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Remote Sens. 2016, 8(3), 169; doi:10.3390/rs8030169

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

1
Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, Trento 38010, Italy
2
Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, Trento 38010, Italy
3
Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, Berlin 14195, Germany
4
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping 60176, Sweden
5
Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin 12587, Germany
6
Mundialis GmbH & Co. KG, Bonn 53227, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 31 October 2015 / Revised: 20 January 2016 / Accepted: 4 February 2016 / Published: 25 February 2016
View Full-Text   |   Download PDF [1890 KB, uploaded 25 February 2016]   |  

Abstract

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 R2 and RMSE of 0.97 and 0.91 K respectively. View Full-Text
Keywords: Remote sensing; AVHRR LAC; thermal calibration; LSWT; SIFT; brightness temperature; GRASS GIS; Pytroll; geo-rectification; image navigation Remote sensing; AVHRR LAC; thermal calibration; LSWT; SIFT; brightness temperature; GRASS GIS; Pytroll; geo-rectification; image navigation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pareeth, S.; Delucchi, L.; Metz, M.; Rocchini, D.; Devasthale, A.; Raspaud, M.; Adrian, R.; Salmaso, N.; Neteler, M. New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data. Remote Sens. 2016, 8, 169.

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