Hyperspectral Radiometer Research Articles

Performance of operational satellite bio-optical algorithms in different water types in the southeastern Arabian Sea

Summary The in situ remote sensing reflectance (Rrs) and optically active substances (OAS) measured using hyperspectral radiometer, were used for optical classification of coastal waters in the southeastern Arabian Sea. The spectral Rrs showed three distinct water types, that were associated with the variability in OAS such as chlorophyll-a (chl-a), chromophoric dissolved organic matter (CDOM) and volume scattering function at 650 nm (β650). The water types were classified as Type-I, Type-II and Type-III respectively for the three Rrs spectra. The Type-I waters showed the peak Rrs in the blue band (470 nm), whereas in the case of Type-II and III waters the peak Rrs was at 560 and 570 nm respectively. The shifting of the peak Rrs at the longer wavelength was due to an increase in concentration of OAS. Further, we evaluated six bio-optical algorithms (OC3C, OC4O, OC4, OC4E, OC3M and OC4O2) used operationally to retrieve chl-a from Coastal Zone Colour Scanner (CZCS), Ocean Colour Temperature Scanner (OCTS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean Colour Monitor (OCM2). For chl-a concentration greater than 1.0 mg m−3, algorithms based on the reference band ratios 488/510/520 nm to 547/550/555/560/565 nm have to be considered. The assessment of algorithms showed better performance of OC3M and OC4. All the algorithms exhibited better performance in Type-I waters. However, the performance was poor in Type-II and Type-III waters which could be attributed to the significant co-variance of chl-a with CDOM.

Stray light effects in above-water remote-sensing reflectance from hyperspectral radiometers.

Stray light perturbations are unwanted distortions of the measured spectrum due to the nonideal performance of optical radiometers. Because of this, stray light characterization and correction is essential when accurate radiometric measurements are a necessity. In agreement with such a need, this study focused on stray light correction of hyperspectral radiometers widely applied for above-water measurements to determine the remote-sensing reflectance (RRS). Stray light of sample radiometers was experimentally characterized and a correction algorithm was developed and applied to field measurements performed in the Mediterranean Sea. Results indicate that mean stray light corrections are appreciable, with values generally varying from -1% to +1% in the 400-700 nm spectral region for downward irradiance and sky radiance, and from -1% to +4% for total radiance from the sea. Mean corrections for data products such as RRS exhibit values that depend on water type varying between -0.5% and +1% in the blue-green spectral region, with peaks up to 9% in the red in eutrophic waters. The possibility of using one common stray light correction matrix for the analyzed class of radiometers was also investigated. Results centered on RRS support such a feasibility at the expense of an increment of the uncertainty typically well below 0.5% in the blue-green and up to 1% in the red, assuming sensors are based on spectrographs from the same production batch.

Estimating the CDOM absorption coefficient in tropical inland waters using OLI/Landsat-8 images

ABSTRACTColoured dissolved organic matter (CDOM) is the most abundant dissolved organic matter (DOM) in many natural waters and can affect the water quality, such as the light penetration and the thermal properties of water system. So the objective of this letter was to estimate the CDOM absorption coefficient at 440 nm, aCDOM(440), in Barra Bonita Reservoir (São Paulo State, Brazil) using operational land imager (OLI)/Landsat-8 images. For this two field campaigns were conducted in May and October 2014. During the field campaigns remote sensing reflectance (Rrs) were measured using a TriOS hyperspectral radiometer. Water samples were collected and analysed to obtain the aCDOM(440). To predict the aCDOM(440) from Rrs at two key wavelengths (650 and 480 nm) were regressed against laboratory-derived aCDOM(440) values. The validation using in situ data of aCDOM(440) algorithm indicated a goodness of fit, R2 = 0.70, with a root mean square error (RMSE) of 10.65%. The developed algorithm was applied to the OLI/Lansat-8 images. Distribution maps were created with OLI/Landsat-8 images based on the adjusted algorithm.

