Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values

Simó, Gemma; García-Santos, Vicente; Jiménez, María A.; Martínez‐Villagrasa, Daniel; Picos, R.; Caselles, Vicente; Cuxart, Joan

doi:10.3390/rs8100849

Cited by 25 publications

(17 citation statements)

References 45 publications

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“…Azeñas et al () have recently shown in a study for the same location that under sunny conditions vegetated areas are very efficient in removing water from the upper soil leading to very dry surfaces that may be hotter in the day and colder at night than the surrounding less vegetated areas. The spatial variability for an instant either at day or at night is in the range of 10 to 15 °C, as found by Simó et al (). Contrarily to T2m, for Tsoil the morning and evening transitions do not take place simultaneously at all poles, each surface reacting differently to the appearance and disappearance of the solar radiation.…”

Section: Spatial Variability Of Air and Soil Variablessupporting

confidence: 73%

“…Simó et al () recently analyzed the land surface temperature (LST) heterogeneities in the Campus using Landsat‐7 ETM data and found that the variability was in the range 5 to 15 °C depending on the season. These results allowed to determine locations in the Campus to deploy nine instrumented poles in different types of terrain representative of the surface variability of the 1‐km side study area.…”

Section: Site and Datamentioning

confidence: 99%

“…The situation between the coast and the Tramuntana range, when high-pressure systems prevail, makes the coupled sea-mountain breeze systems dominant at night (Cuxart et al, 2007) and day (Cuxart et al, 2014;Jiménez et al, 2016), otherwise westerlies prevail. Simó et al (2016) recently analyzed the land surface temperature (LST) heterogeneities in the Campus using Landsat-7 ETM data and found that the variability was in the range 5 to 15 • C depending on the season. These results allowed to determine locations in the Campus to deploy nine instrumented poles in different types of terrain representative of the surface variability of the 1-km side study area.…”

Section: Measurement Sitementioning

confidence: 99%

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Observed Atmospheric and Surface Variability on Heterogeneous Terrain at the Hectometer Scale and Related Advective Transports

et al. 2019

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The values of the atmospheric variables follow from mass, momentum, and energy conservation equations. These equations involve gradients which, in heterogeneous terrain, lead to a connection of the values at one point to the values in their nearby surroundings, due to turbulence mixing or advective local transports. In this work the observed variability of the air and soil variables is analyzed for a 1‐km2 surface in a semirural area for a number of weather stations separated typically 150 m. Data show a large variability at the hectometer scale, with several degrees of difference in temperature sustained in time between neighboring points. While in the daytime turbulence contributes to keep the differences moderate, in weak wind nights the spatial variability increases significantly. The variability of soil variables can be large depending on the soil moisture and the vegetation cover and is less sensitive to the diurnal cycle. The inspection of the vertical gradients of temperature and humidity reveals that their sign and intensity, and consequently the corresponding sensible and latent heat fluxes, differ depending on the measuring point. The thermal advection is obtained from the hectometer‐scale network, which is comparable in magnitude to the turbulent fluxes and to the imbalance of the surface energy budget for clear and calm nights. During the day, the advection term may explain part of the energy budget imbalance, particularly when it is computed on a 30‐min time scale. A similar method is applied to the water vapor, finding that in the very dry conditions of the experiment, moisture advection is significant and with values comparable to the evapotranspiration.

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Section: Spatial Variability Of Air and Soil Variablessupporting

confidence: 73%

Section: Site and Datamentioning

confidence: 99%

Section: Measurement Sitementioning

confidence: 99%

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Observed Atmospheric and Surface Variability on Heterogeneous Terrain at the Hectometer Scale and Related Advective Transports

et al. 2019

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“…Since more than 95% of the surface inside this square is wild grass vegetation, which was the same observed surface from the IR120 broadband radiometer, and a circular geolocation error uncertainty requirement of 12 m is assumed for both the reflective and emitted radiance data [3], it was presumed that both the IR120 and L8-TIRS LST estimations are comparable. Recent studies have also validated the LST estimated from several orbiting TIR sensors against data from this SEB station, concluding that with respect to in situ temperature field measurements, the LST obtained from Landsat 7-ETM + showed a RMSE of ±1.7-1.8 K and, for the Terra-ASTER, a RMSE of ±1.3 K [16,17].…”

Section: Study Sitementioning

confidence: 62%

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

et al. 2018

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After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

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“…Secondly, different resolutions between ground observation and satellite-derived data might introduce uncertainties in the correlation analysis between climate and vegetation indices at the site scale. Simó et al [74] indicated that there is a bias between in situ data and satellite-derived pixel average in a heterogeneous area. Thirdly, other factors may also influence ecosystems, such as CO 2 fertilization effects [9], nitrogen deposition and interference from human activities (e.g., land cover change) [75].…”

Section: Uncertainties and Perspectivesmentioning

confidence: 99%

Climate Extremes and Their Impacts on Interannual Vegetation Variabilities: A Case Study in Hubei Province of Central China

et al. 2018

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As the frequency and intensity of climate extremes are likely to be substantially modified in upcoming decades due to climate warming, an evaluation of the response of interannual vegetation variabilities to climate extremes is imperative. This study comprehensively analyzed the spatio-temporal variabilities of 21 temperature and precipitation indices across Hubei Province in Central China based on daily meteorological records for the period 1961-2015. To quantify the sensitivity of the vegetation to climate indices in the study area, we correlated climate indices with three vegetation indicators: leaf area index, normalized difference vegetation index, and gross primary productivity. The results indicated that warm-related indices exerted considerable increasing trends, especially for summer days at a rate of 0.35 days year −1 (p < 0.01). In addition, the trends of 18 indices during 1982-2015 were larger than those during 1961-2015, indicating accelerated climate changes in Hubei Province. Spatially, extreme precipitation showed increases in the eastern regions of the study area and decreases in the western regions. Correlation analyses revealed that warm anomalies of the Atlantic Multidecadal Oscillation resulted in extreme warm conditions and extreme precipitation in the study area. Stepwise linear regression analyses identified three temperature indices and three precipitation indices, which were mostly correlated with the three ecosystem variables at the site scale. Further multiple regressions demonstrated the main negative impacts caused by frost days, warm spell duration, extremely heavy precipitation, and consecutive dry days on the terrestrial ecosystem in Hubei Province. Our study provides an improved understanding of the effects of climate extremes on terrestrial ecosystems and can also offer a basis for the management of mitigating damage from climate extremes.

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Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values

Cited by 25 publications

References 45 publications

Observed Atmospheric and Surface Variability on Heterogeneous Terrain at the Hectometer Scale and Related Advective Transports

Observed Atmospheric and Surface Variability on Heterogeneous Terrain at the Hectometer Scale and Related Advective Transports

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

Climate Extremes and Their Impacts on Interannual Vegetation Variabilities: A Case Study in Hubei Province of Central China

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