Blue-green Ratio Research Articles

A blue–green ratio of urban wetlands as an ecosystem health indicator: the case of urban sprawl in Nagpur, India

ABSTRACT Rampant urban sprawl has affected the structure and function of existing urban green and blue spaces, exposing developing cities to unexpected disaster events. Drawing on a mixture of secondary information supported by GIS and field-based studies, we assessed the impact of growing impervious surfaces on the dynamic zones of urban wetlands in Nagpur, India. We compared decadal data (2000-2020) to understand the pattern of wetland loss and its drivers in the region. Our findings reveal a significant loss of wetland structure and area over the past two decades, raising alarming concerns regarding ecological and economic repercussions. To address these issues, we propose a blue–green buffer-based assessment approach that incorporates local knowledge and observations along with proactive monitoring of ecosystem health. Our study highlights the urgency of reducing the spatial complexity of landscape patches, restoring wetland buffers, declaring wetland buffers as no-go zones, and promoting corridor connectivity to urban wetlands to improve urban blue infrastructure health. Multi-sectoral and stakeholder cooperation, involvement, and strict enforcement of the Wetland Rules (2017) by local governing bodies can help build urban resilience by protecting urban blue–green infrastructure.

On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R2 ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m2 during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R2 (0.67) and lowest RMSE (83.13 g/m2) and MAE (48.13 g/m2) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m2 but decreased for biomass greater than 200 g/m2. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R2 and RMSE of the models reaching up to 0.82 and 61.67 g/m2 respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.

Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Spectral remote sensing reflectance, Rrs(λ) (sr-1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rrs. Furthermore, since the associated algorithms require Rrs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in Rrs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into Rrs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in Rrs. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in Rrs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chla, mg/m3) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd(490), m-1). Accounting for the error covariance in Rrs generally reduces the estimated relative uncertainty in chla by ∼1-2% (absolute value) in waters with chla < 0.25 mg/m3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chla > 0.35 mg/m3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For Kd(490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in Rrs is further verified through forward-calculating chla from MODIS-retrieved and in situ Rrs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chla can be achieved over deep ocean waters (chla ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in Rrs, some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Remote sensing water quality inversion using sparse representation: Chlorophyll-a retrieval from Sentinel-2 MSI data

Remote sensing has been widely applied to estimate the water quality parameters using empirical approaches. Numerous algorithms have been developed for water quality retrieval. However, the identification of key features for inversion and the reduction of uncertainty with limited field measurements are crucial aspects. In this study, satellite-based sparse representation is developed as an optimization model to simultaneously determine the major spectral features, and estimate water quality maps under noisy environment. Result shows that the blue–green ratio is the important feature for estimation of chlorophyll-a (Chl-a) concentration, whereas the NIR-red algorithm is the better one with in retrieving Chl-a in a high concentration case. The Chl-a map is estimated by using main spectral features of Sentinel 2 MSI data constrained with observations (correlations between observations and estimations: over 0.9). The rapid mapping of Chl-a in inland water allows us to assess spatial distribution of the water quality. This study provides reliable and interpretable information for policymakers to implement effective water quality management practices.

Utilization of unmanned aerial vehicle (UAV) in identifying the characteristics of the riparian ecosystem of the Percut River, North Sumatra Province

The Percut River border area has changed its function. Changes in land use result in a decrease in the area and types of vegetation that grow on river borders. Considering that the area is quite long and inaccessible in some locations, it can identify riparian vegetation by utilizing remote sensing technology. This study aims to identify the characteristics of the Percut river riparian vegetation using a UAV. Eight vegetation indices were used to analyse land cover types in this riparian ecosystem, namely Green-red ratio (GR), Green-red vegetation index (GRVI), RGB-based vegetation index (RGBVI), Visible atmospherically resistant index (VARI), Simple blue-green ratio (BGI2), Excess green index (ExG), Normalized green-blue difference index (NGBDI) and Modified green-red vegetation index (MGRVI). The analysis of the vegetation index based on RGB images shows that not all indices used are good in separating vegetation from other land covers. Analysis of riparian vegetation characteristics based on the RGB index is recommended using the RGBVI index (RGB-based vegetation index).

