| _version_ | 1866902137412255744 |
|---|---|
| author | Fasnacht, Zachary |
| author_facet | Fasnacht, Zachary |
| contents | <p>These data are retrievals of ocean color properties (chlorophyll and remote sensing reflectance) for the Tropospheric Emissions: Monitoring of Pollution (TEMPO ) instrument. They have been developed using a machine learning based approach. In this approach, TEMPO measured reflectances are decomposed into principal components that describe the features in the spectra. The coefficients of those principal components are used to train a neural network that learns the relationships between the principal components and a physical quanity, which in this case is the ocean color properties. The ocean color data used to train the model was co-located from the Moderate Resolution Imaging Spectrometer (MODIS) onboard the Aqua satellite, the Visible Infrared Radiometer Suite (VIIRS) onboard NOAA-20 & NOAA-21, and Ocean and Land Color (OLCI) instrument onboard Sentinel 3A & Sentinel 3B. </p> <p> </p> <p>There are two datasets included in this repository. Both include chlorophyll concentration and remote sensing reflectance (443nm, 488nm, 560nm, 650nm)</p> <p>TEMPO_OceanColor.zip contains Level 2 granules for a few sample days during August 2023 and March 2024 </p> <p>TEMPO_OC_InsituObservations_March2024.h5 includes chlorophyll retrievals from TEMPO, MODIS, VIIRS, and OLCI along with in situ observations from the Point Loma outfall station off the coast of San Diego for March 2024. </p> <p>These data have undergone initial validation but should be used with caution as further validation is needed. </p> <p> </p> <p><span>For further details about how these data were produced, see Fasnacht et al. (2025), submitted to Earth and Space Science. </span></p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14990484 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TEMPO ML-Based Ocean Color Retrievals Fasnacht, Zachary <p>These data are retrievals of ocean color properties (chlorophyll and remote sensing reflectance) for the Tropospheric Emissions: Monitoring of Pollution (TEMPO ) instrument. They have been developed using a machine learning based approach. In this approach, TEMPO measured reflectances are decomposed into principal components that describe the features in the spectra. The coefficients of those principal components are used to train a neural network that learns the relationships between the principal components and a physical quanity, which in this case is the ocean color properties. The ocean color data used to train the model was co-located from the Moderate Resolution Imaging Spectrometer (MODIS) onboard the Aqua satellite, the Visible Infrared Radiometer Suite (VIIRS) onboard NOAA-20 & NOAA-21, and Ocean and Land Color (OLCI) instrument onboard Sentinel 3A & Sentinel 3B. </p> <p> </p> <p>There are two datasets included in this repository. Both include chlorophyll concentration and remote sensing reflectance (443nm, 488nm, 560nm, 650nm)</p> <p>TEMPO_OceanColor.zip contains Level 2 granules for a few sample days during August 2023 and March 2024 </p> <p>TEMPO_OC_InsituObservations_March2024.h5 includes chlorophyll retrievals from TEMPO, MODIS, VIIRS, and OLCI along with in situ observations from the Point Loma outfall station off the coast of San Diego for March 2024. </p> <p>These data have undergone initial validation but should be used with caution as further validation is needed. </p> <p> </p> <p><span>For further details about how these data were produced, see Fasnacht et al. (2025), submitted to Earth and Space Science. </span></p> <p> </p> |
| title | TEMPO ML-Based Ocean Color Retrievals |
| url | https://doi.org/10.5281/zenodo.14990484 |