Seagrass meadows are vital blue carbon ecosystems, and remote sensing provides a cost-effective means of monitoring their changes at high spatiotemporal resolution. While existing algorithms excel in low-tide mapping, accurately and consistently identifying seagrass at mid-to-high tide levels remains challenging. This study presents a Support Vector Machine (SVM)-based Substrate Classification Model (SCM_SVM) for automated seagrass identification across various tidal conditions using Planet SuperDove imagery, with a demonstration provided for the Li’An Lagoon. By training with ~1.8 million matched ground-truth substrate data and Rayleigh scattering-corrected top-of-atmosphere reflectance (ρrc), SCM_SVM could robustly identify seagrass from SuperDove ρrcmeasurements across low to high tide levels. Validation against independent field measurements indicated a detection accuracy of seagrass exceeding 85%. Notably, SCM_SVM provided consistent seagrass distributions in spatial patterns and extents for images acquired under different tidal levels. Time-series analysis from 2021 to 2023 revealed a significant decline of -0.15 km2/yr in the area. These results underscore the potential of SuperDove for high spatiotemporal resolution monitoring of seagrass dynamics. Future work will focus on enhancing the global applicability of SCM_SVM and extending it to detect other submerged vegetation.

Carbon dioxide (CO2) is the most important greenhouse gas in the atmosphere, playing a crucial role in the greenhouse effect and climate change. Lidar, with its high spatiotemporal resolution and high-precision detection capabilities, has become an essential tool for remote sensing of CO2. However, precise temperature information is required for CO2 retrieval. Studies showed that for both differential absorption lidar (DIAL) and spectroscopic lidar, a CO2 concentration measurement error of 2.0–3.5 ppm would result from each 1 K temperature deviation. Therefore, using nonreal-time and non in situ temperature data can lead to significant CO2 retrieval errors. In this study, a column-averaged CO2 spectroscopy lidar is proposed, which enables simultaneous measurements of CO2 concentration, temperature, and semi-heavy water (HDO, isotopic water vapor). First, a model combining five Lorentzian functions with a binomial background was proposed through spectral decomposition. Second, through theoretical analysis, the fitting parameters were reduced from 18 to 5. Finally, theoretical analysis shows that the model achieves system biases of less than 0.1 ppm for CO2, 0.1 K for temperature, and 0.06 ppm for HDO. Considering Poisson noise, the error distributions of CO2, temperature, and HDO under different optical distances and signal-to-noise ratios (SNRs) were studied. This technology will advance the development of CO2 flux remote sensing and is expected to play a crucial role in ecosystem research, atmospheric environmental monitoring, and greenhouse gas emission reduction policies.

Phytoplankton layers are critical bio-environmental coupled dynamic structures in the ocean, playing an essential role in global marine ecosystem functioning and biogeochemical cycles by driving the carbon pump, sustaining energy flow in food webs, and regulating acoustic properties. With its profile detection capability, high vertical resolution, and continuous day-and-night observation, lidar has emerged as an effective tool for detecting phytoplankton layers. However, existing lidar-based inversion algorithms typically rely on specific assumptions and are affected by multiple scattering effects, leading to significant deviations in the inversion results. As an important marginal sea in the western Pacific, the South China Sea, with its unique phytoplankton layer structure, provides valuable insights into regional bio-physical coupling mechanisms. In this study, the typical phytoplankton layer in the South China Sea was selected as the research target, and four representative lidar algorithms (slope method, improved adaptive phytoplankton layer detection method, Klett method and perturbation method) were systematically analyzed for their effectiveness and applicability in inverting the phytoplankton layer based on a semi-analytical Monte Carlo (MC) model. Comparative analysis indicates that the perturbation method is the most effective in extracting key features of the phytoplankton layer, including its depth of maximum and thickness, although significant deviations remain in the thickness inversion. To address this issue, a statistical correction model developed from the semi-analytical MC simulation is proposed to amend the perturbation method’s results. Airborne lidar experiments conducted in spring 2024 demonstrate that the correction model significantly improves the inversion accuracy of phytoplankton layer thickness, with accuracy improvements of 94.8%, 78.9%, 74.6%, and 91.0% at four sampling sites (A1, A2, B1, and B2) in South China Sea, respectively. This study provides novel technology for high-precision lidar inversion of oceanic phytoplankton layers and is expected to advance research on marine primary productivity and carbon cycling.

