Abstract:The implementation of accurate atmospheric correction is a prerequisite for satellite observation and water quality monitoring in coastal areas. The potential of the fast-line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) was investigated here for the medium resolution imaging spectrometer (MERIS). As the comparison between discrete field sampling points and macro-scale satellite pixels is subject to spatial biases associated with small-scale spatial patchiness in the turbid and highly dynamic nearshore zone, an alternative approach was proposed here using high spatial resolution (1 m) airborne hyperspectral images as radiometric truthing references. While FLAASH was not optimal for moderately turbid offshore waters (suspended particulate matter (SPM) concentration < 50 g·m −3 ), it yields satisfactory results in the 50-1500 g·m −3 range, where MERIS standard atmospheric correction was subject to significant biases and failures. Due to the significant intra-pixel variability of SPM distribution in highly turbid areas, the acquisition of high resolution airborne images should be considered as a consistent strategy for the validation of medium resolution satellite remote sensing in the spatially heterogeneous and optically diverse nearshore waters.
The analysis of satellite ocean color data that are acquired over coastal waters is highly relevant to gain understanding of the functioning of these complex ecosystems. In particular, the estimation of the suspended particulate matter (SPM) concentrations is of great interest for monitoring the coastal dynamics. However, a high number of pixels of satellite images could be affected by the surface-reflected solar radiation, so-called the sunglint. These pixels are either removed from the data processing, which results in a loss of information about the ocean optical properties, or they are subject to the application of glint correction techniques that may contribute to increase the uncertainties in the SPM retrieval. The objective of this study is to demonstrate the high potential of exploiting satellite observations acquired in the sunglint viewing geometry for determining the water leaving radiance for SPM dominated coastal waters. For that purpose, the contribution of the water leaving radiance Lw to the satellite signal LTOA is quantified for the sunglint observation geometry using forward radiative transfer modelling. Some input parameters of the model were defined using in-situ bio-optical measurements performed in various coastal waters to make the simulations consistent with real-world observations. The results showed that the sunglint radiance is not sufficiently strong to mask the influence of the oceanic radiance at the satellite level, which oceanic radiance remains significant (e.g., 40% at 560 nm for a SPM concentration value of 9 g m−3). The influence of the sunglint radiance is even weaker for highly turbid waters and/or for strong wind conditions. In addition, the maximum radiance simulated in the sunglint region for highly turbid waters remains lower than the saturation radiances specified for the current ocean color sensors. The retrieval of Lw and SPM should thus be feasible from radiances measured in the sunglint pattern by satellite sensors, thus increasing the number of exploitable pixels within a satellite image. The results obtained here could be used as a basis for the development of inverse ocean color algorithms that would interestingly use the radiance measured in sunglint observation geometry as it has been done for other topics than the field of ocean color research.
Satellite remote sensing of coastal waters is important for understanding the functioning of these complex ecosystems. High satellite revisit frequency is required to permit a relevant monitoring of the strong dynamical processes involved in such areas, for example rivers discharge or tidal currents. One key parameter that is derived from satellite data is the suspended particulate matter (SPM) concentration. Knowledge of the variability of SPM could be used by sediment transport models for providing accurate predictions. Most of the current satellites that are dedicated to ocean color observations have a sun-synchronous orbit that performs a single daytime observation. The Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color sensor (NASA/NOAA) is the only one that is equipped with a panchromatic spectral band, so-called Day-Night Band, which is able to measure extremely low level signals, typically of the order of magnitude of 10 −5 W m −2 sr −1 µm −1. The objective of this paper is to investigate the potential of the panchromatic and radiometric specifications of the VIIRS sensor to detect SPM concentrations from nighttime satellite observations. Realistic radiative transfer simulations are performed to quantitatively determine the amplitude of the top of atmosphere radiances under various conditions such as various moon incident illuminations, observation geometries, atmospheric and oceanic turbidities. The simulations are compared with the minimum detectable radiance as specified for the VIIRS sensor. The results show that the detection of SPM is systematically feasible, including in clear waters, for any observation geometries in the case of a full moon illumination. The sensitivity of the results to the lunar phase (i.e., out of the full moon conditions), which is one of the originalities of the study, shows that the detection should also be feasible for a significant number of nights over the entire lunar cycle, typically from 5 to 15 nights depending on the water turbidity. Therefore, nighttime ocean color panchromatic measurements performed using a VIIRS-like sensor are a highly promising approach, especially if it is combined with daytime observations, for improving the monitoring of ocean dynamics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.