The Potential of Autonomous Ship-Borne Hyperspectral Radiometers for the Validation of Ocean Color Radiometry Data

Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia’s ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral data can be re-sampled for comparison with current and future sensor wavebands. The continuous spectral acquisition along the ship track enables spatial resampling to match satellite footprint. This study reports spectral comparisons of the radiometer data with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua for contrasting water types in tropical waters off northern Australia based on the standard NIR atmospheric correction implemented in SeaDAS. Consistent match-ups are shown for transects of up to 50 km over a range of reflectance values. The MODIS and VIIRS satellite reflectance data consistently underestimated the in situ spectra in the blue with a bias relative to the “dynamic above water radiance and irradiance collector” (DALEC) at 443 nm ranging from 9.8 × 10−4 to 3.1 × 10−3 sr−1. Automated acquisition has produced good quality data under standard operating and maintenance procedures. A sensitivity analysis explored the effects of some assumptions in the data reduction methods, indicating the need for a comprehensive investigation and quantification of each source of uncertainty in the estimate of the DALEC reflectances. Deployment on a Research Vessel provides the potential for the radiometric data to be combined with other sampling and observational activities to contribute to algorithm development in the wider bio-optical research community.

A quantitative blueness index for oligotrophic waters: Application to Lake Tahoe, California–Nevada

AbstractThe perceived blue color of a lake often contributes to its aesthetic appeal, and changes in blueness can be indicative of major shifts in water quality. We developed a quantitative blue water index (Bw) for natural waters, and used it to evaluate spatial and seasonal variations in ultraoligotrophic Lake Tahoe, where clarity and blueness are of ecological and economic value and a focus for lake management strategies. Spectral reflectance was measured using a profiling hyperspectral radiometer, and the values were converted to the axis values of a color space: the International Commission on Illumination L*a*b*, where L* is the lightness of color, a* ranges from green to magenta, and b* ranges from blue to yellow. The blue water index Bw, defined as negative b*, was similarly high at two offshore monitoring sites in Lake Tahoe, but much lower in a semienclosed bay and a small adjacent lake. Seasonal variations of Bw were determined using a hyperspectral radiometer attached to a buoy in the middle of Lake Tahoe. The Bw values were highest in summer, and there was a strong inverse correlation between Bw and phytoplankton concentrations as measured by in vivo chlorophyll a fluorescence. However, there was no significant correlation between Bw and Secchi depth. Blueness and visual clarity are complementary measures of the perceived optical state of natural waters, and for many lakes may provide a powerful combination of indicators for conveying water quality to the public.

Species identification of mixed algal bloom in the Northern Arabian Sea using remote sensing techniques.

Oceanic waters of the Northern Arabian Sea experience massive algal blooms during winter-spring (mid Feb-end Mar), which prevail for at least for 3 months covering the entire northern half of the basin from east to west. Ship cruises were conducted during winter-spring of 2001-2012 covering different stages of the bloom to study the biogeochemistry of the region. Phytoplankton analysis indicated the presence of green tides of dinoflagellate, Noctiluca scintillans (=N. miliaris), in the oceanic waters. Our observations indicated that diatoms are coupled and often co-exist with N. scintillans, making it a mixed-species ecosystem. In this paper, we describe an approach for detection of bloom-forming algae N. scintillans and its discrimination from diatoms using Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua data in a mixed-species environment. In situ remote sensing reflectance spectra were generated using Satlantic™ hyperspectral radiometer for the bloom and non-bloom waters. Spectral shapes of the reflectance spectra for different water types were distinct, and the same were used for species identification. Scatter of points representing different phytoplankton classes on a derivative plot revealed four diverse clusters, viz. N. scintillans, diatoms, non-bloom oceanic, and non-bloom coastal waters. The criteria developed for species discrimination were implemented on MODIS data and validated using inputs from a recent ship cruise conducted in March 2013.