Application of a PLS-Augmented ANN Model for Retrieving Chlorophyll-a from Hyperspectral Data in Case 2 Waters of the Western Basin of Lake Erie

We present results that demonstrate the utility of machine learning techniques that are based on partial least squares (PLS) and artificial neural networks (ANNs) for estimating low-moderate chlorophyll-a (chl-a) concentrations in the western basin of Lake Erie (WBLE). Previous ocean color studies have resulted in a large number of algorithms that are based on spectral indices to estimate water quality parameters (WQPs) such as chl-a concentration from remote sensing reflectance. However, these spectral index algorithms are based on reflectance features at specific wavelengths and do not take advantage of the wealth of spectral information that is contained in hyperspectral data, and are often not easily adaptable to waters with conditions that are different from those in the datasets that were used to originally calibrate the indices. Recently, there have been efforts to use machine learning techniques that are based on ANNs and PLS regression to exploit the spectral richness contained in hyperspectral data and retrieve WQPs. In this study, we have combined an ANN model with output from PLS regression to retrieve chl-a concentration from hyperspectral data in the WBLE. We compared the results from the PLS-ANN method to those that were obtained from a band-ratio algorithm that is based on reflectances in the blue and green spectral regions, a band ratio algorithm that is based on reflectances in the red and near-infrared (NIR) spectral regions, and a PLS-only approach. For a dataset that was collected in 2012, with chl-a concentrations ranging from 0.48 to 21.2 µg/L, the PLS-ANN method yielded a root mean square error (RMSE) of 1.22 µg/L, whereas the blue-green ratio algorithm yielded an RMSE of 1.75 µg/L, the NIR-red ratio algorithm yielded an RMSE of 1.95 µg/L, and the PLS-only approach yielded an RMSE of 1.95 µg/L. The PLS-ANN method takes advantage of the PLS regression to identify specific wavelengths that contain most information about the variation in chl-a concentration, minimize spectral collinearity and redundancy in the data, and simplify the neural network’s input structure. The better performance of the PLS-ANN method can also be attributed to the neural network’s ability to account for nonlinearity in the relationship between chl-a concentration and spectral reflectance. The results indicate that the PLS-ANN method can be reliably used to estimate and monitor low-moderate chl-a concentrations in optically complex waters.

Spatiotemporal effects of urban ecological land transitions to thermal environment change in mega-urban agglomeration

Urban ecological land transitions (UELTs) have far-reaching effects on the thermal environment, but their dynamic effects in urban agglomerations remain poorly understood. This study defines the UELTs concept and quantifies its spatiotemporal effects and driving mechanisms on land surface temperature interdecadal variations (LSTIVs) in the Guangdong–Hong Kong–Macao Greater Bay Area using remote sensing, fuzzy overlay, shape-weighted landscape evolution index, and Geodetector methods. The results showed that UELTs shifted from degradation, increasing pressure, and decreasing vegetation proportion in the central city to scattered restoration, pressure relief, and increasing vegetation proportion in 2010–2020. LSTIVs simultaneously transitioned from rapid growth and contiguous expansion to reduction and dispersion. Moreover, the contribution of UELTs to LSTIVs increased by 19.49% from 2000 to 2020, and gradually shifted from being driven by dominant transition (isolating and adjacent degradation) (mean q = 0.58) to recessive transition (increased population and construction land pressure) (mean q = 0.62), where q is the determinant power. Interactions between edge-expansion and infilling restoration with the blue–green ratio (BGR; i.e., ratio of waterbodies to vegetation), habitat quality, and population layout had significant effects on LSTIVs. In addition, the relative magnitude of the effect of UEL restoration–degradation and BGR on LSTIVs was not fixed, but rather related to their interaction effect and the urban agglomeration development stage. Therefore, in addition to promoting an increase in UEL, optimizing the landscape structure of UEL (e.g., increasing aggregation and connectivity, adjusting BGR) and UEL distribution with other human factors are also crucial to reduce the urban thermal environment.

