Uncertainty in the Retrieval of Shallow Coastal Properties from Remote Sensing Imposed by Sensor Noise
Abstract
Satellite remote sensing systems designed for coastal applications strive to provide high quality data across the visible and near infrared portions of the electromagnetic spectrum. Data quality is driven by factors related to sensor design and others environmental variability. This work is focused on the impact of sensor signal to noise (SNR) on the retrieval of key ecological parameters; water column impurity concentration (chlorophyll, colored dissolved organic matter, and suspended sediment), water depth, and benthic cover. Uncertainty is defined as parameter variability producing a spectral response that is indistinguishable from the true condition. The impact of sensor SNR is investigated using Monte Carlo methods that include all joint interactions between environmental parameters. The results quantify important concepts in parameter retrieval uncertainty; increasing parameter uncertainty with increasing system noise, increasing water column parameter uncertainty with decreasing water depth, and increasing benthic parameter uncertainty with increasing water depth. The results will inform users of coastal products derived from current remote sensing systems and developers of future sensors intended for shallow water coastal applications.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.B44A..15A
- Keywords:
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- 0480 Remote sensing;
- BIOGEOSCIENCESDE: 1640 Remote sensing;
- GLOBAL CHANGE