Retrieval Analysis of Atmospheric Water Vapor for K-Band Ground-Based Hyperspectral Microwave Radiometer

In this letter, we study the performance of a K-band ground-based hyperspectral microwave radiometer for the observation of atmospheric water vapor. First, a prototype of a K-band ground-based hyperspectral microwave radiometer for atmospheric sounding is proposed. This microwave radiometer is able to split the 18–26-GHz signal into 80 hyperspectral channels with identical bandwidth. Simulation studies, including the retrieval performance of water vapor and the vertical resolution of observation compared with the five-humidity-channel radiometer TP/WVP-3000 under the same conditions, are presented to assess the capability of the prototype. Simulation results show that the vertical resolution of this prototype is better than that of TP/WVP-3000 at a higher altitude, and the RMS water vapor error improves by about 10% at an altitude of 0–6 km. Moreover, by simulation, we analyze the impact of the radiometer channel number on the Shannon information gain and the RMS water vapor error of the hyperspectral microwave radiometer. At an altitude of 1.5–6 km, more information can be obtained by increasing the number of microwave spectrum channels. For water vapor profiling, the improvement of the retrieval RMS error from 10 to 800 channels at a higher altitude exceeds about 5%–10%.

Alteration information extraction using improved relative absorption band-depth images, from HJ-1A HSI data: a case study in Xinjiang Hatu gold ore district

On the basis of analysing the deficiency of the relative absorption band-depth (RBD) approach, we present an improved similarity measure called weighted RBD (WRBD) applied to the extraction of iron-stained alteration information from Huan Jing-1A (HJ-1A) Hyperspectral Imaging Radiometer (HSI) data. By introducing the continuum removed reflectance (CRR) and the full width at half maximum (FWHM), the WRBD approach fully considered the non-symmetry of the reflection peaks and the effective bandwidth of each band spectrum. The results of Hatu gold ore district HSI data experiments show that using the improved WRBD images to extract alteration information improves user’s accuracy by 45%, although it is 14% lower in producer’s accuracy, compared with the RBD approach. The improved approach can more accurately reflect the alteration state and reduce exploration costs.

Recording of selected effects and hazards caused by current and expected storm events in the Baltic Sea coastal zone

Furmańczyk, K., Andrzejewski, P., Benedyczak, R., Bugajny, N., Cieszyński, Ł., Dudzińska-Nowak, J., Giza, A., Paprotny, D., Terefenko, P., Zawiślak, T., 2014. Recording of selected effects and hazards caused by current and expected storm events in the Baltic Sea coastal zone. In: Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th International Coastal Symposium (Durban, South Africa), Journal of Coastal Research, Special Issue No. 70, pp. 338–342, ISSN 0749-0208.Routine monitoring has already become a legal obligation and apparent necessity for EU members, especially of such dynamic and important habitat as the Baltic Sea and its shores. One of the subsystems of SatBałtyk, a Polish research project for maritime observation, is dedicated to coastal monitoring. It focuses on recording and forecasting the consequences of storm events along the Baltic's Polish shoreline. Three sites (7–12 km-long sections of dune coast) were selected for detailed research: the Dziwnow Spit, Jamno and Bukowo lakes' spits and part of the Hel Peninsula. Work started in 2010 and will continue until 2015. A numerical model, XBeach, was implemented in order to provide forecasts of several parameters, i.e. beach flooding, dune erosion, suspension of matter in the water after storm and rip current occurrence. It combines external data with in situ measurements by the researchers. Many pieces of equipment were installed for this research: a satellite receiving station, underwater probe ADCP (Acoustic Doppler Current Profiler), a tide gauge, a hyperspectral radiometer and two video cameras for constant shoreline observation. The system is currently being calibrated and verified; an online visualisation available to the public is due to be launched as a long-lasting outcome of the project.

