Wesley J. Moses

Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands.

Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3=[Rrs(-1)(665)-Rrs(-1)(708)]×Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl]>1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning.

Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data—successes and challenges

We present and discuss here the results of our work using MODIS (moderate resolution imaging spectroradiometer) and MERIS (medium resolution imaging spectrometer) satellite data to estimate the concentration of chlorophyll-a (chl-a) in reservoirs of the Dnieper River and the Sea of Azov, which are typical case II waters, i.e., turbid and productive. Our objective was to test the potential of satellite remote sensing as a tool for near-real-time monitoring of chl-a distribution in these water bodies. We tested the performance of a recently developed three-band model, and its special case, a two-band model, which use the reflectance at red and near-infrared wavelengths for the retrieval of chl-a concentration. The higher spatial resolution and the availability of a spectral band at around 708 nm with the MERIS data offered great promise for these models. We compared results from several different atmospheric correction procedures available for MODIS and MERIS data. No one particular procedure was consistently and systematically better than the rest. Nevertheless, even in the absence of a perfect atmospheric correction procedure, both the three-band and the two-band models showed promising results when compared with in situ chl-a measurements. The challenges and limitations involved in satellite remote monitoring of the chl-a distribution in turbid productive waters are discussed.

A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters.

The objective of this work was to test the performance of a recently developed three-band model and its special case, a two-band model, for the remote estimation of the chlorophyll-a (chl-a) concentration in turbid productive case 2 waters. We specifically focused on (a) determining the ability of the models to estimate chl-a < 20 mg m −3 , typical for coastal and estuarine waters, and (b) assessing the potential of MODIS and MERIS to estimate chl-a concentrations in turbid productive waters, using red and near-infrared (NIR) bands. Reflectance spectra and water samples were collected in 89 stations over lakes in the United States with a wide variability in optical parameters (i.e. 2.1 < chl-a < 184 mg m −3 ;0 .5 < Secchi disk depth < 4. 2m ; 1.2 < total suspended matter < 15 mg l −1 ). The three-band model, using wavebands around 670, 710 and 750 nm, explains more than 89% of the chl-a variation for chl-a ranging from 2 to 20 mg m −3 and can be used to estimate chlorophyll-a concentrations with a root mean square error (RMSE) of<1.65 mg m −3 . MODIS (bands 13 and 15) and MERIS (bands 7, 9, and 10) red and NIR reflectances were simulated from the collected reflectance spectra and potential estimation errors were assessed. The MODIS two-band model is able to estimate chl-a concentrations with a RMSE of <7. 5m g m −3 for chl-a ranging from 2 to 50 mg m −3 ;h owever, the model loses its sensitivity for chl-a < 20 mg m −3 . Benefiting from the higher spectral resolution of the MERIS data, the MERIS three-band model accounts for 93% of chl-a variation and is able to estimate chl-a concentrations with a RMSE of <5. 1m g m −3 for chl-a ranging from 2 to 50 mg m −3 ,a nd aR MSE of<1. 7m g m −3 for chl-a ranging from 2 to 20 mg m −3 . These findings imply that, provided that an atmospheric correction scheme specific to the red and NIR spectral region is available, the extensive database of MODIS and MERIS images could be used to quantitatively monitor chl-a in case 2 waters.

Satellite Estimation of Chlorophyll-

We present here the results of calibrating and validating a three-band model and, its special case, a two-band model, which use MEdium Resolution Imaging Spectrometer (MERIS) reflectances in the red and near-infrared spectral regions for estimating chlorophyll-a (chl- a) concentration in inland, estuarine, and coastal turbid productive waters. During four data collection campaigns in 2008 and one campaign in 2009 in the Taganrog Bay and the Azov Sea, Russia, water samples were collected, and concentrations of chl-a and total suspended solids were measured in the laboratory. The data collected in 2008 were used for model calibration, and the data collected in 2009 were used for model validation. The models were applied to MERIS images acquired within two days from the date of in situ data collection. Two different atmospheric correction procedures were considered for processing the MERIS images. The results illustrate the high potential of the models to estimate chl-a concentration in turbid productive (Case II) waters in real time from satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems.

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A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chl-a concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21 mg m(-3) during four field campaigns. A two-band model without re-parameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5 mg m(-3). Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes.

Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study

Errors in the estimated constituent concentrations in optically complex waters due solely to sensor noise in a spaceborne hyperspectral sensor can be as high as 80%. The goal of this work is to elucidate the effect of signal-to-noise ratio (SNR) on the accuracy of retrieved constituent concentrations. Large variations in the magnitude and spectral shape of the reflectances from coastal waters complicate the impact of SNR on the accuracy of estimation. Due to the low reflectance of water, the actual SNR encountered for a water target is usually quite lower than the prescribed SNR. The low SNR can be a significant source of error in the estimated constituent concentrations. Simulated and measured at-surface reflectances were used in this study. A radiative transfer code, Tafkaa, was used to propagate the at-surface reflectances up and down through the atmosphere. A sensor noise model based on that of the spaceborne hyperspectral sensor HICO was applied to the at-sensor radiances. Concentrations of chlorophyll-a, colored dissolved organic matter, and total suspended solids were estimated using an optimized error minimization approach and a few semi-analytical algorithms. Improving the SNR by reasonably modifying the sensor design can reduce estimation uncertainties by 10% or more.

NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study

We present here results that demonstrate the potential of near-infrared (NIR)-red models to estimate chlorophyll- a (chl- a) concentration in coastal waters using data from the spaceborne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrometer (MERIS), the use of sensors such as HICO has become critical for coastal ocean color research. Algorithms based on two- and three-band NIR-red models, which were previously used very successfully with MERIS data, were applied to HICO images. The two- and three-band NIR-red algorithms yielded accurate estimates of chl- a concentration, with mean absolute errors that were only 10.92% and 9.58%, respectively, of the total range of chl- a concentrations measured over a period of several months in 2012 and 2013 on the Taganrog Bay in Russia. Given the uncertainties in the radiometric calibration of HICO, the results illustrate the robustness of the NIR-red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to HICO data as they relate to estimating chl- a concentration in productive coastal waters. Inherent limitations due to the characteristics of the sensor and its orbit prohibit HICO from providing anywhere near the level of frequent global coverage as provided by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining water quality in select coastal areas and the cross-sensor applicability of NIR-red models and provide an indication of what could be achieved with future spaceborne hyperspectral sensors in estimating coastal water quality.

Impact of Signal-to-Noise Ratio in a Hyperspectral Sensor on the Accuracy of Biophysical Parameter Estimation in Case II Waters

Using simulated data, we investigated the effect of noise in a spaceborne hyperspectral sensor on the accuracy of the atmospheric correction of at-sensor radiances and the consequent uncertainties in retrieved water quality parameters. Specifically, we investigated the improvement expected as the F-number of the sensor is changed from 3.5, which is the smallest among existing operational spaceborne hyperspectral sensors, to 1.0, which is foreseeable in the near future. With the change in F-number, the uncertainties in the atmospherically corrected reflectance decreased by more than 90% across the visible-near-infrared spectrum, the number of pixels with negative reflectance (caused by over-correction) decreased to almost one-third, and the uncertainties in the retrieved water quality parameters decreased by more than 50% and up to 92%. The analysis was based on the sensor model of the Hyperspectral Imager for the Coastal Ocean (HICO) but using a 30-m spatial resolution instead of HICO’s 96 m. Atmospheric correction was performed using Tafkaa. Water quality parameters were retrieved using a numerical method and a semi-analytical algorithm. The results emphasize the effect of sensor noise on water quality parameter retrieval and the need for sensors with high Signal-to-Noise Ratio for quantitative remote sensing of optically complex waters.

HICO-Based NIR–Red Models for Estimating Chlorophyll-

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise in the data. In this study, we have compared the Euclidean distance and the Mahalanobis distance as metrics for spectral comparison. In addition to providing justification for the preference of the Mahalanobis Distance over the Euclidean Distance, we have also included a statistical description of noisy hyperspectral data.

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Atmospheric correction of hyperspectral image data is frequently a requirement for using remote sensing to understand and quantify various phenomena that take place on the Earth. This is particularly true when the analysis requires the use of spectral reflectance. Although sophisticated models exist that can be used to perform atmospheric correction, evaluating the performance of these procedures is non-trivial. In this study, two atmospheric correction programs, FLAASH (based on MODTRAN 4), and TAFKAA_6S (based on 6S), were applied to a pair of images of the same area but collected six weeks apart. The results of the two atmospheric correction procedures are analyzed based on the expected stability of pseudo-invariant features (PIFs). Although both procedures performed rather well in terms of removing atmospheric absorption features in the infrared region, the analysis identified some anomalous behaviors as well, the most important of which appears to be related to the bidirectional reflectance distribut...