New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data
Abstract
:1. Introduction
2. Methods
2.1. Data and Software
2.2. Pre-Processing
2.3. Precise Geo-Rectification Using SIFT
2.4. Validation of Thermal Calibration by Estimating LSWT
3. Results
3.1. Time Series of Calibrated AVHRR LAC Data
3.2. Geo-Rectification of Calibrated AVHRR LAC Data Using SIFT
3.3. Quality Assessment of the Time Series Using Estimated LSWT
3.4. Software Code for Deploying the Method Elsewhere
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
Appendix
A. Python Script to Read and Calibrate Level 1B AVHRR LAC Data Using Pytroll Libraries
B. Bash Script to Process the Level 1B AVHRR LAC Data—Entire Workflow
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Channels | AVHRR/1 | AVHRR/2 | AVHRR/3 |
---|---|---|---|
1 | 0.58–0.68 | 0.58–0.68 | 0.58–0.68 |
2 | 0.725–1.10 | 0.725–1.10 | 0.725–1.10 |
3a | N.A | N.A | 1.58–1.68 |
3b | 3.55–3.93 | 3.55–3.93 | 3.55–3.93 |
4 | 10.50–11.50 | 10.3–11.3 | 10.3–11.3 |
5 | Ch.4 repeated | 11.5–12.5 | 11.5–12.5 |
Software | Source | Version | License |
---|---|---|---|
Pytroll/mpop | https://github.com/pytroll/mpop | - | GNU GPL v3 |
Pytroll/pygac | https://github.com/adybbroe/pygac | - | GNU GPL v3 |
Pytroll/pyresample | https://github.com/pytroll/pyresample/ | - | GNU GPL v3 |
Orfeo Toolbox | https://www.orfeo-toolbox.org/ | 4.4.0 | CeCILL v2 |
GRASS GIS | http://grass.osgeo.org/ | 7.0.0 | GNU GPL v3 |
Sensor pair | N12/N14 | N17/N18 | N18/N19 | N11/ATS1 | N14/ATS2 | N17/AATSR | N17/MODT | N16/MODA | N18/MODA | N19/MODA |
---|---|---|---|---|---|---|---|---|---|---|
MAE | 1.18 | 0.67 | 0.35 | 0.81 | 0.72 | 0.6 | 1.2 | 0.99 | 0.75 | 0.95 |
RMSE | 1.36 | 0.88 | 0.44 | 1.0 | 0.98 | 0.85 | 1.35 | 1.16 | 0.92 | 1.12 |
N | 26 | 75 | 10 | 38 | 98 | 28 | 75 | 149 | 262 | 385 |
Satellites | Slope | R2 | RMSE | MAE | N |
---|---|---|---|---|---|
NOAA-11 | 0.97 | 0.98 | 0.81 | 0.65 | 12 |
NOAA-14 | 0.90 | 0.93 | 1.38 | 1.05 | 22 |
NOAA-16 | 0.98 | 0.99 | 0.36 | 0.28 | 10 |
NOAA-17 | 0.89 | 0.99 | 0.56 | 0.46 | 7 |
NOAA-18 | 0.98 | 0.97 | 1.04 | 0.77 | 12 |
NOAA-19 | 0.92 | 0.94 | 1.40 | 1.08 | 21 |
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Share and Cite
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. https://doi.org/10.3390/rs8030169
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 Sensing. 2016; 8(3):169. https://doi.org/10.3390/rs8030169
Chicago/Turabian StylePareeth, Sajid, Luca Delucchi, Markus Metz, Duccio Rocchini, Abhay Devasthale, Martin Raspaud, Rita Adrian, Nico Salmaso, and Markus Neteler. 2016. "New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data" Remote Sensing 8, no. 3: 169. https://doi.org/10.3390/rs8030169
APA StylePareeth, S., Delucchi, L., Metz, M., Rocchini, D., Devasthale, A., Raspaud, M., Adrian, R., Salmaso, N., & Neteler, M. (2016). New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperatures from Historical AVHRR LAC Data. Remote Sensing, 8(3), 169. https://doi.org/10.3390/rs8030169