Studies on Textural and Compositional Characteristics of Sand and Clay Mixtures Using Hyperspectral Radiometry

This paper examines the hyperspectral signatures (in the Visible Near Infrared (VNIR)-Shortwave Infrared (SWIR) regions) of soil samples with varying colour and minerals. 36 samples of sands (from river and beach) with differing clay contents were examined using a hyperspectral radiometer operating in the 350–2,500 nm range, and the spectral curves were obtained. Analysis of the spectra indicates that there is an overall increase in the reflectance in the VNIR-SWIR region with an increase in the content of kaolinite clay in the sand samples. As regards the red and black clays and sand mixtures, the overall reflectance increases with decreasing clay content. Several spectral parameters such as depth of absorption at 1,400 nm and 1,900 nm regions, radius of curvature of the absorption troughs, slope at a particular wavelength region and the peak reflectance values were derived. There exists a correlation between certain of these spectral parameters (depth, slope, position, peak reflectance, area under the curve and radius of the curve) and the compositional and textural parameters of the soils. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to identify the type of clay and estimate the clay content in a given soil and thus define its geotechnical category.

Using ocean colour remote sensing products to estimate turbidity at the Wadden Sea time series station Spiekeroog

Time series measurements at the Wadden Sea time series station Spiekeroog (WSS) in the southern North Sea were used to empirically develop approaches for determining turbidity from ocean colour remote sensing products (OCPs). Turbidity was observed by a submerged optical sensor. Radiometric quantities were collected using hyperspectral radiometers. Surface reflected glint correction was applied to the radiometric quantities to compute remote sensing reflectance (RRS) and the RRS was converted into perceived colour of seawater matching the Forel-Ule colour Index (FUI) scale. The empirical approaches for determining turbidity from OCPs showed good least squares linear correlations and statistical significance (R2 > 0.7, p < 0.001). These OCP approaches had relatively low uncertainties in predicting turbidity with encouraging mean absolute percent difference less than 31 %. The problem of bio-fouling on submerged sensors and the potential application of OCPs to monitor or correct for sensor drifts was evaluated. A protocol is proposed for the acquisition and processing of hyperspectral radiometric measurements at this optically complex station. Use of the classic FUI as a time series indicator of surface seawater changes did show promising results. The application of these OCPs in operational monitoring changes in water quality was also explored with the aim to evaluate the potential use of the WSS datasets in calibration and validation of satellite ocean colour remote sensing of these very turbid coastal waters.

Retrieval and Validation of Chlorophyll-a Concentrations in the Coastal Waters Off Yanam and Kakinada (Godavari) Basin Along East Coast of India

In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R2 value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R2 value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.

Genetic algorithm‐based partial least squares regression for estimating legume content in a grass‐legume mixture using field hyperspectral measurements

AbstractThis study investigated the ability of a field hyperspectral radiometer (400–2350 nm) and genetic algorithm‐based partial least squares (GA‐PLS) regression to estimate legume content in a mixed sown pasture in Hokkaido, Japan. Canopy reflectance data and plant samples were obtained from 50 selected sites in the spring (May) and summer (July) of 2007 (n = 100). The predictive accuracy of GA‐PLS was compared with that of multiple linear regression (MLR) and of standard full‐spectrum PLS (FS‐PLS) for the spring and summer datasets. Overall, the highest coefficient of determination (R2) and the lowest root mean squared error of cross validation (RMSECV) values were obtained in the GA‐PLS models for both datasets (R2 = 0.72–0.86, RMSECV = 4.10–5.73%). Selected hyperspectral wavebands in the GA‐PLS models did not perfectly match wavelengths identified previously using MLR, but in most cases, they were within 20 nm of previously known wavelength regions.

Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton

Phenacoccus solenopsis Tinsley, a native of North America, is a widespread exotic mealybug infesting cotton, Gossypium spp. in several countries. Monitoring of this pest is generally undertaken through regular field surveys, which is labour intensive, time consuming and error prone. Alternately, radiometry is a reliable technique for rapid and non-destructive assessment of plant health. Thus, a study was conducted to characterize reflectance spectra of cotton plants with known mealybug infestation levels (grade-0 is healthy and grade-4 is severe), and seek to identify specific narrow wavelengths sensitive to mealybug damage. Reflectance measurements were made in the spectral range of 350–2500nm using a hyperspectral radiometer. Significant differences were found in green, near infrared and short wave infrared spectral regions for plants with early stages of P. solenopsis infestation, and for plants showing higher grades of infestation these differences extended to all the regions except blue. A significant reduction in total chlorophyll (12.83–35.83%) and relative water content (1.93–23.49%) was observed in the infested plants. Reflectance sensitivity analysis of the hyperspectral data revealed wavelengths centered at 492, 550, 674, 768 and 1454nm as most sensitive to mealybug damage. Mealybug Stress Indices (MSIs) were developed using two or three wavelengths, tested using multinomial logistic regression (MLR) analysis and compared with other indices published earlier. Results showed that the MSIs were superior (R2=0.82) to all other spectral vegetation indices tested. Further, the proposed MLR models corresponding to each MSIs were validated using two independent field data sets. The overall percent correct classification of cotton plants into different mealybug damage severity grades was in the range of 38.3 and 54.9. High classification accuracy for grade-1 (81.8%) showed that models are capable of early detection of mealybug damage. Results of this study could suggest potential usage of remote sensing in monitoring spatial and temporal distribution of the solenopsis mealybug, and thereby enable effective planning and implementation of site-specific pest management practices.

A study on the hyperspectral signatures of sandy soils with varying texture and water content

This paper examines the hyperspectral signatures (in the visible near-infrared (VNIR)–shortwave infrared (SWIR) regions) of different samples of sandy soils possessing varying grain size and water content. Ten samples of sandy soils with differing textures and water contents were examined using a hyperspectral radiometer operating in the 350–2,500-nm range, and the spectral curves were obtained. Analyses of the curves indicate that grain size of sand has a considerable influence on the spectral response in the visible, NIR and SWIR regions, i.e. there is a decrease in the overall reflectance with increase in grain size. While the 350–575-nm region exhibits maximum overlap of spectral reflectance and, hence, least spectral separability between the soil types, the 2,000–2,100-nm region exhibits highest spectral separability between the sand samples of different textures. As regards the water content, it is seen that overall reflectance increases with decreasing water content in the sample, especially in the 1,400–1,800-nm regions. Further, the slope of the curves in the 1,890–2,100-nm region shows a well-defined relationship with the water content. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to estimate the texture and water content of sandy soils.

Predicting salinity in tomato using soil reflectance spectra

Soil salinity is one of the most common soil degradation processes, found particularly in both arid and semi-arid areas. Salt (Cl)- and sodium (Na)-affected soils impact vegetation and plant communities. Under these conditions, soil salinity can serve as an indicator of vegetation salinity. In this study, we explored whether spectroscopy could quantitatively assess foliar Cl and Na concentration as indicators to assess salinity in tomato plants. Reflectance spectra of soil samples were obtained in the 400–2500 nm region using a hyperspectral radiometer. The relationship between the Na and Cl contents of tomato plants growing in various saline environments and soil spectral reflectance was determined using partial least squares regression. The Cl-content model was more accurate for determining leaf salinity (R 2 = 0.92, root mean square error of prediction (RMSEP) = 0.2%) than the Na-content model (R 2 = 0.87, RMSEP = 0.6%). We conclude that reflectance spectroscopy is potentially useful for characterizing the key properties of salinity in growing vegetation and assessing its salt quality. The results of this study can serve as a starting point in precision agriculture for salinity measurements in tomato fields and could be further upgraded for use by airborne/satellite remote-sensing modes.

Remote identification of the invasive tunicate Didemnum vexillum using reflectance spectroscopy

Benthic coverage of the invasive tunicate Didemnum vexillum on Georges Bank is largely unknown. Monitoring of D. vexillum coverage is vital to understanding the impact this invasive species will have on the productive fishing grounds of Georges Bank. Here we investigate using reflectance spectroscopy as a method for remote identification of D. vexillum. Using two different systems, a NightSea Dive-Spec and a combination of LED light sources with a hyperspectral radiometer, we collected in-situ measurements of reflectance from D. vexillum colonies. In comparison to reflectance spectra of other common benthic substrates, D. vexillum appears to have a unique spectral signature between 500 and 600 nm. Measuring the slope of the spectrum between these wavelengths appears to be the most robust method for spectral identification. Using derivative analysis or principal component analysis, the reflectance spectra of D. vexillum can be identified among numerous other spectra of common benthic substrates. An optical system consisting of a radiometer, light source, and camera was deployed on a remotely operated vehicle to test the feasibility of using reflectance to assess D. vexillum coverage. Preliminary results, analyzed here, prove the method to be successful for the areas we surveyed and open the way for its use on large-scale surveys.