Bathymetric lidar, with its deep penetration, continuous day-and-night operation, and high accuracy, is an important tool for remotely sensing bottom depths. However, the strong forward scattering of the laser beam during transmission in water introduces substantial multiple scattering components into the lidar signal reflected by sea bottom, leading to a peak shift and signal broadening. The peak shift leads to an overestimation of the bottom depth, while the signal broadening makes peak extraction more challenging. To quantitatively study this impact, a semianalytic Monte Carlo (MC) simulation is applied to model seabed reflected signals. By statistically analyzing the peak position bias (termed as Bias) and full-width at half-maximum (termed as FWHM) of the seabed lidar reflected signals across four platforms—spaceborne, airborne, shipborne, and underwater—empirical models are established to relate Bias and FWHM to scattering efficient (b), bottom depth ($z_{\mathbf {m}}$), and lidar receiver footprint ($r_{\mathbf {s}}$). Here,$r_{\mathbf {s}}$represents the radius of the footprint of the lidar receiver on the water surface. Furthermore, the effects of different scattering phase functions and the absorption coefficient are analyzed. This study shows that the Bias and FWHM are influenced by$b, z_{\mathbf {m}}$, and$r_{\mathbf {s}}$. For lidar systems with an$r_{\mathbf {s}}$of dozens of meters, measuring deeper depths in water with higher b can result in a bottom depth overestimation of nearly 4% and an FWHM broadening exceeding 28 ns solely due to multiple scattering effects. This article provides a theoretical basis for correcting and evaluating bathymetric lidar data, thereby improving the accuracy and applicability of bathymetric lidar results.

HiSea-II, designed for monitoring coastal-inland biogeochemical properties and launched into orbit on June 11, 2021, is the first small satellite (SmallSat) simultaneously satisfying high spatial resolution (20 m), high signal-to-noise ratio (SNR) (~300) and wide swath (~200 km). In this article, we provide detailed descriptions of this SmallSat, introduce its data products, highlight its unique features, also share its shortcomings so future such SmallSat could be improved. SmallSat for ocean color is at its early stage, we advocate more investment and efforts to advance this useful platform, so a constellation of ocean-color SmallSats could be established in the future for effective monitoring of coastal-inland aquatic ecosystems.

Oceanic lidar has emerged as a strong technology for oceanic three-dimensional remote sensing. However, most existing oceanic lidars are bulky and high-power consumption, thus difficult to enable underwater operation. Here we present a compact single-photon lidar system for long-range underwater measurement. A single-photon detector was adopted to achieve a high signal-to-noise ratio. Benefiting from the single-photon sensitivity in detection, long-range active detection was realized with a low pulse energy laser at 1 μJ and a small-aperture coupler at 12 mm. Moreover, a narrow linewidth picosecond fiber laser with high repetition rate was employed to guarantee a high spatial resolution and high update rate. A fiber-connected configuration was specially designed for the miniaturized and robust structure in an optical receiver. In an experimental demonstration, the profile of backscattered signal from clean water was obtained over 70 m with high spatial-temporal resolution to demonstrate the capability of this lidar system. The maximum detection distance of the single-photon lidar reaches ~3.6/Kd(Kd, diffuse attenuation coefficient) for waterbody and up to 5.5/Kdfor a hard target. Furthermore, it exhibits a high update rate capability and realizes the localization and quantification of underwater bubbles up to 26 m away at a high update rate of 100 Hz. These results indicate its potential in a variety of applications including remote sensing of marine biogeochemical parameters, the quantification of seabed gas emissions, and long-range underwater imaging.

