Retrieval Of Inherent Optical Properties Research Articles

Analysis of bio-optical active constituents for lentic ecosystem through spectral-spatial and in-vitro observation.

The neural network algorithm approach was adopted in Kolavai Lake to retrieve the inherent optical properties (IOP) of active constituents. The retrieval of IOP by absorption and the scattering of optically active constituents (OAC) through employing Sentinel-2 MSI reflectance and field measured the salinity and temperature. The result illustrates the relationship between the IOP and measured OAC's concentrations and its sensitivity towards spectral wavelength. It shows that the phytoplankton absorption ap is highly related with chlorophyll-a concentration and has an R2 value of 0.808. Furthermore, at the total absorption of water has high correlation with chl-a which indicates the significant dominance in the lentic water. Also, the pigment constituents are showing an R2 value of 0.754. The total backscattering of water (btot) is strongly related to the total suspended matter with R value > 0.73. The spatial distribution of OAC in Kolavai Lake helps monitor the lake water quality. This approach is well-performed in estimating the inherent optical properties of optically active constituents that gives insight for assessing the relationship between IOP and water quality. The research has proved to be a good potential for monitoring lentic water quality through Sentinel-2 MSI.

Estimating the model parameters for remote sensing reflectance pixel by pixel: a neural network approach for optically deep waters

ABSTRACT Remote sensing reflectance (R rs) and inherent optical properties (IOPs) conversions are fundamental in accurate satellite measurements to guarantee the semi-analytical retrieval quality of IOPs and the biogeochemical products. Traditionally, the R rs-IOPs conversions are determined by a quadratic polynomial function with two model parameters (G x = 0,1). However, G x values vary in time and location, which are attributed to the spatial and temporal variability inherent to illumination conditions, sea surface properties, and meteorological states. To improve the performance of three classical existing models used for R rs-IOPs conversions, we designed two novel neural network models (NNG x = 1,2) to quantitatively calculate G x from the R rs spectrum pixel by pixel without requirement of any auxiliary illumination and meteorological data, and then proposed for R rs-IOPs conversions. We evaluated these approaches with numerical simulations and field measurements, and the results show that the NNG x models are more effective in semi-analytically converting R rs into IOPs than the three existing models. Furthermore, we applied the NNG x models to satellite images to understand the downstream influence of the G x values on IOPs estimates for the global oceans. We further confirm that the G x values dramatically change for the global ocean, which is especially true for very oligotrophic gyres, coastal waters, and high latitude oceans. When we use a constant G x for the R rs-IOPs conversions, it leads to substantial uncertainty of up to 30% in the IOPs retrievals for China’s coastal regions. Our results suggest that it is possible to improve the data quality of IOPs for the global oceans by providing accurate pixel-level G x values using NNG x models.

Retrieval of inherent optical properties from multiple aquatic systems using a quasi-analytical algorithm for several water types

The estimation of inherent optical properties (IOPs) from remote sensing measurements is important for the establishment of a physically meaningful relationship to retrieve water quality parameters. However, remote sensing algorithms for the retrieval of IOPs are only reliable at specific study sites. In this context, the primary goal of the present study is to evaluate the use of the proposed single algorithm formulation for the retrieval of IOPs in different inland waters. The secondary goal is to examine the performance of the proposed formulation for different optical water types. The proposed algorithm single algorithm formulation got a normalized Root Mean Square Error (nRMSE) ranging from 4% to 28% for the estimation of the absorption coefficient of colored dissolved matter (aCDM). For the estimation of the absorption coefficient of phytoplankton (aphy) the proposed algorithm got nRMSE values ranging from 10% to 42% for 487 sampling points from 7 different aquatic systems. These results indicate the possibility of retrieving the absorption coefficients of phytoplankton and colored dissolved matter from different aquatic environments. Therefore, the proposed algorithm can be the first step for a unique methodology to estimate IOPs which are important for understanding biogeochemical processes on inland waters.

