Standard Atmospheric Correction Algorithm Research Articles

Enhanced POLYMER atmospheric correction algorithm for water-leaving radiance retrievals from hyperspectral/multispectral remote sensing data in inland and coastal waters.

Accurate retrieval of the water-leaving radiance from hyperspectral/multispectral remote sensing data in optically complex inland and coastal waters remains a challenge due to the excessive concentrations of phytoplankton and suspended sediments as well as the inaccurate estimation and extrapolation of aerosol radiance over the visible wavelengths. In recent years, reasonably accurate methods were established to estimate the enhanced contribution of suspended sediments in the near-infrared (NIR) and shortwave infrared (SWIR) bands to enable atmospheric correction in coastal waters, but solutions to derive the dominant phytoplankton contribution in the NIR and SWIR bands are less generalizable and subject to large uncertainties in the remotely-derived water color products. These issues are not only associated with the standard atmospheric correction algorithm in the SeaDAS processing system but with the non-traditional algorithms such as POLYMER (POLYnomial-based approach established for the atmospheric correction of MERIS data). This study attempts to enhance the POLYMER algorithm to enable atmospheric correction of hyperspectral and multispectral remote sensing data over a wide range of inland and ocean waters. The original POLYMER algorithm is less suitable owing to its complete reliance on a polynomial approach to model the atmospheric reflectance as a function of the wavelength and retrieve the water-leaving reflectance using two semi-analytical models (MM01 and PR05). The polynomial functions calculate the bulk atmospheric contribution instead of using an explicit method to estimate aerosol radiance separately, resulting the erroneous water color products in inland and coastal waters. The modified POLYMER algorithm (mPOLYMER) employs more realistic approaches to estimate aerosol contributions with a combination of UV and Visible-NIR bands and enables accurate retrievals of water-leaving radiance from both hyperspectral and multispectral remote sensing data. To assess the relative performance and wider applicability of mPOLYMER, the original and enhanced algorithms were tested on a variety of HICO, MSI and MODIS-Aqua data and the retrieved Lwn products were compared with AERONET-OC and OOIL-regional in-situ data. Expectedly, the mPOLYMER algorithm greatly improved the accuracy of Lwn (in terms of magnitude and spectral shape) when applied to MODIS-Aqua and HICO data in highly turbid productive waters (with higher concentrations of phytoplankton or with dense algal blooms) in Muttukadu Lagoon, Lake Erie, Yangtze River Estuary, Baltic Sea and Arabian Sea. In contrast, the original POLYMER algorithm overestimated Lwn in the visible and NIR bands and produced unphysical negative Lwn or distorted Lwn spectra in turbid productive waters. The mPOLYMER yielded a relative mean error reduction of more than 50% (i.e., from 79% to 34%) in Lwn for a large number of matchup data. The improved accuracy and data quality is because the mPOLYMER algorithm's funio and coefficients sufficiently accounted for the enhanced backscattering contribution of phytoplankton and suspended sediments in optically complex waters.

Aerosol Optical Properties above Productive Waters of Gorky Reservoir for Atmospheric Correction of Sentinel-3/OLCI Images

The main challenge that one has to face during the atmospheric correction (AC) of productive inland waters is the inability to correctly separate aerosol radiance from water-leaving radiance in the near-infrared range (NIR) bands. This leads both to incorrect estimates of the aerosol parameters and the remote-sensing reflectance (Rrs). For the Gorky Reservoir, where we are developing regional bio-optical models, the situation is complicated by the lack of field measurements of aerosol optical properties due to the significant remoteness of AERONET stations. The standard AC algorithms, as shown earlier, greatly overestimated the aerosol radiance in all spectral bands up to red bands during the period of intense cyanobacteria blooms, while the algorithm with a fixed aerosol optical depth (AOD) obtained in a clean water area gave encouraging results. Therefore, it was important to investigate the characteristics of the atmosphere above the reservoir and validate the proposed approach for regular use of Sentinel-3 imagery of the Gorky Reservoir. To solve these issues, regular in situ aerosol measurements using the handheld sun photometer SPM were performed. The measured AOD and the Angstrom exponent were compared with the estimates of these parameters from two Sentinel-3/OLCI Level-2 products, namely, Synergy (SYN) and Water Full Resolution products (OL_2_WFR). It was found that AOD and the Angstrom exponent from these standard products were overestimated by 2–3 times and almost 2 times in all cases. Atmospheric correction with fixed AOD, defined by measurements, allows us to completely get rid of negative Rrs, and its shapes and values became typical for the Gorky Reservoir. Despite the overestimation of AOD in traditional AC and its large variations in general, it was found that the minimum AOD spectrum is close to the measured spectrum. Therefore, the AOD spectra, which correspond to the two percentiles of the distribution, can be used for preliminary AC with a fixed AOD of the Sentinel-3/OLCI imaginary. The relative errors of the Rrs retrievals using the two percentile AOD compared to the measured AOD were 3–35% in the green and red bands of Sentinel-3/OLCI.