Primary production (PP) of the South China Sea (SCS) basin area (waters depth deeper than 200 m) is estimated using satellite products, with an overarching goal to reliably characterize the spatial distribution and temporal variation of PP of this important marginal sea. Among the PP models used, the absorption-based model (AbPM) showed better performance ($R^{2}=0.47$and$N =39$). In comparison, the$R^{2}$value is 0.26 for a chlorophyll-based model [vertically generalized production model (VGPM)] and 0.15 for the carbon-based model (CbPM). Furthermore, we observed that the PP spatial patterns obtained from these models were similar but disagree on the annual PP magnitude, where VGPM and CbPM, respectively, obtained$\sim $50% lower and$\sim $40% higher annual PP compared to that obtained by AbPM. In particular, after analysis using empirical orthogonal functions (EOFs), the upwelling-induced high PP off Luzon (winter) and Vietnam coast (summer) was clearly reflected in the first EOF mode of the AbPM results, and its principal component 1 has shown a decreasing trend for the period of 2003–2019 (−15.0% yr$^{-1}$for winter,$p < 0.05$; −14.7% yr$^{-1}$for summer,$p < 0.05$), which reflects the impact of weakening wind and higher sea surface temperature in the SCS. For the results of VGPM and CbPM, however, no strong relationships were found with the main regional oceanographic features. These results suggested that the spatiotemporal variations of SCS PP obtained from AbPM are more reasonable and further highlight the importance of a robust model in reliably capturing large-scale spatiotemporal dynamics of PP in marine environments.

In the above article[1],(2)should be expressed as follows:

For the first time the vertical variation in light quality in the global ocean is quantified with a single parameter—the hue angle ($\alpha _{E}$, in degree) in chromaticity of downwelling irradiance. For oceanic waters,$\alpha _{E}$is ~140° at surface, but it becomes ~230° at the bottom of the euphotic zone;$\alpha _{E}$changes rapidly near the surface, and we term this layer of rapid change in light quality as chromocline, analogous to the thermocline or pycnocline in oceanography. The 3-D variations in light quality are further highlighted with data from satellite ocean color measurements, where global distributions of$\alpha _{E}$for depths of 99%, 37%, and 1% of surface photosynthetically available radiation (PAR) are presented. As an example to demonstrate the importance to consider the change in light quality, the relationship between the light quality and the ratio of phytoplankton absorbed light to PAR is presented where this ratio may vary by a factor of 3 or more under different chlorophyll-a concentrations; otherwise, the ratio would be constant vertically. We advocate quantitative measurement and report the light quality in the upper ocean with such a single and objective parameter to accompany the routine measurement and report the light intensity, which will greatly improve our understanding of light-related processes and further bridge ocean optics and oceanography.

Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance (${R_{\mathrm{ rs}}}$), it is found that, for P. globosa blooms, the maximum of the second-derivative (${d\lambda ^{2}{R}_{\mathrm{ rs}}}$) of${R_{\mathrm{ rs}}(\lambda)}$in the 460–480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll${c_{3}}$(Chl$-/{c_{3}}$) around 466 nm, a prominent feature of P. globosa. This position of$ {d\lambda ^{2}{R}_{\mathrm{ rs}}}$maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured${R_{\mathrm{ rs}}}$. Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl$-/{a}$) concentration and colony numbers of P. globosa were estimated from UAV-derived${R_{\mathrm{ rs}}}$, where P. globosa colonies were found in a range of ~3–37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.

Ocean color satellite sensors have become an indispensable component in the Earth Observing System, in which the use of multiple ocean color satellite sensors not only improves the spatiotemporal coverage of the global oceans but also maintains the continuity of the data products for long-term monitoring. In this research, the performance of a new ocean color satellite sensor Chinese Ocean Color and Temperature Scanner (COCTS) from HY1C launched in September 2018 is thoroughly evaluated with two important aspects: the signal-to-noise ratio (SNR) at the top of the atmosphere and the uncertainty in the remote-sensing reflectance Rrs) products. The results showed that the SNR of the COCTS can satisfy the requirements of the ocean color applications, and the uncertainty in the Rrs at the blue bands in the ocean waters meets the demand of less than 5%. A further comparison with other well-known ocean color sensors indicates that not only the COCTS can provide reliable ocean color data but also the processing system is robust and reliable. These results provide a solid base for merging the COCTS products with other ocean color sensors for the studies of ocean biogeochemistry.

