Impact of AVHRR filter functions on surface temperature estimation from the split window approach

Czajkowski, Kevin; Goward, Samual N.; Ouaidrari, H.

doi:10.1080/014311698215126

Cited by 23 publications

(12 citation statements)

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“…These coefficients account for effects such as those due to the atmosphere and air-sea interactions, improper sensor calibration, and possible contamination in the optical system (Yokoyama and Tanba, 1991). Since the different filter functions of the five successive AVHRR instruments may introduce errors into the application of the split-window equation (Czajkowski et al, 1998), only scenes recorded from the NOAA-14 platform have been included in this algorithm devel- (Wooster et al, 2001) when compared to the influence of seasonal variations and therefore the correction was neglected. Since no coefficients for the split-window technique have so far been determined for the Lake Constance region with its specific atmospheric characteristics, different algorithms were applied to the dataset and compared (Table 1, Fig.…”

Section: Application Of the Split-window Techniquementioning

confidence: 99%

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

Thiemann

Schiller²

2003

International Journal of Applied Earth Observation and Geoinfor

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The spatial and temporal variability of the bulk temperature gives important insights into biological and hydrodynamic processes. However, standard algorithms for satellite data only provide information of the surface temperature. The comparison of current and new split-window coefficients applied on NOAA-14/AVHRR brightness temperatures of Lake Constance showed that a regional adaption was most promising. To derive the bulk temperature information, a priori progression from a weather station was included into the AVHRR analysis. Among the weather is data, the mean temperature of the three preceding days and the day of the year were the most relevant additional information. By a multiple regression approach the bulk temperature in the upper 4 m of Lake Constance could be determined with an accuracy of ±1.20 • C. The training of a neural network improved the determination of the bulk temperature to ±1.04 • C.An extended field campaign demonstrated that the algorithm is also applicable to other sensors with the same spectral band settings (in this case NOAA-16/AVHRR) with an acceptable error and that it is equally accurate over the entire lake.

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Section: Application Of the Split-window Techniquementioning

confidence: 99%

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

Thiemann

Schiller²

2003

International Journal of Applied Earth Observation and Geoinfor

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“…We also examined the sensitivity of modeled NPP to key driving variables. Analyses of the surface environmental variable recoveries (T•, D, and CSI) are reported elsewhere [e.g., Czajkowski et al, 1997b;Prince et al, 1998].…”

Section: Resultsmentioning

confidence: 99%

“…shown to be within 2øC rms error of field measurements at a diverse range of study sites[Czajkowski et al, 1997b; Prihodko and Goward, 1997; Prince et al, 1998]. Deviations of this magnitude may result in significant errors in energy balance calculations but the effect on plant growth is relatively smaller.…”

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confidence: 94%

Mapping net primary production and related biophysical variables with remote sensing: Application to the BOREAS region

Goetz¹,

Prince²,

Goward³

et al. 1999

J. Geophys. Res.

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Abstract. Maps of net and gross primary production, autotrophic respiration, biomass, and other biophysical variables were generated for 106 km 2 of boreal forest in central Canada (the Boreal Ecosystem-Atmosphere (BOREAS) region) using a production efficiency model (PEM) driven with remotely sensed observations at 1 km spatial resolution. The PEM was based on carbon yields of absorbed photosynthetically active radiation for both gross and net primary production (GPP and NPP), accounting for environmental stress and autotrophic respiration (Ra). Physiological control was modeled using remotely sensed maps of air temperature, vapor pressure deficit, and soil moisture. The accuracy of the inferred variables was generally within 10-30% of point measurements at the surface and independent model results (both at the stand level). Biomass maps were derived from visible reflectance measurements and were also compared to independently

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“…GOES observations offer the possibility of surface radiometric temperature observations at a higher temporal frequency (hourly as opposed to daily) and finer spatial resolution (2‐km as opposed to 1°). Following Czajkowski et al [1998], surface temperature retrieval from GOES channel 4 and 5 observations were based on the split‐window approach described in Rhoads et al [2001]. GOES radiometric surface temperature products were taken from 1/8° degree resolution data generated for the North American Land Data Assimilation System (NLDAS).…”

Section: Methodsmentioning

confidence: 99%

Multiobjective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals

2003

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[1] Physically based models of surface water and energy balance processes typically require a large number of soil and vegetation parameters as inputs. Accurate specification of these parameters is often difficult without resorting to calibration of model predictions against independent observations. Along with streamflow observations from gauging stations, spaceborne surface radiometric temperature retrievals offer the only independent observation of land surface model output commonly available at regional spatial scales (i.e., >50 2 km 2 ). This analysis examines the potential benefits of incorporating spaceborne radiometric surface temperature retrievals and streamflow observations in a multiobjective calibration framework to accurately constrain regional-scale model evapotranspiration predictions. Results for the VIC (Variable Infiltration Capacity) model over the Southern Great Plains of the United States suggest that multiobjective model calibration against radiometric skin temperatures and steamflow observations can reduce error in model monthly evapotranspiration predictions by up to 20% relative to single-objective model calibration against streamflow alone.

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Impact of AVHRR filter functions on surface temperature estimation from the split window approach

Cited by 23 publications

References 0 publications

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

Determination of the bulk temperature from NOAA/AVHRR satellite data in a midlatitude lake

Mapping net primary production and related biophysical variables with remote sensing: Application to the BOREAS region

Multiobjective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals

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