A neural network approach for deriving absorption coefficients of ocean water constituents from total light absorption and particulate absorption coefficients

Inherent Optical Properties (IOPs) of oceanic and coastal waters provide useful information for studying light availability in various ocean layers, primary productivity, particulate matter, and aquatic photochemistry. The total spectral absorption coefficient, a(λ) and spectral particulate absorption coefficient, ap(λ) are the IOPs that are some of the extensively measured IOPs with the existing absorption instruments. The total spectral absorption coefficient is often expressed as the sum of subcomponent IOPs, phytoplankton (aph(λ)), combined absorption due gelbstoff and detritus (adg(λ)) and the water itself. Similarly, ap can be subdivided into aph(λ) and ad(λ), the detrital absorption coefficients. The existing models partition these IOPs into subcomponent IOPs by assuming spectral shapes for subcomponent IOPs and are computationally intensive. Hence, in this study, we implemented Neural Networks to derive the subcomponent IOPs due to their lower computational time requirement and their capability to model complex relationships. We trained and validated four Neural Networks (NN) for retrieval of IOPs from a(λ) and ap(λ). NNs - I &II are used to retrieve aph and adg at 443 nm from a(λ). Similarly, NNs - III &IV are used to derive aph(443) and ad(443) from ap(λ). The four NNs are trained and tested using synthetic data created encompassing a wide range of optical properties observed in natural waters. For validation of the four NNs, the International Ocean Color Coordinating Group (IOCCG) simulated dataset, NASA bio-Optical Marine Algorithm Dataset (NOMAD) and global bio-optical insitu dataset (GBI) are used. The R2 values obtained for GBI in the retrieval of aph(443) and adg(443) using NNs - I &II are 0.95 and 0.97, respectively. Similarly, aph and ad retrieved using NN–III & IV resulted in R2 values of 0.94 and 0.94 for the NOMAD dataset. Upon comparing the performance NNs - I &II with Quasi-Analytical Algorithm (QAA) in retrieving IOPs using the IOCCG dataset, the NNs exhibited lower errors. We also developed a hybrid algorithm, QAANN, to demonstrate the capability of NNs - I &II for remote sensing applications. QAANN resulted in 11–43% lower mean absolute percentage error (MAPE) compared to QAA, Generalized Inherent Optical Property (GIOP) and Garver-Siegel-Maritorena (GSM) models for aph(443) retrieval using remote sensing reflectance from IOCCG simulated dataset. In the case of adg(443), QAANN resulted in 5–10% less MAPE compared to other models for the same dataset. These results suggest that the developed NNs can be applied to satellite remote sensing data to retrieve subcomponent IOPs with improved accuracy.

Sun glint correction with an inherent optical properties data processing system

ABSTRACT Frequent and extensive sun glint is a serious obstacle to real-time monitoring of ocean colour anomalies. We semi-analytically adjust an inherent optical properties (IOPs) data processing system for the open ocean to correct for sun glint (called ‘IDAS-SGC’) and for remote sensing reflectance (R rs) and IOPs retrievals. Tests with synthetic data validated the effectiveness of our algorithm in deriving ocean colour data from severely glint-contaminated images to produce high quality images. Evaluating results from single mission images suggested that our approach provides spatially smooth and consistent ocean colour products from both severe glint and glint-free regions for Visible Infrared Imaging Radiometer Suite and for Medium Resolution Spectral Imager II instruments. Specifically, complete coverage of circulation-caused ocean colour anomalies can be recovered from a single severe sun glint image. Comparing multi-mission images found that the inter-mission consistency for IDAS-SGC R rs in sun glint regions is comparable with the inter-mission consistency in glint-free regions. Furthermore, we evaluate the performance of the Cox-Munk algorithm for sun glint estimation, and we find that our IDAS-SGC algorithm is more effective than the Cox-Munk algorithm in deriving R rs products from severe sun glint regions due to the absence of accurate real-time wind data. Our results suggest that the IDAS-SGC algorithm obtains meaningful ocean colour products from sun glint-contaminated images of the open oceans.