Photosynthetically Available Radiation (PAR) is an important parameter to estimate marine primary productivity (PP), where MODIS daily PAR is generated by an instantaneous PAR (iPAR) around local solar noon. The uncertainties associated with MODIS daily PAR need to be quantified in order to understand the uncertainties in the estimated PP. On the other hand, Geostationary Operational Environmental Satellite system (GOES) provides estimates of hourly solar radiation product (24 images per day) over North America, which can be integrated to produce daily PAR. In this study, both GOES iPAR and daily PAR are used to compare with the corresponding MODIS products to understand uncertainties in MODIS daily PAR products and to understand how multiple measurements per day can improve daily PAR estimates. While GOES iPAR and MODIS iPAR showed excellent consistency over cloud-free regions, their daily PAR products showed some degree of discrepancy due to variable cloud cover.

Phytoplankton pigment absorption data from algal-bloom-dominated waters are highly desirable to better understand the primary productivity and carbon uptake by algal biomass in a regional scale. However, retrieving phytoplankton pigment absorption coefficients, in turbid and hypereutrophic waters, from above-surface remote sensing reflectance (Rrs) is often challenging because of the optical complexity of the water body. In this paper, a quasi-analytical algorithm has been parameterized using in situ data to retrieve inherent optical properties from Rrs(λ) in highly turbid productive aquaculture ponds, where the phytoplankton absorption coefficient (3.44-37.67 m-1) contributes 54 % of the total absorption at 443 nm (4.99-47.21 m-1). The model was validated using an independent data set by comparing the model-derived optical parameters with in situ measured values. The absolute percentage error (assuming no error in the in situ measurements) of the estimated total absorption coefficient at( λ) varied from 15.22 % to 24.13 % within 413-665 nm, and the overall average error was 19.87 %. Maximum and minimum errors occurred at 443 and 665 nm, respectively. Similarly, the percentage error for the phytoplankton absorption coefficient aφ(λ) varied from 15.9 % to 41.27 % within the 413-665-nm range, and the average error was 27.24 %. The spectral shape of modeled aφ(λ) matched very well (R2= 0.97) with the measured aφ(λ). A supplementary method was also developed to retrieve first-order estimates of colored detrital matter absorption coefficients aCDM( λ) from subsurface remote sensing reflectance rrs( λ) using an empirical approach. Results reveal that the retrieval accuracy of aφ(λ) improved after incorporating the first-order estimates of aCDM(λ) in the algorithm.

The application of ocean color product retrieval algorithms for pixels containing cloud shadows leads to erroneous results. Thus, shadows are an important scene type that should be identified and excluded from the set of clear-sky pixels. In this paper, we present an optical cloud shadow-detection technique called the Cloud Shadow Detection Index (CSDI). This approach is for homogeneous water bodies such as deep waters where shadow detection is very challenging due to the relatively small differences in the brightness values of the shadows and neighboring sunlit or some other regions. The CSDI technique is developed based on the small differences between the total radiances reaching the sensor from the shadowed and neighboring sunlit regions of similar optical properties by amplifying the differences through integrating the spectra of the two regions. The Integrated Value (IV) is then normalized by the mean of the IVs within a spatial adaptive sliding box where atmospheric and marine optical properties are assumed homogeneous. Assuming that the true color and the IV images represent accurate shadow locations, the results were visually compared. The CSDI images agree reasonably well with the corresponding true color and the IV images over open ocean. Also, the shape of the cloud shadow particularly for the isolated cloud closely follows that of the cloud, as expected, reconfirming the potential of the CSDI technique.

WorldView 2 (WV2), launched in September 2009, is a satellite with hyperspatial resolution ($\sim$0.5–2 m) capability for Earth surface observation. It has eight spectral bands with enhanced signal-to-noise ratio to cover the visible-to-near-infrared (V–NIR) domain, thus providing a great potential for remote sensing of coastal ecosystem, in particular, the aquatic environments with shallow bottoms (e.g., coral reefs and seagrass beds). Traditionally, it requires$\sim$15 spectral bands in the V–NIR domain for reliable analytical retrieval of bottom properties (e.g., bathymetry) from remotely observed radiance spectrum. Data from WV2, however, have eight bands, and the width of each band is quite wide ($\sim$50 nm or more). Thus, the spectral configuration of WV2 is far from optimal for spectral remote sensing of various complex shallow environments, and it is important and necessary to know how such a band setting affects the reliability of remote-sensing retrievals. Here, we applied a hyperspectral optimization scheme [hyperspectral optimization processing exemplar (HOPE)] to a simulated shallow-bottom data set (sandy bottom) and compared retrievals from both hyperspectral and WV2 spectral settings. Retrieved results suggest that, for bottom contribution making up 40% or more of the measured signal, the depths derived from both hyperspectral and WV2 settings are generally consistent for waters shallower than 5 m. However, depths derived with WV2 setting have greater uncertainty and, in general, are shallower than those derived from the hyperspectral setting, particularly for waters deeper than 10 m. Options to produce higher confident properties from such band settings are discussed.