Radiometric Calibration Accuracy of GOES Sounder Infrared Channels

Quality infrared (IR) radiances and their derived products from the Geostationary Environmental Operational Satellite (GOES) Sounder are very important data sources to weather prediction and nowcasting applications for the continental United States and adjacent ocean regions. With demanding requirements for more accurate weather nowcasting models and climate change studies, it is necessary to assess and improve the radiometric calibration accuracy of the GOES Sounder data. The objective of this paper is to examine the GOES Sounder IR radiometric calibration accuracy, and the diurnal calibration variation using intercalibration with two well-calibrated hyperspectral radiometers onboard low earth orbit satellites, the Atmospheric Infrared Sounder (AIRS) on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop-A satellite. The results show that most sounder IR channels of GOES-11 through GOES-15 are well calibrated outside of the satellite midnight effect time period, with a less than 0.5 K of mean bias of brightness temperature with respect to IASI. Yet, the impact of the satellite midnight effect on the radiance quality varies greatly at different IR channels among different satellites. Further research is needed to understand the changes of instrument environmental flux on the GOES IR radiance around satellite midnight.

Changes in the spectral features of fuel layers of an Australian dry sclerophyll forest in response to prescribed burning

Prescribed burning is a landscape management tool often used for asset protection and ecological maintenance. Accordingly, there is a need to understand the effects fire has on the landscape and how these changes might be measured. Remote sensing pre- and post-burn has the potential to inform decisions about burn severity and ecosystem sensitivity to fire. The aim of this research was to identify changes in the electromagnetic radiation (EMR) following a prescribed burn in the fuel layers of an Australian dry sclerophyll forest using a hyperspectral radiometer (HSR). Results indicated three major changes in spectral features (1) absence of the green reflectance peak (550nm), (2) flattening or absence of red edge (680–750nm) and (3) disappearance of water absorption feature (970nm). The greatest difference in the intensity and shape of spectral signatures from pre-burn levels for all the targets occurred within the first 2 weeks post-burn. The trend of a return to the pre-burn spectral signature was seen to occur from week 5 onwards for most targets. These findings have important implications for identifying suitable remote sensing parameters for monitoring the effects of fire on vegetation.

Remote sensing reflectance in the Great Lakes: In situ measurements, closure analyses, and a forward model

Observations of remote sensing reflectance (Rrs), the signal available to support remote sensing of optically active constituents (OACs) of water quality interest, are presented for multiple sites within each of the Laurentian Great Lakes based on in situ measurements made with a hyperspectral radiometer. Rrs(λ) spectra are contrasted among these lakes and in time and space within selected systems. Qualitative analyses of spectra are provided that identify the inherent optical property (IOP) and coupled OAC conditions responsible for the differences in Rrs(λ). The much higher Rrs peaks observed in the green wavelengths for the lower Great Lakes (Erie and Ontario) are attributed to elevated backscattering levels caused by higher concentrations of minerogenic particles. The credibility of the Rrs(λ) spectra is established through successful closure analyses that demonstrate good matches with IOP-based predictions and consistency of coefficient values for radiative transfer expressions with related literature and theory. A mechanistic forward model of Rrs(λ) is developed that accommodates the effects of three OACs, including metrics of phytoplankton biomass, minerogenic particles and colored dissolved organic material. This includes the development of the critical cross-section relationships that quantify the couplings between the OACs and IOPs, and in turn the IOPs and the Rrs(λ) signal. The model is demonstrated to perform well in matching observations in Lake Erie, and to be sensitive to the representation of the spectral dependency of backscattering and likely variations in the dependence of phytoplankton absorption on chlorophyll. The modeled predicted responses of Lake Erie to different OAC levels are presented.