Benthic classification and IOP retrievals in shallow water environments using MERIS imagery

Deriving inherent optical properties (IOPs) from multispectral imagery of shallow water environments using physics-based inversion models require prior knowledge of the spectral reflectance of the bottom substrate. The use of an incorrect bottom reflectance adversely affects the IOPs and, in part, the depth derived from inversion models. To date, an operational approach that determines the bottom reflectance from multispectral imagery is lacking; development in this area is especially paramount for locations that exhibit temporal variability in the spatial distributions of submerged aquatic vegetation and benthic microalgae. In this work, we develop a multispectral implementation of the HOPE-LUT algorithm (Hyperspectral Optimization Processing Exemplar with benthic Look Up Table), and apply the approach to MERIS imagery of the Great Bahama Bank (GBB). Overall benthic classification accuracy of this approach was 80.0%, revealing the areal coverage of benthic flora can range from 1052.3 km2 to 6169.3 km2 between years in the Exumas, GBB. Comparison of HOPE-LUT IOP retrievals to common inversion model implementations (particularly HOPE, with its default sand endmember) shows that using an incorrect bottom reflectance can lead to over-estimations in aphy(443) (absorption coefficient of phytoplankton at 443 nm), of up to 95%, under-estimations of adg(443) (absorption coefficient of detritus and gelbstoff) up to 50%, and over-estimations of depth up to 20%. In addition, the HOPE-LUT parameterizations generate IOPs within the range of those measured in situ. We demonstrate that, at the scale of a MERIS pixel, the dominant substrates of seagrass, unattached bottom macroalgae and benthic microalgae are spectrally unresolvable at the depths that these classes occur in the GBB. Lastly, we evaluate the performance of commonly used atmospheric corrections algorithms for bathymetry estimation and benthic classification accuracy. The combined benthic classification and inversion scheme presented here is autonomous, i.e., it does not require scene-specific thresholds or modifications. Thus, it should be portable to Sentinel 3 OLCI and potentially MODIS Aqua imagery to obtain a continuous time series of changes in IOPs and benthic cover for the shallow waters over the Great Bahama Bank.

The Nonlinear Effective Attenuation Coefficient of Spaceborne Oceanic Lidar Signal

Multiple scattering is an inevitable effect in spaceborne oceanic lidar because of the large footprint size and the high optical density of seawater. The effective attenuation coefficient klidar in oceanic lidar equation, which indicates the influence of the multiple scattering effect on the formation of lidar returns, is an important parameter in the retrieval of inherent optical properties (IOPs) of seawater. In this paper, the nonlinearity of klidar and the relationships between klidar and the IOPs of seawater are investigated by solving the radiative transfer equation with an improved semianalytic Monte Carlo model. klidar is found to decrease exponentially with the increase of depth in homogeneous waters. klidar is given as an exponential function of depth and IOPs of seawater. The results in this paper can help to have a better understanding of the multiple scattering effect of spaceborne lidar and improve the retrieval accuracy of the IOPs of seawater using spaceborne lidar.

An Ocean Color Algorithm Based on Power Functions to Retrieve Inherent Optical Properties From Remotely Sensed Data Off the Baja California Peninsula, Mexico

Ocean color is determined by the different components of seawater. Satellite ocean color data are commonly used to infer the inherent optical properties (IOPs) of seawater and concentration of chlorophyll $a$ (Chl- $a$ ). Many of the algorithms that relate satellite data to the components of seawater use global data to determine their parameters. Regionalizing the algorithms is recommended to improve the retrieval of IOPs from satellite data. In this article, we use the in situ and satellite data from the southern part of the California Current System (CCS) off the Baja California Peninsula during 1999 to test the Garver and Siegel IOPs’ inversion model (GSM). The strong seasonal variability observed in the data was not reproduced by this algorithm. Thus, we propose a new regional algorithm whose order and coefficient depend on seasonal variability. This algorithm includes the linear form of the GSM model but allows changes in two parameters according to the season. These two parameters are the magnitude and spectral shape of the relationship of the remote sensing reflectance with the ratio of the backscattering coefficient and the sum of backscattering and absorption coefficients. In this area, both parameters increase gradually from January to October. The new algorithm improves the retrieval of the IOPs’ ratio from an ${r}^{2}$ below 0.5 with the GSM model to an ${r}^{2}$ that ranges from 0.87 in April and October to 0.93 and 0.94 in January and August, respectively. Furthermore, Chl- $a$ data were used as a proxy to evaluate the new algorithm. Absorption data retrieved from remotely sensed data for 1999, 2008, and 2011 relate well to the in situ Chl- $a$ data.

