The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing
Open accessAbstract
Accurate correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.
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Cites
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The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm
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Cited by
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R., McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., et al. (2017) · Water Resources Research · doi:10.1002/2016wr020175 · Cited by 991
Joint deep learning for land cover and land use classification
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.014 · Cited by 451
Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005583117 · Cited by 165
Metadata
- Authors
- Emberton, S., Chittka, L., Cavallaro, A., & Wang, M.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2015-12-22
- Volume
- 8
- Issue
- 1
- Pages
- 1
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2232920211
- Cited by
- 486
- References
- 97
- FWCI
- 42.88
- Type
- journal article
- Language
- en
- Open access
- gold
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- Emberton, S., Chittka, L., Cavallaro, A., & Wang, M. (2015). Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing. Remote Sensing. https://doi.org/10.3390/rs8010001
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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