Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains

Kerchove, Ruben Van De; Lhermitte, Stef; Veraverbeke, Sander; Goossens, Rudi

doi:10.1016/j.jag.2011.09.007

Cited by 71 publications

(39 citation statements)

References 74 publications

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“…Second, the correlation between day-time LST and elevation is greater in winter months (December to February) and lower in summer (July-August) for all land cover classes; the opposite is observed for night-time LST. Other authors pointed out the greater correlation between night-time LST and elevation with respect to day-time LST [Fu and Rich, 2002;Pouteau et al, 2011;Van De Kerchove et al, 2013], but our results add that this relation is not consistent throughout the year. The scatter plots also show the effect of the vegetation altitudinal distribution on day-time LST; starting in spring and with a maximum in July and August, points are more scattered around the regression line for lower regions where the variety of the land cover types is greater compared to higher areas (see histograms in Fig.…”

Section: Lst and Elevationcontrasting

confidence: 94%

“…The forest class has the lowest difference between day and night temperatures due to the cooling effect of forests [Van Leeuwen et al, 2011]; denser vegetation canopies prevent incoming radiation from reaching the surface and increasing the temperature; this effect also combines with a greater amount of evapotranspiration of dense canopies which has a cooling effect [Van Leeuwen et al, 2011]. Other authors reported that non forested areas experience a greater cooling effect at night than forested regions [Goulden et al, 2006;Van Leeuwen et al, 2011;Van De Kerchove et al, 2013] with greater surface temperatures when vegetation gets denser. Finally, the day-night difference shows the greatest values for cropland areas which have a high surface temperature during the day which decreases significantly during the night; this effect can be observed when compared to the non forest class.…”

Section: Lst and Elevationmentioning

confidence: 99%

“…Our work has developed in this research framework as preliminary analysis of the spatio-temporal variability of MODIS LST for investigating surface thermal anomalies at regional scale. Spatio-temporal variability of LST at the regional scale is driven by influencing factors such as vegetation cover characteristics (fractional cover, canopy structure, vegetation surface roughness, albedo, leaf conductance), surface thermal properties (soil type and moisture), topography, incoming solar radiation and meteorological conditions [Julien et al, 2006;Van de Kerchove et al, 2013;Veraverbeke et al, 2013]. Moreover, many of these controlling parameters interact thus creating a very complex interpretation scheme [Sandholt et al, 2002].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, many of these controlling parameters interact thus creating a very complex interpretation scheme [Sandholt et al, 2002]. The development of quantitative models that describe the relation between spatio-temporal variability of LST and the driving factor is therefore crucial although few studies have addressed this issue using remotely sensed data [Ge, 2010;Westermann et al, 2011;Van De Kerchove et al, 2013]. In this study we analyze day-time and night-time MODIS LST over Southern Italy as provided by the MOD11A2 product to investigate spatio-temporal variability of the surface temperature as a function of the land cover, topography (altitude) and potential solar radiation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

Stroppiana

Antoninetti

Brivio

2014

European Journal of Remote Sensing

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Land Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. Satellite data provide consistent, continuous and spatially distributed information on the Earth's surface conditions among which LST. Ten years of NASA-MODIS day-time and night-time 1 km LST data over Southern Italy have been analyzed to quantify the influence of factors such as topography and the land cover on LST spatio-temporal variations. Results show that topography significantly influence LST variability as a function of the land cover and to a different extent for day-time and night-time data. Moreover, the relation between LST and the influential factors varies with the season during the year. This study contributes to a further understanding of the complex relationship between the spatio-temporal variability of the surface thermal conditions and its driving factors highlighting how these relationships might change within the year.

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Section: Lst and Elevationcontrasting

confidence: 94%

Section: Lst and Elevationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

Stroppiana

Antoninetti

Brivio

2014

European Journal of Remote Sensing

View full text Add to dashboard Cite

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“…With the addition of geostationary sensors in recent years, the ability to utilise algorithms with a temporal component for land surface temperature (LST) determination has improved. Modelling of the DTC for LST from geostationary images has been undertaken using absolute descriptive models (Göttsche andOlesen, 2001, Jiang et al, 2006), Reproducing Kernel Hilbert Space models (van den Bergh et al, 2006, Udahemuka andBergh, 2008), Fourier analysis (van de Kerchove et al, 2013), Artificial Neural Networks (Voyant et al, 2014) and Kalman filtering (Masiello et al, 2013, van den Bergh et al, 2009). Utilisation of a single value decomposition (SVD) method, such as that proposed in (Black and Jepson, 1998), has been applied by (Udahemuka and Bergh, 2008) and extended by (Roberts and Wooster, 2014) as part of a robust matching algorithm with significant improvements in the ability to handle temperature anomalies in the observation data.…”

Section: Land Surface Temperature Determinationmentioning

confidence: 99%

Assessment of the Utility of the Advanced Himawari Imager to Detect Active Fire Over Australia

Hally¹,

Wallace²,

Reinke³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire seasonincluding many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.

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Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km resolution

et al. 2014

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Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500 × 500 km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) = ±2• C for areas densely covered with stations and between ± 2• C and ± 4• C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (> 1000 m) and in Antarctica with an RMSE around 6• C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation http://WorldClim.org repository) and to feed various global environmental models.

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Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains

Cited by 71 publications

References 74 publications

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation

Assessment of the Utility of the Advanced Himawari Imager to Detect Active Fire Over Australia

Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km resolution

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