On the Adequacy of Representing Water Reflectance by Semi-Analytical Models in Ocean Color Remote Sensing

Deterministic or statistical inversion schemes to retrieve ocean color from space often use a simplified water reflectance model that may introduce unrealistic constraints on the solution, a disadvantage compared with standard, two-step algorithms that make minimal assumptions about the water signal. In view of this, the semi-analytical models of Morel and Maritorena (2001), MM01, and Park and Ruddick (2005), PR05, used in the spectral matching POLYMER algorithm (Steinmetz et al., 2011), are examined in terms of their ability to restitute properly, i.e., with sufficient accuracy, water reflectance. The approach is to infer water reflectance at MODIS wavelengths, as in POLYMER, from theoretical simulations (using Hydrolight with fluorescence and Raman scattering) and, separately, from measurements (AERONET-OC network). A wide range of Case 1 and Case 2 waters, except extremely turbid waters, are included in the simulations and sampled in the measurements. The reflectance model parameters that give the best fit with the simulated data or the measurements are determined. The accuracy of the reconstructed water reflectance and its effect on the retrieval of inherent optical properties (IOPs) is quantified. The impact of cloud and aerosol transmittance, fixed to unity in the POLYMER scheme, on model performance is also evaluated. Agreement is generally good between model results and Hydrolight simulations or AERONET-OC values, even in optically complex waters, with discrepancies much smaller than typical atmospheric correction errors. Significant differences exist in some cases, but having a more intricate model (i.e., using more parameters) makes convergence more difficult. The trade-off is between efficiency/robustness and accuracy. Notable errors are obtained when using the model estimates to retrieve IOPs. Importantly, the model parameters that best fit the input data, in particular chlorophyll-a concentration, do not represent adequately actual values. The reconstructed water reflectance should be used in bio-optical algorithms. While neglecting cloud and aerosol transmittances degrades the accuracy of the reconstructed water reflectance and the retrieved IOPs, it negligibly affects water reflectance ratios and, therefore, any variable derived from such ratios.

Impacts of pure seawater absorption coefficient on remotely sensed inherent optical properties in oligotrophic waters.

The spectral absorption coefficient of pure seawater (aw(λ)) in published studies differ significantly in the blue domain, yet the impacts of such discrepancies on the inherent optical properties (IOPs) derived from ocean color have been scarcely documented. In this study, we confirm that changes in aw(λ) may have significant impacts on retrieved IOPs in oligotrophic waters, especially for the phytoplankton absorption coefficient (aph(λ)). Two sets of aw(λ) data, aw_PF97 (Appl. Opt. 36, 8710, 1997) and aw_Lee15 (Appl. Opt. 54, 546, 2015), were selected for optical inversion analysis. It is found that aph(λ) retrieved with aw_Lee15 agree better with the in-situ measurements in oligotrophic waters. Further applications to satellite images show that the derived aph(λ) using aw_Lee15 can be up to 238% higher than the retrievals using aw_PF97 in the core zone of the subtropical ocean gyres. Given that aw_PF97 is commonly accepted as the "standard" aw(λ) by the ocean color community in the past decades, this study highlights the need and importance to update aw(λ) with aw_Lee15 for IOPs retrievals in oligotrophic waters.

A blended inherent optical property algorithm for global satellite ocean color observations

AbstractWater inherent optical properties (IOPs) can be derived from satellite‐measured normalized water‐leaving radiance (nLw(λ)) spectra. In this study, we evaluate the performance of the quasi‐analytical algorithm (QAA) and the near‐infrared (NIR)‐based IOP algorithm using a Hydrolight simulation data set covering a wide range of water types that span from clear open ocean to turbid coastal/inland waters. The NIR‐based algorithm produces significantly improved IOP retrievals over turbid coastal and inland waters, while the QAA algorithm performs well in the open ocean and less turbid coastal waters. Based on the advantages of the NIR‐based and QAA‐based algorithms, a combination of the NIR‐ and QAA‐based algorithm has been proposed using satellite‐measured nLw(745) as the threshold in order to produce accurate IOP products for both the open ocean and turbid coastal/inland waters. The new combined IOP algorithm can produce reasonably accurate IOP data for all water types, and can be easily implemented into the satellite ocean color data processing. The La Plata River Estuary region is used as an example to show the difference in performance of IOP retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements between 2012 and 2017 with the NIR‐based, QAA‐based, and NIR‐QAA combined IOP algorithms. We also demonstrate that the NIR‐QAA combined algorithm can be applied to VIIRS global ocean color observations to derive good quality IOP products in China's east coastal region, the US east coastal region, and the region of Mississippi River Estuary and tributaries.