A Semi-Empirical Chlorophyll-a Retrieval Algorithm Considering the Effects of Sun Glint, Bottom Reflectance, and Non-Algal Particles in the Optically Shallow Water Zones of Sanya Bay Using SPOT6 Data

Chlorophyll-a (Chl-a) concentration retrieval is essential for water quality monitoring, aquaculture, and guiding coastline infrastructure construction. Compared with common ocean color satellites, land observation satellites have the advantage of a higher resolution and more data sources for retrieving the concentration of Chl-a from optically shallow waters. However, the sun glint (Rsg), bottom reflectance (Rb), and non-algal particle (NAP) derived from terrigenous matter affect the accuracy of Chl-a concentration retrieval using land observation satellite image data. In this paper, we propose a semi-empirical algorithm based on the remote sensing reflectance (Rrs) of SPOT6 to retrieve the Chl-a concentration in Sanya Bay (SYB), considering the effect of Rsg, Rb, and NAP. In this semi-empirical algorithm, the Cox–Munk anisotropic model and radiative transfer model (RTM) were used to reduce the effects of Rsg and Rb on Rrs, and the Chl-a concentration was retrieved by the Chl-a absorption coefficient at 490 nm (aphy(490)) to remove the effect of NAP. The semi-empirical algorithm was in the form of Chl-a = 43.3[aphy(490)]1.454, where aphy (490) was calculated by the total absorption coefficient and the absorption coefficients of each component by empirical algorithms. The results of the Chl-a concentration retrieval show the following: (1) SPOT6 data are available for Chl-a retrieval using this semi-empirical algorithm in oligotrophic or mesotrophic coastal waters, and the accuracy of the algorithm can be improved by removing the effects of Rsg, Rb, and NAP (R2 from 0.71 to 0.93 and root mean square error (RMSE) from 0.23 to 0.11 ug/L); (2) empirical algorithms based on the blue-green band are suitable for oligotrophic or mesotrophic coastal waters, and the algorithm based on the blue-green band difference Chl-a index (DCI) has stronger anti-interference in terms of the effects of sun glint and bottom reflectance than the algorithm based on the blue-green ratio (BGr); (3) in the case of ignoring Rsg unrelated to inherent optical properties (IOPs), NAP is the biggest interference factor when &gt;9.5 mg/L and the effect of bottom reflectance should be considered when the water depth (H) &lt;5 m in SYB; and (4) the inherent optical properties of the waters in SYB are dominated by NAP (Chl-a = 0.2–2.6 ug/L and NAP = 2.2–30.1 mg/L), and the nutrients are concentrated by enclosed terrain and southeast current. This semi-empirical algorithm for Chl-a concentration retrieval has the potential to monitor Chl-a in oligotrophic and mesotrophic coastal waters using other land observation satellites (e.g., Landsat8 OLI, ASTER, and GaoFen2).

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.

Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions

Numerous empirical algorithms have been operationally used to retrieve global ocean chlorophyll-a (Chla) concentrations from ocean color satellite data, for example, the OC4V4 algorithm for sea-viewing wide field-of-view sensor and the OC3M algorithm for moderate-resolution imaging spectroradiometer. However, the algorithms have been established and validated based on in situ data mainly measured under low to moderate solar zenith angle (SZA) (<70 deg). Currently, with the development of geostationary satellite ocean color remote sensing, enabling observations from early morning to late afternoon, it has become necessary to know whether the empirical Chla algorithms can be applied to high SZA. The performances of seven widely used Chla algorithms (i.e., OC2, OC3M, OC3V, OC4V4, Clark, ocean-color index, and Yellow Sea Large Marine Ecosystem Ocean Color Work Group) under high SZAs were evaluated using the global in situ ocean color dataset (NASA bio-optical marine algorithm dataset). The results show that the performances of all seven algorithms decreased significantly under high SZAs compared with those under low to moderate SZAs. For instance, for the OC4V4 algorithm, the relative percent difference (RPD) and root-mean-square error (RMSE) were 13.78% and 1.66 μg/L for the whole dataset and 3.95% and 1.49 μg/L for SZAs ranging from 30 deg to 40 deg, respectively. However, RPD and RMSE values increased to 30.45% and 6.10 μg/L for SZAs larger than 70 deg. Adjusting the coefficients of the algorithms using the in situ dataset with high SZA can only slightly improve the performance. The bidirectional remote sensing reflectance can explain the underestimation of the retrieved Chla under high SZA, and the low sensitivity of the blue–green ratio is responsible for the relatively larger scatter between the retrieved and the in situ Chla under high SZA.

