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  • 1. Pareeth, Sajid
    et al.
    Delucchi, Luca
    Metz, Markus
    Rocchini, Duccio
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Raspaud, Martin
    SMHI, Core Services.
    Adrian, Rita
    Salmaso, Nico
    Neteler, Markus
    New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 3Article in journal (Refereed)
    Abstract [en]

    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.

  • 2. Pareeth, Sajid
    et al.
    Delucchi, Luca
    Metz, Markus
    Rocchini, Duccio
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Raspaud, Martin
    SMHI, Core Services.
    Adrian, Rita
    Salmaso, Nico
    Neteler, Markus
    New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 3, p. NIL_481-NIL_508Article in journal (Refereed)
    Abstract [en]

    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.

  • 3.
    Raspaud, Martin
    et al.
    SMHI, Core Services.
    Hoese, David
    Dybbroe, Adam
    SMHI, Core Services.
    Lahtinen, Panu
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Itkin, Mikhail
    Hamann, Ulrich
    Rasmussen, Lars Orum
    Nielsen, Esben Stigard
    Leppelt, Thomas
    Maul, Alexander
    Kliche, Christian
    Thorsteinsson, Hrobjartur
    PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data2018In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 99, no 7, p. 1329-1336Article in journal (Refereed)
  • 4.
    Scheirer, Ronald
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Raspaud, Martin
    SMHI, Core Services.
    A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 4, article id 560Article in journal (Refereed)
1 - 4 of 4
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