Effects of forward models on the semi-analytical retrieval of inherent optical properties from remote sensing reflectance.

Inherent optical properties (IOPs) play a key role in modulating an aquatic light field; they are the core link for remotely sensing water constituents based on ocean color remote sensing. Many semi-analytical algorithms (SAAs) have been developed to obtain IOPs from remote sensing reflectance (Rrs) data; these algorithms require a forward model (FM) to link the IOPs to Rrs. Most currently available SAAs use the FM presented by Gordon etal.[J. Geophys. Res.93, 10909 (1988)JGREA20148-022710.1029/JD093iD09p10909] (G88 hereafter) without knowledge of how other models would impact the retrieval of IOPs from Rrs. This study evaluates the effects of two popular SAAs, namely, the quasi-analytical algorithm (QAA) and the generalized IOP algorithm (GIOP), combined with six different FMs on the retrieval of IOPs from a synthetic data set generated with Hydrolight software. The results indicated that different FMs can have quite different effects on the computed Rrs(λ), and the effects were not uniform across the Rrs spectrum. Of the six FMs tested, G88 and P05 [Appl. Opt.44, 1236 (2005)APOPAI0003-693510.1364/AO.44.001236] produced the best estimates of Rrs(λ) at 350, 440, and 550nm in both oceanic and coastal sub-datasets; they also were less impacted by changes in the particle phase function. M02 also produced a good estimation of Rrs but only at 440nm, and L04 performed well only in the oceanic condition. When the two SAAs were combined with the six FMs, in the oceanic condition, QAA and GIOP combined with M02 (QM02 and GM02) provided better quality for the absorption coefficient [a(λ)] at 350, 440, and 550nm when compared with the SAAs combined with the other models. However, for the retrieval of the particle backscattering coefficient [bbp(λ)] in the oceanic condition, QAA and GIOP combined with L04 (QL04 and GL04) performed better than the others, and GL04 always provided a better estimation of bbp(λ) than QL04. In the coastal condition, QAA and GIOP combined with G88 or P05 produced slightly better quality of IOPs compared with the other four FMs. Compared with GIOP in the coastal condition, QAA combined with G88 or P05 always showed better quality of retrieval of a(λ) but weaker quality of retrieval of bbp(λ).

Sensitivity of inherent optical properties from ocean reflectance inversion models to satellite instrument wavelength suites.

The Earth science community seeks to develop climate data records (CDRs) from satellite measurements of ocean color, a continuous data record which now exceeds twenty years. Space agencies will launch additional instruments in the coming decade that will continue this data record, including the NASA PACE spectrometer. Inherent optical properties (IOPs) quantitatively describe the absorbing and scattering constituents of seawater and can be estimated from satellite-observed spectroradiometric data using semi-analytical algorithms (SAAs). SAAs exploit the contrasting optical signatures of constituent matter at spectral bands observed by satellite sensors. SAA performance, therefore, depends on the spectral resolution of the satellite spectroradiometer. A CDR spanning SeaWiFS, MODIS, OLCI, and PACE, for example, would include IOPs derived using varied wavelength suites if all available wavelengths were considered. Here, we explored differences in derived IOPs that stem simply from the use of (eight) different wavelength suites of input radiometric measurements. Using synthesized data and SeaWiFS Level-3 mission-long composites, we demonstrated equivalent SAA performance for all wavelength suites, but that IOP retrievals vary by several percent across wavelength suites and as a function of water type. The differences equate to roughly ≤ 6, 12, and 7% for adg (443), aph (443), and bbp (443), respectively, for waters with Ca ≤ 1 mg m-3. These values shrink for sensors with similar wavelength suites (e.g., SeaWiFS, MODIS, and MERIS) and rise to substantially larger values for higher Ca waters. Our results also indicate that including 400 nm (in the case of OLCI) influences the derived IOPs, using longer wavelengths (>600 nm) influences the derived IOPs when there is a red signal, and, including additional spectral information shows potential for improved IOP estimation, but not without revisiting SAA parameterizations and execution. While modest in scope, we believe this study contributes to the knowledge base for CDR development. The implication of ignoring such an analysis as CDRs continue to be developed is a prolonged inability to distinguish between algorithmic and environmental contributions to trends and anomalies in the IOP time-series.

Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China's three largest freshwater lakes

Inherent optical properties (IOPs) play an important role in underwater light field, and are difficult to estimate accurately using satellite data in optically complex waters. To study water quality in appropriate temporal and spatial scales, it is necessary to develop methods to obtain IOPs form space-based observation with quantified uncertainties. Field-measured IOP data (N = 405) were collected from 17 surveys between 2011 and 2017 in the three major largest freshwater lakes of China (Lake Chaohu, Lake Taihu, and Lake Hongze) in the lower reaches of the Yangtze River and Huai River (LYHR). Here we provide a case-study on how to use in-situ observation of IOPs to devise an improved algorithm for retrieval of IOPs. We then apply this algorithm to observation with Sentinel-3A OLCI (Ocean and Land Colour Instrument, corrected with our improved AC scheme), and use in-situ data to show that the algorithm performs better than the standard OLCI IOP product. We use the satellite derived products to study the spatial and seasonal distributions of IOPs and concentrations of optically active constituents in these three lakes, including chlorophyll-a (Chla) and suspended particulate matter (SPM), using all cloud-free OLCI images (115 scenes) over the lakes in the LYHR basin in 2017. Our study provides a strategy for using local and remote observations to obtain important water quality parameters necessary to manage resources such as reservoirs, lakes and coastal waters.

Deriving inherent optical properties from decomposition of hyperspectral non-water absorption

Semi-analytical algorithms (SAAs) developed for multispectral ocean color sensors have benefited from a variety of approaches for retrieving the magnitude and spectral shape of inherent optical properties (IOPs). SAAs generally follow two approaches: 1) simultaneous retrieval of all IOPs, resulting in pre-defined bio-optical models and spectral dependence between IOPs and 2) retrieval of bulk IOPs (absorption and backscattering) first followed by decomposition into separate components, allowing for independent retrievals of some components. Current algorithms used to decompose hyperspectral remotely-sensed reflectance into IOPs follow the first strategy. Here, a spectral deconvolution algorithm for incorporation into the second strategy is presented that decomposes at-w(λ) from in situ measurements and estimates absorption due to phytoplankton (aph(λ)) and colored detrital material (adg(λ)) free of explicit assumptions. The algorithm described here, Derivative Analysis and Iterative Spectral Evaluation of Absorption (DAISEA), provides estimates of aph(λ) and adg(λ) over a spectral range from 350 to 700 nm. Estimated aph(λ) and adg(λ) showed an average normalized root mean square difference of <30% and <20%, respectively, from 350 to 650 nm for the majority of optically distinct environments considered. Estimated Sdg median difference was <20% for all environments considered, while distribution of Sdg uncertainty suggests that biogeochemical variability represented by Sdg can be estimated free of bias. DAISEA results suggest that hyperspectral satellite ocean color data will improve our ability to track biogeochemical processes affiliated with variability in adg(λ) and Sdg free of explicit assumptions.

Relationship between the effective attenuation coefficient of spaceborne lidar signal and the IOPs of seawater.

Multiple scattering is an inevitable effect in spaceborne oceanic lidar because of the large footprint size and the high optical density of seawater. The effective attenuation coefficient klidar in the oceanic lidar equation, which indicates the influence of the multiple scattering effect on the formation of lidar returns, is an important parameter in the retrieval of inherent optical properties (IOPs) of seawater. In this paper, the relationships between klidar of the spaceborne lidar signal and the IOPs of seawater are investigated by solving the radiative transfer equation with an improved semianalytic Monte Carlo model. Apart from the geometric loss factors, klidar is found to decrease exponentially with the increase of depth in homogeneous waters. klidar is given as an exponential function of depth and IOPs of seawater. The mean percentage errors between klidar calculated by the exponential function and the simulated ones in three typical stratified waters are within 0.5%, proving the effectiveness and applicability of this klidar-IOPs function. The results in this paper can help researchers have a better understanding of the multiple scattering effect of spaceborne lidar and improve the retrieval accuracy of the IOPs and the chlorophyll concentration of case 1 water from spaceborne lidar measurements.

