Document Type
Article
Publication Date
7-11-2024
Department
Department of Physics
Abstract
The Deep Space Climate Observatory (DSCOVR) spacecraft drifts about the Lagrangian point ≈ 1.4 − 1.6 × 106 km from Earth, where its Earth Polychromatic Imaging Camera (EPIC) observes the entire sunlit face of Earth every 1–2 h. In an attempt to detect “signals,” i.e., longer-term changes and semi-permanent features such as the ever-present ocean glitter, while suppressing geographic “noise,” in this study, we introduce temporally and conditionally averaged reflectance images, performed on a fixed grid of pixels and uniquely suited to the DSCOVR/EPIC observational circumstances. The resulting images (maps), averaged in time over months and conditioned on surface/cover type such as land, ocean, or clouds, show seasonal dependence literally at a glance, e.g., by an apparent extent of polar caps. Clear ocean-only aggregate maps feature central patches of ocean glitter, linking directly to surface roughness and, thereby, global winds. When combined with clouds, these blue planet “moving average” maps also serve as diagnostic tools for cloud retrieval algorithms. Land-only images convey the prominence of Earth’s deserts and the variable opacity of the atmosphere at different wavelengths. Insights into climate science and diagnostic and educational tools are likely to emerge from such average reflectance maps.
Publication Title
Frontiers in Remote Sensing
Recommended Citation
Kostinski, A.,
Marshak, A.,
&
Várnai, T.
(2024).
Deep space observations of conditionally averaged global reflectance patterns.
Frontiers in Remote Sensing,
5.
http://doi.org/10.3389/frsen.2024.1404461
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/940
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
Copyright © 2024 Kostinski, Marshak and Várnai. Publisher’s version of record: https://doi.org/10.3389/frsen.2024.1404461