface Temperature (LST). One of them is through the use of Landsat 8 images that requires the use of single-channel (MC) and two-channel (BC) algorithms. In this study, the LST of a medium-sized city, Granada (Spain) has been determined over a year by using five Landsat 8 algorithms that are subsequently compared with ambient temperatures. Few studies compare the data source with the seasonal variations of the same metropolis, which together with its geographical location, high pollution and the significant thermal variations it experiences make it a suitable place for the development of this research. As a result of the statistical analysis process, the regression coefficients R2, mean square error (RMSE), mean error bias (MBE) and standard deviation (SD) were obtained. The average results obtained reveal that the LST derived from the BC algorithms (1.0 °C) are the closest to the ambient temperatures in contrast to the MC (-5.6 °C), although important variations have been verified between the different zones of the city according to its coverage and seasonal periods. Therefore, it is concluded that the BC algorithms are the most suitable for recovering the LST of the city under study.[ES] El empleo de imágenes satelitales se ha convertido, en las últimas décadas, en una de las formas más habituales para determinar la Temperatura de la Superficie Terrestre (TST). Una de ellas es mediante el empleo de imágenes Landsat 8 que requiere del uso de algoritmos del tipo monocanal (MC) y bicanal (BC). En este estudio se ha determinado la TST de una ciudad de tamaño medio, Granada (España) a lo largo de un año mediante el empleo de cinco algoritmos Landsat 8 que posteriormente se comparan con las temperaturas ambientales. Pocos estudios comparan la fuente de datos con las variaciones estacio-temporales de una misma metrópolis lo que unido a su situación geográfica, alta contaminación y las importantes variaciones térmicas que experimenta la convierten en un lugar adecuado para el desarrollo de esta investigación. Como resultado del proceso de análisis estadístico se obtuvieron los coeficientes de regresión R2, el error medio cuadrático (RMSE), sesgo medio del error (MBE) y la desviación estándar (DE). Los resultados medios obtenidos revelan que las TST derivada de los algoritmos BC (1,0 °C) son las más próximas a las temperaturas ambientales en contraposición con los MC (-5,6 °C) aunque se han verificado importantes variaciones entre las distintas zonas de la urbe según su cobertura y los periodos estacionales. Por todo ello, se concluye que los algoritmos BC son los más adecuados para recuperar la TST de la urbe objeto de estudio.Hidalgo-García, D. (2021). 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Sea Surface Temperature Mapping at Medium Scale Using Landsat 8 -TIRS Satellite Image

The Sea Surface Temperature (SST) retrieval from satellites data has been available since 1980’s both temporally and spatially. Some researchers have established SST inversion models depending on the correlation between the TM/ETM+ TIR radiance and the in-situ data. The objective of this research is to evaluate the performance of Landsat 8-estimated SST from 4 existing algorithms: Planck, Mono-Window Algorithm (MWA), Syariz and Split Window Algorithm (SWA) algorithms on  4 different tested areas: Eastern Bali, Bangkalan, Bombana and Poteran waters. Algorithm of Syariz dan SWA produced acceptable accuracy on all tested area with the NMAE ranged at 0.2-19.6% and 3.4-9.9% for Syariz and SWA, respectively. However, MWA and Planck produced NMAE larger than 30% on Bali and Poteran waters. Following the successful of SWA algorithm, the same algorithm was developed using insitu data collected on Poteran waters. The estimated SST by the developed algorithm produced acceptable accuracies on all tested water areas with the NMAE ranged from 0.401% to 16.630%. It was indicated that   Syariz, SWA and the developed algorithms were applicable for SST retrieval on all tested water

Low Cost Radiometer Design for Landsat Surface Temperature Validation

Land Surface Temperature (ST) represents the radiative temperature of the Earth’s surface and is used as an input for hydrological, agricultural, and meteorological science applications. Due to the synoptic nature of satellite imaging systems, ST products derived from spaceborne platforms are invaluable for estimating ST at the local, regional, and global scale. Over the past two decades, an emphasis has been placed on the need to develop algorithms necessary to deliver accurate ST products to support the needs of science users. However, corresponding efforts to validate these products are hindered by the availability of quality ground based reference measurements. NOAA’s Surface Radiation Budget Network (SURFRAD) is commonly used to support ST-validation efforts, but SURFAD’s instrumentation is broadband (4-50 micrometer) and several of their sites lack spatial uniformity, which can lead to large ST calculation errors. To address the apparent deficiencies within existing validation networks, this work discusses a prototype instrument developed to provide ST estimates to support validation efforts for spaceborne thermal sensor products. Specifically, a prototype radiometer was designed, built, calibrated, and utilized to acquire ground reference data to validate ST product(s) derived from Landsat 8 imagery. Field based efforts indicate these radiometers demonstrate agreement to Landsat-derived ST products to within 1.37 K over grass targets. This is an improvement of over 2 K when comparing to the SURFRAD validation network. Additionally, the radiometers proposed in this research were designed to calculate the largest unknown variable used to create Landsat 8 derived ST products: the target emissivity. Algorithms have been developed with the purpose of using Landsat 8’s two thermal bands to calculate the ST of a given scene. One popular method is the split window algorithm, which uses at sensor apparent temperatures collected by band 10 and 11 of Landsat, along with atmospheric data to calculate the surface leaving temperatures. A key input into the split window algorithm is the emissivity of the target, which is currently calculated using data from another spaceborne sensor; the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data from EOS-1 (Terra). This emissivity calculation is not ideal because, like the ST calculation, the emissivity is also propagated through the atmosphere before being calculated. An ideal approach is to measure and calculate the emissivity close to the surface, thus eliminating any atmospheric compensation errors. To eliminate the reliance on ASTER data and calculate the emissivity of the target before atmospheric effects, four additional response bands in the 8 – 9 micrometer range are added to the radiometer resulting in a six band instrument capable of calculating the ST and emissivity of a ground target. Through a validation effort using a commercial Fourier Transform Infrared Spectrometer (FTIR) the six-band radiometer\u27s ability to calculate the emissivity of a target is in agreement to the FTIR derived emissivity values to within 0.025 for Landsat-like band 10 and 0.022 for Landsat-like band 11 over grass targets. More accurate target emissivity values can decrease the error in the split window ST calculation by as much as 2.4 K over grass targets. The proposed instruments in this research can provide a more accurate validation of the Landsat 8 ST product when comparing to current validation networks, and therefore more accurate target emissivity values. Combining the two capabilities of this proposed radiometer allows the delivery of trusted data to the scientific community for their use in multiple applications

Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)

Over the past decade, satellite imaging has become a habitual way to determine the land surface temperature (LST). One means entails the use of Landsat 8 images, for which mono window (MW), single channel (SC) and split window (SW) algorithms are needed. Knowing the precision and seasonal variability of the LST can improve urban climate alteration studies, which ultimately help make sustainable decisions in terms of the greater resilience of cities. In this study we determine the LST of a mid-sized city, Granada (Spain), applying six Landsat 8 algorithms that are validated using ambient temperatures. In addition to having a unique geographical location, this city has high pollution and high daily temperature variations, so that it is a very appropriate site for study. Altogether, 11 images with very low cloudiness were taken into account, distributed between November 2019 and October 2020. After data validation by means of R2 statistical analysis, the root mean square error (RMSE), mean bias error (MBE) and standard deviation (SD) were determined to obtain the coefficients of correlation. Panel data analysis is presented as a novel element with respect to the methods usually used. Results reveal that the SC algorithms prove more effective and reliable in determining the LST of the city studied here.ERDF (European Rural Development Fund)Ministry of Science and Innovation (State Research Agency) EQC2018-004702-