Performance of the MODIS FLH algorithm in estuarine waters: a multi-year (2003–2010) analysis from Tampa Bay, Florida (USA)

Although satellite technology promises great usefulness for the consistent monitoring of chlorophyll-α concentration in estuarine and coastal waters, the complex optical properties commonly found in these types of waters seriously challenge the application of this technology. Blue–green ratio algorithms are susceptible to interference from water constituents, different from phytoplankton, which dominate the remote-sensing signal. Alternatively, modelling and laboratory studies have not shown a decisive position on the use of near-infrared (NIR) algorithms based on the sun-induced chlorophyll fluorescence signal. In an analysis of a multi-year (2003–2010) in situ monitoring data set from Tampa Bay, Florida (USA), as a case, this study assesses the relationship between the fluorescence line height (FLH) product from the Moderate Resolution Imaging Spectrometer (MODIS) and chlorophyll-α.

An overview of remote sensing of chlorophyll fluorescence

Besides empirical algorithms with the blue-green ratio, the algorithms based on fluorescence are also important and valid methods for retrieving chlorophyll-a concentration in the ocean waters, especially for Case II waters and the sea with algal blooming. This study reviews the history of initial cognitions, investigations and detailed approaches towards chlorophyll fluorescence, and then introduces the biological mechanism of fluorescence remote sensing and main spectral characteristics such as the positive correlation between fluorescence and chlorophyll concentration, the red shift phenomena. Meanwhile, there exist many influence factors that increase complexity of fluorescence remote sensing, such as fluorescence quantum yield, physiological status of various algae, substances with related optical property in the ocean, atmospheric absorption etc. Based on these cognitions, scientists have found two ways to calculate the amount of fluorescence detected by ocean color sensors: fluorescence line height and reflectance ratio. These two ways are currently the foundation for retrieval of chlorophyl l - a concentration in the ocean. As the in-situ measurements and synchronous satellite data are continuously being accumulated, the fluorescence remote sensing of chlorophyll-a concentration in Case II waters should be recognized more thoroughly and new algorithms could be expected.

Overview of remote sensing of chlorophyll flourescene in ocean waters

Besides empirical algorithms with the blue-green ratio, the algorithms based on fluorescence are also important and valid methods for retrieving chlorophyll-a concentration in the ocean waters, especially for Case II waters and the sea with algal bloom. This study reviews the history of initial cognitions, investigations and detailed approaches towards chlorophyll fluorescence, and then introduces the biological mechanism of fluorescence remote sensing and main spectral characteristics such as the positive correlation between fluorescence and chlorophyll concentration and the red shift phenomena. Meanwhile, there exist many influence factors that increase complexity of fluorescence remote sensing, such as fluorescence quantum yield, physiological status of various algae, substances with related optical property in the ocean, atmospheric absorption etc. Based on these cognitions, scientists have found two ways to calculate the amount of fluorescence detected by ocean color sensors: fluorescence line height and reflectance ratio. These two ways are currently the foundation for retrieving chlorophyll-a concentrations from the ocean. As in-situ measurements and synchronous satellite data are continuously being accumulated, the fluorescence remote sensing of chlorophyll-a concentration in Case II waters would be recognized more thoroughly and new algorithm could be expected. International Journal of Natural and Applied Sciences Vol. 2 (4) 2006: pp. 321-331

Absorption spectra of inorganic particles in the Irish Sea and their relevance to remote sensing of chlorophyll

Abstract Mineral suspended solids contribute significantly to the total suspended sediment load in shelf seas and estuaries. Even in moderate concentrations, mineral suspended solids affect algorithms for retrieving chlorophyll concentrations from remotely-sensed colour data. Brown and Simpson (1990) showed empirically that the slope of a log-log graph of chlorophyll against the blue-green reflectance ratio increased as the concentration of mineral suspended solids increased. In an attempt to explain this, we have measured the absorption spectrum of over 100 samples of mineral suspended solids collected from the Menai Strait in the Irish Sea. The absorption spectra of the mineral suspended solids particles on filters consistently show an exponential decrease with increasing wavelength across the visible spectrum. The absorption coefficient at a single wavelength is proportional to the concentration of mineral suspended solids. This spectrum has been incorporated into a simple model of the blue-green ratio in the presence of both chlorophyll and mineral suspended solids. The results are in qualitative agreement with Brown and Simpson's observations and can be made to be in quantitative agreement by correcting the absorption of mineral suspended solids on filters to in situ absorption using a pathlength correction factor β = 4. The main effect of mineral suspended solids is to decrease the sensitivity of the blue-green ratio to changes in chlorophyll in shelf seas and we conclude that the blue-green ratio may not be the best remote sensing tool for measuring chlorophyll in case 2 waters.