Multi-band spectral matching inversion algorithm to derive water column properties in optically shallow waters: An optimization of parameterization

Deriving inherent optical properties (IOPs) and other water quality parameters from satellite remote sensing data covering optically shallow environments has historically been problematic due to difficulties in separating the benthic signal from that of the water column. While recent advances have improved such retrievals, most methods have high uncertainties for very shallow (<5m) or very bright (e.g., carbonate sand) targets. Here, we present a two-stage process to improve IOP derivations from satellite-derived reflectance data, termed the Shallow Water Optimization with Resolved Depth (SWORD). Within this process, a raster bathymetry is first derived through multiple pixel-wise implementations of a spectral matching algorithm on mapped reflectance data. This bathymetry is then used as a fixed input in subsequent implementations of the algorithm, leading to improved IOP retrievals. The SWORD approach was developed and tested using a dataset of simulated reflectance spectra as well as MERIS reflectance data covering two optically shallow water environments. Bathymetries derived using this process showed strong agreement with those determined from soundings and coastal relief models. Although SWORD-derived raster albedo maps showed general concordance to benthic habitat surveys, we found limited benefit of fixing this parameter in spectral matching routines. IOP derivations from SWORD show expected spatiotemporal patterns in the Florida Keys region, consistent with local hydrodynamic processes, seasonal fluctuations, and known anomalous events. This approach is portable to multispectral reflectance data from similar satellite instruments, allowing regular and ongoing assessment and monitoring of optical water quality for ecologically and economically important marine systems.

Improving satellite data products for open oceans with a scheme to correct the residual errors in remote sensing reflectance

AbstractAn approach to semianalytically derive waters' inherent optical properties (IOPs) from remote sensing reflectance (Rrs) and at the same time to take into account the residual errors in satellite Rrs is developed for open‐ocean clear waters where aerosols are likely of marine origin. This approach has two components: (1) a scheme of combining a neural network and an algebraic solution for the derivation of IOPs, and (2) relationships between Rrs residual errors at 670 nm and other spectral bands. This approach is evaluated with both synthetic and Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) data, and the results show that it can significantly reduce the effects of residual errors in Rrs on the retrieval of IOPs, and at the same time remove partially the Rrs residual errors for “low‐quality” and “high‐quality” data defined in this study. Furthermore, more consistent estimation of chlorophyll concentrations between the empirical blue‐green ratio and band‐difference algorithms can be derived from the corrected “low‐quality” and “high‐quality” Rrs. These results suggest that it is possible to improve both data quality and quantity of satellite‐retrieved Rrs over clear open‐ocean waters with a step considering the spectral relationships of the residual errors in Rrs after the default atmospheric correction procedure and without fixing Rrs at 670 nm to one value for clear waters in a small region such as 3 × 3 box.

On the modeling of hyperspectral remote-sensing reflectance of high-sediment-load waters in the visible to shortwave-infrared domain.

We evaluated three key components in modeling hyperspectral remote-sensing reflectance in the visible to shortwave-infrared (Vis-SWIR) domain of high-sediment-load (HSL) waters, which are the relationship between remote-sensing reflectance (R(rs)) and inherent optical properties (IOPs), the absorption coefficient spectrum of pure water (a(w)) in the IR-SWIR region, and the spectral variation of sediment absorption coefficient (a(sed)). Results from this study indicate that it is necessary to use a more generalized R(rs)-IOP model to describe the spectral variation of R(rs) of HSL waters from Vis to SWIR; otherwise it may result in a spectrally distorted R(rs) spectrum if a constant model parameter is used. For hyperspectral a(w) in the IR-SWIR domain, the values reported in Kou et al. (1993) provided a much better match with the spectral variation of R(rs) in this spectral range compared to that of Segelstein (1981). For a(sed) spectrum, an empirical a(sed) spectral shape derived from sample measurements is found working much better than the traditional exponential-decay function of wavelength in modeling the spectral variation of R(rs) in the visible domain. These results would improve our understanding of the spectral signatures of R(rs) of HSL waters in the Vis-SWIR domain and subsequently improve the retrieval of IOPs from ocean color remote sensing, which could further help the estimation of sediment loading of such waters. Limitations in estimating chlorophyll concentration in such waters are also discussed.

Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments

Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr &lt; 0.0005) of bottom reflectance on the spectrally-averaged remote sensing reflectance signal at depths &gt;17 m for MODIS and &gt;19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments.