Journal
REMOTE SENSING OF ENVIRONMENT
Volume 262, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112516
Keywords
Satellite remote sensing; Carbon monoxide; Biomass burning; MOPITT
Funding
- National Aeronauticsand Space Administration (NASA) Earth Observing System (EOS) Program
- National Science Foundation
- ACRIDICON-CHUVA campaign by the Max Planck Society
- German Aerospace Center (DLR)
- FAPESP (Sao Paulo Research Foundation)
- German Science Foundation (Deutsche Forschungsgemeinschaft, DFG) within the DFG Priority Program [SPP 1294]
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The study describes revisions to the MOPITT cloud detection algorithm, which substantially increase retrieval sampling over land in varying pollution conditions. The improved algorithm's performance is evaluated through validation, case studies, and continental-scale maps, showing significant improvements in regions like South America and Asia.
Measurements made by the MOPITT (Measurements of Pollution in the Troposphere) instrument on the NASA Terra polar-orbiting platform enable the retrieval of tropospheric carbon monoxide (CO) concentrations. As determined by the Terra orbit and MOPITT swath width, the frequency of MOPITT observations at a specific location, or measurement sampling frequency, is typically about once every three to four days. However, because the MOPITT retrieval algorithm is only applicable to clear-sky scenes, MOPITT retrieval sampling frequency strongly depends on regional cloudiness and can be much smaller than the measurement sampling frequency. Moreover, highly polluted scenes, characterized by high aerosol optical depths, can be confused with cloudy scenes and thus be discarded unnecessarily by the MOPITT cloud detection algorithm. Herein are described revisions to this algorithm which substantially increase retrieval sampling over land in varying pollution conditions. The performance of the revised cloud detection algorithm is evaluated through validation, case studies, and continental-scale maps of retrieval sampling frequency. Presented case studies illustrate (1) why the current operational MOPITT cloud detection algorithm excludes extended areas of potentially valuable cloud-free MOPITT observations and (2) how, for the same scenes, improved retrieval coverage benefits analyses of regional CO variability. Maps of retrieval sampling frequency for South America and Asia exhibit well-defined improvements, especially in regions with poor sampling frequency in the current product.
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