Change search
Link to record
Permanent link

Direct link
BETA
Raspaud, Martin
Publications (4 of 4) Show all publications
Scheirer, R., Dybbroe, A. & Raspaud, M. (2018). A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects. Remote Sensing, 10(4), Article ID 560.
Open this publication in new window or tab >>A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects
2018 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 4, article id 560Article in journal (Refereed) Published
National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-4827 (URN)10.3390/rs10040560 (DOI)000435187500072 ()
Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-08-06
Raspaud, M., Hoese, D., Dybbroe, A., Lahtinen, P., Devasthale, A., Itkin, M., . . . Thorsteinsson, H. (2018). PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data. Bulletin of The American Meteorological Society - (BAMS), 99(7), 1329-1336
Open this publication in new window or tab >>PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data
Show others...
2018 (English)In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 99, no 7, p. 1329-1336Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4799 (URN)10.1175/BAMS-D-17-0277.1 (DOI)000439442900003 ()
Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-08-06Bibliographically approved
Pareeth, S., Delucchi, L., Metz, M., Rocchini, D., Devasthale, A., Raspaud, M., . . . Neteler, M. (2016). New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data. Remote Sensing, 8(3)
Open this publication in new window or tab >>New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data
Show others...
2016 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 3Article in journal (Refereed) Published
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.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-2869 (URN)10.3390/rs8030169 (DOI)
Available from: 2016-08-23 Created: 2016-08-23 Last updated: 2017-11-28Bibliographically approved
Pareeth, S., Delucchi, L., Metz, M., Rocchini, D., Devasthale, A., Raspaud, M., . . . Neteler, M. (2016). New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data. Remote Sensing, 8(3), NIL_481-NIL_508
Open this publication in new window or tab >>New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data
Show others...
2016 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 3, p. NIL_481-NIL_508Article in journal (Refereed) Published
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.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-2024 (URN)000373627400024 ()
Available from: 2016-05-03 Created: 2016-05-02 Last updated: 2017-11-30Bibliographically approved
Organisations

Search in DiVA

Show all publications
v. 2.35.8
|