A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS

We present a simple modification to the standard coastal zone color scanner (CZCS) atmospheric correction algorithm for application to Sea-viewing-wide field-ofview-sensor (SeaWiFS). The modification reduces the error in the water-leaving reflectance using the standard algorithm by a factor of 2–6 when the aerosol behaves as predicted by the LOWTRAN-6 models. For many aerosol models likely to approximate aerosol properties over the oceans the error in the retrieved water-leaving reflectance is predicted to be < ± 0.002 at 443 nm for an aerosol load approximately 2 to 3 times that normally occurring in a maritime atmosphere. These errors in atmospheric correction lead to an error in the pigment concentration ( C), retrieved using the blue-green ratio algorithm, of < 50% for more than 75% of the aerosol models tested, whenever the algorithm retrieves a “reasonable” pigment concentration, and when 0.1 ≤ C ≤ 1 mg / m 3. This accuracy may be sufficient for some applications, for example, at-sea processing to guide ships to desirable sampling locations. An important feature of this algorithm is that, unlike more sophisticated and computational intensive algorithms, aerosol models are not required to effect the actual atmospheric correction. Investigators already having the CZCS algorithm implemented on an image processing system should be able to process SeaWiFS imagery by making very simple modifications to the code.

Detection of phytoplankton pigments from ocean color: improved algorithms

Passive ocean-color data at 32 wavelengths in the visible domain and laser-induced fluorescence line heights of chlorophyll and phycoerythrin, measured simultaneously from an aircraft in the New York Bight area, are used to examine the problem of developing algorithms for pigment retrieval from ocean-color data that would be capable of distinguishing between chlorophyll and phycoerythrin. Using factor analysis, it is shown that it is indeed possible to develop such algorithms. Furthermore, the wavelengths used in the algorithms can be reduced from 32 to 6 (similar to the SeaWiFS channels) without much loss in information. These multiwavelength algorithms yield significantly higher correlation coefficients for chlorophyll compared with the conventional blue-green ratio used for retrieval of this pigment. The Coastal Zone Color Scanner wavelengths appear to be inadequate for quantitative retrieval of the phycoerythrin signal.

Factor analysis of multispectral radiances over coastal and open ocean water based on radiative transfer calculations

Factor analysis is applied to multispectral (seventeen wavelengths) radiances simulated by a radiative transfer model (matrix-operator method) in and above coastal and open ocean waters. The calculated radiances were compared with measured radiances before applying factor analysis. They agree well for different sun elevations and even for turbid coastal waters. The factor analysis technique allows us to extract the characteristic signatures of phytoplankton, suspended matter, and yellow substance. The fluorescence of chlorophyll at lambda = 685 nm is found to be a clear signal for phytoplankton, also in the presence of other suspensions and yellow substance. A comparison of different algorithms for the extraction of the fluorescence peak favors the addition of chlorophyll absorption at lambda = 670 nm. The blue-green ratio is found to be useless for chlorophyll detection in coastal waters. Suspended matter and yellow substance can also clearly be seen in the factor loading for all multispectral radiances analyzed. However, suspended matter is reflected more strongly than yellow substance.

Optimization of a Rayleigh-type equation for the detection of tritanomaly

Colour matches made by 50 young experienced trichromats on six spectral equations of the kind: “Blue” + “Green” ≡ “Cyan” + “Yellow” yielded Blue/Green ratios with minimal inter-individual variability for a Green of 499 nm and Blue in the range 436–439 nm. This combination agrees closely with Ruddock's (1963) macular pigment extinction spectrum and with Walraven's (1974) tritanopic confusion wavelengths and may be suitable for detecting tritanomaly. Blue Green ratios were not affected by fixing the Cyan/Yellow (saturation) setting at the population mean. This reduction from trichromatic to dichromatic control will facilitate the testing of naive subjects.