Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information

Coops, Nicholas C.; Duro, Dennis C.; Wulder, Michael A.; Han, Tong

doi:10.1080/01431160701294653

Cited by 46 publications

(28 citation statements)

References 7 publications

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“…In a different context, temporal interpolation has been performed for NDVI reconstruction using non-linear least squares fits of asymmetric Gaussian model functions [15]. Another temporal interpolation method estimates missing Aqua LST values from Terra LST values and the seasonal average between Aqua LST and Terra LST [16,17]. This method replaces some, but not all, missing values, and the resulting dataset may still not be gap-free.…”

Section: Previous Approachesmentioning

confidence: 99%

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

2014

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Abstract:Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.

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Section: Previous Approachesmentioning

confidence: 99%

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

2014

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“…It appears to be strongly correlated to the density of the canopy of various land covers [21]. Using a combination of these two parameters can provide insight into surface energy fluxes and vegetation cover changes at regional and global scales [6,7,11,20].…”

Section: Introductionmentioning

confidence: 99%

“…In tropical regions, EVI is more suitable than NDVI to study vegetation, as it has been shown to have improved sensitivity to high biomass through a de-coupling of the canopy background signal and a reduction in atmospheric influences [11][12][13][14][15][16]. LST is used to measure the heat energy flux from the Earth's surface [17][18][19][20]. It appears to be strongly correlated to the density of the canopy of various land covers [21].…”

Section: Introductionmentioning

confidence: 99%

MODIS EVI and LST Temporal Response for Discrimination of Tropical Land Covers

et al. 2015

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MODIS enhanced vegetation index (EVI) and land surface temperature (LST) are key indicators for monitoring vegetation cover changes in broad ecosystems. However, there has been little evaluation of these indices for detecting changes in a range of land covers in tropical regions. In this study, we investigated the characteristics and seasonal responses of LST and EVI for four different land covers in Lao tropical forests: native forest, rubber plantation, mixed wooded/cleared areas and agriculture. We calculated long-term averages of MODIS LST and EVI 16-day time series and compared their monthly transitions over the seven-year period from 2006 to 2012. We also tested whether these indices can be used to classify these four land covers. The findings demonstrate the complex interrelationship of LST and EVI and their monthly transitions for different land covers: they each showed distinctly different intra-annual LST and EVI variations. Native forests have the highest EVI, and the lowest LST throughout the year. In contrast, agricultural areas with little or no vegetation cover have the highest LST. The transition of LST/EVI for the land covers other than native forests showed marked seasonality. Linear discriminant analysis (LDA) showed that there was high overall accuracy of separation of land covers by these indices (86%). The encouraging results indicate that the combined use of MODIS LST and EVI holds promise for improving monitoring of changes in a Lao tropical forest. OPEN ACCESSRemote Sens. 2015, 7 6027

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“…Furthermore, as an input model in the studies of surface temperature spatio-temporal downscaling, ATC model has an effect on improvement of the accuracy, and alleviates the problem of spatial and temporal resolution effectively. Besides, the data reflecting the annual variability of LST, can also adjust the inconformity between the satellite transit time and the observation angles, and correct the problems about the generation of satellite LST products (e.g., the eight-day synthetic products from Terra and Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) sensor) without considering the missing observation data (Coops et al, 2007;Crosson et al, 2012).Therefore, it is particularly urgent to simulate the LST annual change.…”

Section: Introductionmentioning

confidence: 99%

“…Progresses have been made in the study of modeling diurnal temperature cycle (DTC) since the middle of the 20th century. And numerous methods have been proposed, including the physical methods (Dickinson et al, 1993), the quasi-physical (or thermal inertial based) methods (Sobrino and El Kharraz, 1999a, b;Watson, 2000;Sagalovich et al, 2002), the semi-physical (or semiempirical) methods (Schä dlich et al, 2001;Göttsche and Olesen, 2001;Göttsche and Olesen, 2009;Duan et al, 2013), and the statistical methods (Coops et al, 2007;Crosson et al, 2012). In recent years, considering the annual dynamic variation of the LST, the studies that aim at satellite LSTs with multi-time scales have gradually emerged (Fu and Weng, 2016;Quan et al, 2016;Huang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Modeling of Remotely Sensed Annual Land Surface Temperature Cycle

Zhi¹,

Zhan²,

Jiang³

2017

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

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ABSTRACT:Satellite thermal remote sensing provides access to acquire large-scale Land surface temperature (LST) data, but also generates missing and abnormal values resulting from non-clear-sky conditions. Given this limitation, Annual Temperature Cycle (ATC) model was employed to reconstruct the continuous daily LST data over a year. The original model ATC O used harmonic functions, but the dramatic changes of the real LST caused by the weather changes remained unclear due to the smooth sine curve. Using Aqua/MODIS LST products, NDVI and meteorological data, we proposed enhanced model ATC E based on ATC O to describe the fluctuation and compared their performances for the Yangtze River Delta region of China. The results demonstrated that, the overall root mean square errors (RMSEs) of the ATC E was lower than ATC O , and the improved accuracy of daytime was better than that of night, with the errors decreased by 0.64 K and 0.36 K, respectively. The improvements of accuracies varied with different land cover types: the forest, grassland and built-up areas improved larger than water. And the spatial heterogeneity was observed for performance of ATC model: the RMSEs of built-up area, forest and grassland were around 3.0 K in the daytime, while the water attained 2.27 K; at night, the accuracies of all types significantly increased to similar RMSEs level about 2 K. By comparing the differences between LSTs simulated by two models in different seasons, it was found that the differences were smaller in the spring and autumn, while larger in the summer and winter.

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Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information

Cited by 46 publications

References 7 publications

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

MODIS EVI and LST Temporal Response for Discrimination of Tropical Land Covers

Enhanced Modeling of Remotely Sensed Annual Land Surface Temperature Cycle

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