## 4 Discussion
In addition to \(SNR\) the likelihood of detecting a bleaching event depends on the contrast between healthy and bleached coral. Bleached coral is not always as bright as the surrounding uncolonized sand [13] and the fraction of symbiont loss does not always scale with reflectance [45; 46]. Therefore, the uncertainty estimates with regard to bleaching are likely optimistic. The joint retrieval of both benthic cover and water constituent information is problematic, regardless of \(SNR\). The least uncertainty in water constituent concentration occurred in optically deep water where the effects of benthic cover were minimal, but the benthic cover was highly uncertain. Conversely, the most accurate detection of benthic cover occurred in optically shallow water, but uncertainty in water constituent concentration was greatest. There is perhaps a glimmer of hope for the shallow water conundrum in that water constituent uncertainty is lower when the water constituent and benthic cover result in dissimilar effects on the total water reflectance. If the water constituent material looks like the benthic cover material, uncertainty in the retrieval of either the water constituent or benthic cover is large. The converse is also true. Within shallow water, uncertainty associated with water column chlorophyll and dissolved organic matter (both results in darker water) is minimal when the bottom is composed of bright, uncolonized sand whereas uncertainty in suspended calcite sediment (which increases water reflectance) is smallest when the bottom is composed of dark coral or algae. This raises the possibility of constructing water constituent retrieval algorithms that are benthic cover specific. For example, one algorithm could potentially retrieve \(C_{chl}\) and \(a_{g,450}\) over bright, shallow features while an algorithm for retrieving \(C_{cal}\) could apply only to areas of dark benthic cover. Such an approach, if successful, would likely resolve spatial variability in water quality within a heterogeneous reef environment and would be an improvement over the practice of retrieving water constituent concentration from adjacent deep water signals that are then extended to the entire adjacent shallow reef. Regardless, it is important to note that \(SNR\) will have a significant impact on the detection uncertainty, especially when \(SNR\) < 500 and \(D_{o}\) < 1. This study suggests that \(SNR\) for shallow coral reef applications could be optimized around a value of 500. For \(SNR\) < 500, uncertainties associated with detecting changes in benthic cover type, bottom depth and water constituent concentration increase rapidly and limit remote sensing utility to the shallowest portions of a reef environment. As \(SNR\) increases beyond 500 the parameter uncertainty decreases, but at a much slower rate. Detection of coral reef benthic features, e.g., coral reef species, seagrasses, benthic algae, and uncolonized sand, has been demonstrated utilizing airborne scanning spectrometers with \(SNR\) approaching 500, computed as the top of atmosphere radiance from a clear maritime atmosphere and a 5% reflectance surface [14; 15; 16; 17; 18]. In comparison, data from the Landsat-8 Operational Land Imager (OLI) with \(SNR\) between 237 and 367 and four relatively broad visible bands have been applied to surveys of shallow reef flats [47; 48] with far less effectiveness. [PERSON] et al. [49], for example, combined empirically derived geomorphic-ecological rules with OLI data to map benthic cover type to a maximum depth of 20 m at selected locations within the Great Barrier Reef system. However, the OLI imagery was only used to characterize benthic cover directly where \(D\) < 0.75 m. In the deeper reef areas, only single-band determinations were used to characterize the bottom as either uncolonized sand (light pixels) or combinations of coral, rock, and algae (dark pixels).