Retrieval of Lake Bulk and Skin Temperatures Using Along-Track Scanning Radiometer (ATSR-2) Data: A Case Study Using Lake Tahoe, California

Hook, Simon J.; Prata, Fred; Alley, Ronald E.; Abtahi, A.; Richards, Robert C.; Schladow, S. Geoffrey; Pálmarsson, Sveinn Ó.

doi:10.1175/1520-0426(2003)20<534:rolbas>2.0.co;2

Cited by 92 publications

(74 citation statements)

References 29 publications

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“…Though the validation with in-situ data show good agreement, it is important to note that the satellite sensor measures temperature of a sub-micron layer between water surface and air which is highly variable according to the meteorological conditions, whereas in-situ data represents bulk temperatures in the lake [47,48]. Due to the skin effect over the lake surface there is a considerable difference between the skin and bulk temperature which may also reflect the actual differences between satellite derived LSWT and the in-situ data [46,48]. The accuracy of the derived LSWT also depends upon the viewing zenith angle at which the observation is taken (nadir or wide angle) [43].…”

Section: Discussionmentioning

confidence: 91%

New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data

et al. 2016

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Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs-NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R 2 and RMSE of 0.97 and 0.91 K respectively.

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Section: Discussionmentioning

confidence: 91%

New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data

et al. 2016

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“…The suite of field sensors has been used to perform thermal calibration assessment of a number of sensors including MODIS and ASTER and therefore uses radiometers with a wide bandpass [15][16][17]. Because the radiometers are not filtered to match the Landsat spectral bandpass, the surface temperature, corrected for the cool skin effect, is computed using a combination of the observed radiometric temperature, the near surface contact temperature and the downwelling radiance computed from MODTRAN [14,15].…”

Section: Jpl Lake Tahoe and Salton Seamentioning

confidence: 99%

Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

et al. 2014

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Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring OPEN ACCESSRemote Sens. 2014, 6 11608 of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m 2 ·sr·μm or −2.1 K and −4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m 2 ·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required.

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“…Fig. 6a), especially during summer months and is mostly driven by the radiation budget and the meteorological conditions (Hook et al, 2003;Minnett et al, 2011). Periods with -dotted), the 95 % confidence interval of the regression line, the coefficient of determination as square of the correlation coefficient (R 2 ), the root-mean-square error (RMSE), the Bias as the mean temperature difference and standard deviation (∆T ± σ ∆T ) between MODIS and RT-lswt, and the number of coincident observations (within ±15 minutes).…”

Section: In Situ Datamentioning

confidence: 99%

Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

Riffler

Lieberherr

Wunderle

2015

Earth Syst. Sci. Data

120

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Abstract. Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellitebased lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km 2 and the resulting biases and RMSEs were found to be within the range of −0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderateresolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and −0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ∼ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at

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Retrieval of Lake Bulk and Skin Temperatures Using Along-Track Scanning Radiometer (ATSR-2) Data: A Case Study Using Lake Tahoe, California

Cited by 92 publications

References 29 publications

New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data

New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data

Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

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