The Ocean Color CAL/VAL team is evaluating the VIIRS bio-optical products for real-time operations. VIIRS ocean data are being processed using standard government algorithms, and channel calibration and product validation evaluation activities are ongoing. A network of 27 global “Golden Regions” has been established to evaluate and validate bio-optical products. Satellite inter-comparison for data consistency with current ocean color products, and real time vicarious adjustment calculation are performed using in situ water leaving radiance propagated to Top of Atmosphere in coastal and open ocean regions. In addition, routine matchups with VIIRS and MODIS-Aqua are done with in situ data collection from ships and real time coastal AERONET-OC sites. The above activities, product evaluation and tracking of channel stability, are being contributed to the JPSS Team to evaluate the overall mission, including calibration and inter-satellite product consistency. Initial NPP VIIRS ocean bio-optical products are demonstrated with other ocean color satellites.

The Ocean-Colour Climate-Change Initiative (OC-CCI) aims to create a long-term, consistent, error-characterised time series of ocean-colour products, for use in climate change studies. Climate Change Initiative is a programme of the European Space Agency devoted to using satellites to generate climate quality time series data of Essential Climate Variables (ECVs) identified by the Global Climate Observing System (GCOS). Within the ocean colour CCI project, a user consultation was undertaken, targeting both the climate modelling community and the Earth Observation community. Taking the user requirements into account, a set of criteria was developed, for selecting the best ocean-colour algorithms for climate research. Candidate atmospheric correction algorithms and in water algorithms have been submitted to a round robin comparison. The overall best performers are being used to generate test products, to be evaluated further.

In this paper, we present a method for atmospheric correction that uses the cloud and shadow image features. An iterative scheme is formulated to computes the ratio of diffuse to direct irradiance as well as the path radiance using the radiance detected over the open water and shadow pixels. These parameters are then used to compute the cloud and water reflectance. We implemented this method on IKONOS images which are known to have inferior signal to noise ratio compared to sensors specially designed for ocean color measurements. The corrected image reflectance over water pixels are compared to field measurements.

The Coastal Zone Color Scanner (CZCS) on NASA's Nimbus-7 satellite (1978-1986) demonstrated the utility of ocean color measurements for studying the dynamics of the ocean. The CZCS worked well for the continental shelf and open ocean regions. However, it did not have the spectral and spatial resolution needed to deal with the complexity of the coastal ocean. With the goal of developing tools suitable for the coastal ocean we initiated studies using the Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS) in 1989. This paper reviews the progress from those initial studies to the current state of imaging spectrometry for the coastal ocean.

Daily high-resolution Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images of the central North Atlantic Ocean (1998-2003) show that temporal changes in the absorption coefficient of colored dissolved organic matter (CDOM) or "yellow substance" follow changes in phytoplankton pigment absorption coefficient in time. CDOM peaks (between January and March) and troughs (late summer and fall) followed pigment peaks and troughs by approximately two and four weeks, respectively. This phase shift is additional strong evidence that CDOM in the marine environment is derived from phytoplankton degradation. The common assumption of linear covariation between chlorophyll and CDOM is a simplification even in this ocean gyre. Due to the temporal changes in CDOM, chlorophyll concentration estimated based on traditional remote sensing band-ratio algorithms may be overestimated by about 10% during the spring bloom and underestimated by a similar 10% during the fall. These observations are only possible through use of synoptic, precise, accurate, and frequent measurements afforded by space-based sensors because in situ technologies cannot provide the required sensitivity or synoptic coverage to observe these natural phenomena.

