Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

Zakšek, Klemen; Schroedter-Homscheidt, Marion

doi:10.1016/j.isprsjprs.2009.02.006

Cited by 122 publications

(77 citation statements)

References 14 publications

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“…However, the aggregation did have a noticeable effect on the linear regression (R 2 = 0.84, RSME = 1.54 K), where the slope become flatter than the 1:1 line and the y-intercept became positive and increased by 1.6 °C compared to the daily MMM result. The RMS error reported here for daily and aggregated MMM method is similar to that reported elsewhere [29]. …”

Section: Resultssupporting

confidence: 88%

“…RMS errors ranged between 4.09 and 4.90 K (Table 2), which were approximately 1 to 2 K larger than typical when compared to air temperatures [29]. Variations in R 2 values did not correlate with distance of the interpolated diurnal curve form (Table 1) indicating factors other than spatial interpolation were playing a dominant role in correlation variability.…”

Section: Resultsmentioning

confidence: 91%

“…Steps have been taken to compare MODIS LST with much coarser spatial resolution passive microwave surface temperatures [27] and climate reanalysis products [28]. The typical range of errors when correlating LST to air temperature is approximately 2-3 °C [29] irrespective of the methodology, spatial or temporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

et al. 2014

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Spatially continuous satellite infrared temperature measurements are essential for understanding the consequences and drivers of change, at local and regional scales, especially in northern and alpine environments dominated by a complex cryosphere where in situ observations are scarce. We describe two methods for producing daily temperature fields using MODIS -clear-sky‖ day-time Land Surface Temperatures (LST). The Interpolated Curve Mean Daily Surface Temperature (ICM) method, interpolates single daytime Terra LST values to daily means using the coincident diurnal air temperature curves. The second method calculates daily mean LST from daily maximum and minimum LST (MMM) values from MODIS Aqua and Terra. These ICM and MMM models were compared to daily mean air temperatures recorded between April and October at seven locations in southwest Yukon, Canada, covering characteristic alpine land cover types (tundra, barren, glacier) 947 8-day LST averages, aggregated from the MMM method, to air temperature, we found a high correlation (R 2 = 0.84) with less variability (RMSE = 1.54 K). Where the trend was less steep and the y-intercept increased by 1.6 °C compared to the daily correlations. This effect is likely a consequence of LST temperature averages being differentially affected by cloud cover over warm and cold surfaces. We conclude that satellite infrared skin temperature (e.g., MODIS LST), which is often aggregated into multi-day composites to mitigate data reductions caused by cloud cover, changes in its relationship to air temperature depending on the period of aggregation.

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Section: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 91%

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Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

et al. 2014

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“…Zaksek and Schroedter-Homscheidt [2] proposed a model to obtain SAT that considered solar incidence parameters and the terrain curvature, with a term defined as the difference between station elevation and mean elevation within 20 km vicinity (∆h). We computed topographic aspects and slopes (φ t ,s), h, Lat, distance to coast (dist) as a measurement of continentally, and ∆h for each station from a 50-m digital elevation model (DEM) to analyze the SAT dependences on them.…”

Section: Geographical Datamentioning

confidence: 99%

“…The monitoring of SAT patterns can also improve meteorological model results by using them as input data. Additionally, extreme SATs have health consequences, increase electricity loads, and result in reduced crop yields [2]. SAT is usually measured by meteorological stations and thus SAT data are limited by the density and distribution of available station networks [3].…”

Section: Introductionmentioning

confidence: 99%

Land Surface Air Temperature Retrieval From EOS-MODIS Images

Niclòs

Valiente

Barberà

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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Abstract-The knowledge of the spatial and temporal patterns of Surface Air Temperature (SAT) is essential to monitor a region's climate and meteorology, quantify surface exchange processes, improve climatic and meteorological model results, and study health and economic impacts. This work analyzed correlations between SAT and geophysical land surface variables, Land Surface Temperature (LST) mainly, to establish operative techniques to obtain spatially-continuous land SAT maps from satellite data, unlike data provided by meteorological station networks. The correlations were analyzed by using EOS-MODIS images, meteorological station network data, and geographical variables. Linear regressions with MODIS-retrieved LST data gave SAT with uncertainties higher than ±2K during daytime and of ±1.8K at night-time. Nevertheless, SAT uncertainties decreased up to ±1.2K when other satellite-retrieved surface parameters, i.e. vegetation index and albedo, together with meteorological and geographical data were considered as terms of multivariable regressions. The equations finally proposed were shown to work properly for different land covers.

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An improved satellite‐based approach for estimating vapor pressure deficit from MODIS data

Zhang

Niu

et al. 2014

JGR Atmospheres

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Vapor pressure deficit (VPD) is an important variable widely used in ecosystem and climate models. In this paper, an improved satellite-based approach to estimating VPD was presented that uses several remote sensing products coupled with field measured data. The proposed method exploits an optimized algorithm to derive near-surface actual vapor pressure (e a ) from Moderate Resolution Imaging Spectroradiometer (MODIS) data and upgrades Smith's (1966) methodology for estimating e a . The proposed new algorithm for calculating e a was evaluated against in situ measurements at 119 validation sites in China for 2 months in 2013. The mean absolute error (MAE) and root-mean-square error (RMSE) were less than 0.25 kPa and 0.33 kPa, respectively. The near-surface air temperature (T a ), which is an important input data for calculating VPD, was estimated from satellite-retrieved land surface temperature, and had an RMSE of less than 2.5 K. The estimated VPD values were validated with ground observation data from the Heihe River Basin for 5 months in 2012 and for all of China for August 2013. A coefficient of determination (R 2 ) of 0.912, MAE of 0.27 kPa, and RMSE value of 0.32 kPa were achieved for the 2012 test data, and corresponding values of 0.88, 0.278 kPa, and 0.367 kPa for the 2013 test data. These results are promising, especially considering the comparatively high spatial resolution (1 km) of the VPD map estimated from the satellite data. Potential applications include global evapotranspiration estimation, fire warning, and vegetation analysis.

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Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

Cited by 122 publications

References 14 publications

Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

Land Surface Air Temperature Retrieval From EOS-MODIS Images

An improved satellite‐based approach for estimating vapor pressure deficit from MODIS data

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