For many oceanic studies, it is required to know the distribution of visible solar radiation (EPAR) in the upper water column. One way to reach this is by remote sensing. This includes two components: First, EPAR at surface is calculated based on atmosphere properties along with the position of the Sun. Second, the vertical attenuation of EPAR (KPAR) is derived from products of ocean-color remote sensing. Currently, KPAR is estimated based on chlorophyll concentration ([C]) from ocean color. This kind of approach works well for waters where all optical properties can be adequately described by values of [C], but will result in large uncertainties for coastal waters where [C] alone cannot accurately describe the optical properties. In this paper, we present an innovative model that describes KPAR as a function of water's inherent optical properties (IOP).

For many oceanographic studies and applications, it is desirable to know the spectrum of the attenuation coefficient. For water of the vast ocean, an effective way to get information about this property is through satellite measurements of ocean color. Past and present satellite sensors designed for ocean-color measurements, however, can only provide data in a few spectral bands. A tool is needed to expand these multiband measurements to hyperspectral information. The major contributors to the attenuation coefficient are absorption and backscattering coefficients. The spectral backscattering coefficient can generally be well described with a couple of parameters, but not so for the spectral absorption coefficient. In this paper, based on available hyperspectral absorption data, spectral-transfer coefficients are developed to expand multiband absorption coefficients to hyperspectral (400-700 nm with a 10-nm step) absorption spectrum. The derived transfer coefficients are further applied to data from field measurements to test their performance, and it is found that modeled absorption matches measured absorption very well (/spl sim/5% error). These results indicate that when absorption and backscattering coefficients are available at multiple bands, a hyperspectral attenuation-coefficient spectrum can now be well constructed.

HYPERspectral Tethered Spectral Radiometer Buoy (Hyper-TSRB, Satlantic Inc.) has 123 channels from 400 nm to 800 nm to measure downwelling irradiance (E/sub d/) and upwelling radiance (L/sub u/). The supplied software for Hyper-TSRB data processing (AKA, PROSOFT) is based on Case I and/or empirical algorithms. A new semi-analytical method is proposed for Level 3 data processing. Basically, for open oceans case I waters, no large differences are found between the new method and PROSOFT (/spl sim/10%); while for coastal case II waters, the new method is much better than PROSOFT in computed remote sensing reflectance (/spl sim/10%-350%). The effect of phase function to the new method is also analyzed, it is shown that the new method can work stably for a wide phase function ranges.

Using three different particle phase functions, subsurface remote-sensing reflectance (rrs) are simulated by Hydrolight, with the Sun positioned at 10deg, 30deg, and 60deg from zenith, respectively. The values of rrsat six angles are compared with that at nadir, so do the spectral ratios of rrs(440)/rrs(560). It is found that, generally, rrsvalues differ the most when the Sun is at 60deg and the sensor is partially facing the Sun. For all sun angles, observation angles, and particle phase functions in this study, however, the ratios of rrs(440)/rrs(560) only show limited variations. These results suggest that remote-sensing algorithms based on such kind of ratios are generally applicable to rrsobserved in the remote-sensing domain

A multiband Quasi-Analytical Algorithm (QAA) was proposed by Lee et al. in 2002. It can be used to quickly retrieve the total absorption coefficients, particle back scattering coefficients, and absorption coefficients of phytoplankton pigment and colored dissolved organic matter (CDOM) etc for optically deep water. Effects of inelastic scattering, e.g., Raman scattering and CDOM fluorescence, were not taken into account. Contribution of Raman scattering for clear ocean waters, and effect of CDOM fluorescence for high CDOM concentration waters, however, were not neglectable, so their effects were simulated using the Hydrolight numerical simulation technique. It is found that contributions of Raman scattering to remote sensing reflectance are mainly between 400 nm and 500 nm, and are ~9% to 25%; effects of CDOM fluorescence are mainly between 400 nm and 550 nm, and about 1% to 15%; however, effects of Raman scattering and CDOM fluorescence to the inversion QAA algorithm are not significant (less than 5%), and may not to be corrected during inversion

Retrieval of water optical properties and concentrations can be identified as a nonlinear optimization problem. This problem may be difficult to solve by conventional optimization methods owing to its multimodel nonconvex nature. This letter explores the potential of genetic algorithms as the optimization scheme in such a problem. A remote sensing reflectance model for optically deep waters was used to illustrate the performance of the algorithms. The superiority of genetic algorithms over conventional optimization methods was demonstrated by experiments on a field dataset.