Sensitivity of Ocean Color Atmospheric Correction to Uncertainties in Ancillary Data: A Global Analysis With SeaWiFS Data

Atmospheric correction (AC) algorithms for ocean color (OC) data processing usually rely on ancillary data documenting the atmosphere and the sea state to help the calculation of the remote sensing reflectance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> from the radiance measured by a space sensor. This study aims at assessing the impact that the uncertainties associated with these ancillary data have on the AC outputs. For this objective, a full year of global Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery is processed with the standard AC algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l2gen</i> of the National Aeronautics and Space Administration with different sets of ancillary data, the reference case with National Centers for Environmental Prediction (NCEP) Reanalysis-2 meteorological data and satellite ozone products, as well as with ten ensemble members from the European Centre for Medium-Range Weather Forecast (ECMWF) CERA-20C data. The spread within the ensemble data and the differences with respect to the reference case are taken as a measure of the uncertainties associated with ancillary data. The impact on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> of perturbations in ancillary variables vary in space, the variables having the largest effects being wind speed and relative humidity, and ozone at bands where ozone absorption is largest, while sea-level pressure and precipitable water have the smallest effect. Sensitivity coefficients quantifying the relationship between perturbations in ancillary variables and effects on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> change with variable and wavelength. At the global scale, the variations found on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> when ancillary data are perturbed are usually small but not negligible and should be considered in the ocean color (OC) data uncertainty budget.

Development of a Deep Learning-Based Atmospheric Correction Algorithm for Oligotrophic Oceans

Although the 5% mission goal for NASA’s standard atmospheric correction (AC) algorithm (i.e., the near-infrared (NIR) algorithm) for oligotrophic oceans has been met, this algorithm applies only to blue bands and is highly sensitive to contamination from cloud straylight and sunglint. Here, we developed an AC algorithm for clear waters based on deep learning (namely, DLAC). The algorithm was trained using 3.6 million pairs of MODIS-Aqua high-quality Rrs from the NIR algorithm and Rayleigh-corrected reflectances selected across the global oceans and from all seasons. Validations using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data and a chlorophyll (Chl) constraint-based approach showed that the uncertainties in the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> retrievals for DLAC are lower than those for the NIR algorithm, especially for the green and red bands. More importantly, the DLAC algorithm is more tolerant to cloud adjacency effects and moderate sunglint. As a result, the number of valid observations increased by ~50%, and the coverage of monthly global Level-3 R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> composites increased by up to 20%. More spatially and temporally consistent patterns were also found for the Level-3 R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> and Chl products, and large changes in their magnitudes (up to 20% for R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rs</sub> and 30% for Chl) were detected in some oceanic regions. With these improvements in the quality and quantity of data, our DLAC algorithm may be valuable as another option for processing global data.

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters.

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans

With a revisit time of 1 h, spatial resolution of 500 m, and high radiometric sensitivity, the Geostationary Ocean Color Imager (GOCI) is widely used to monitor diurnal dynamics of oceanic phenomena. However, atmospheric correction (AC) of GOCI data with high solar zenith angle (>70°) is still a challenge for traditional algorithms. Here, we propose a novel neural network (NN) AC algorithm for GOCI data under high solar zenith angles. Unlike traditional NN AC algorithms trained by radiative transfer-simulated dataset, our new AC algorithm was trained by a large number of matchups between GOCI-observed Rayleigh-corrected radiance in the morning and evening and GOCI-retrieved high-quality noontime remote-sensing reflectance (Rrs). When validated using hourly GOCI data, the new NN AC algorithm yielded diurnally stable Rrs in open ocean waters from the morning to evening. Furthermore, when validated by in-situ data from three Aerosol Robotic Network-Ocean Color (AERONET-OC) stations (Socheongcho, Gageocho and Ieodo), the GOCI-retrieved Rrs at visible bands obtained using the new AC algorithm agreed well with the in-situ values, even under high solar zenith angles. Practical application of the new algorithm was further examined using diurnal GOCI observation data acquired in clear open ocean waters. Results showed that the new algorithm successfully retrieved Rrs for the morning and evening GOCI data. Moreover, the amount of Rrs data retrieved by the new algorithm was much higher than that retrieved by the standard AC algorithm in SeaDAS. Our proposed NN AC algorithm can not only be applied to process GOCI data acquired in the morning and evening, but also has the potential to be applied to process polar-orbiting satellite ocean color data at high-latitude ocean that also include satellite observation with high solar zenith angles.

Atmospheric Correction of OLCI Imagery over Extremely Turbid Waters Based on the Red, NIR and 1016 nm Bands and a New Baseline Residual Technique

A common approach to the pixel-by-pixel atmospheric correction of satellite water colour imagery is to calculate aerosol and water reflectance at two spectral bands, typically in the near infra-red (NIR, 700–1000 nm) or the short-wave-infra-red (SWIR, 1000–3000 nm), and then extrapolate aerosol reflectance to shorter wavelengths. For clear waters, this can be achieved simply for NIR bands, where the water reflectance can be assumed negligible i.e., the “black water” assumption. For moderately turbid waters, either the NIR water reflectance, which is non-negligible, must be modelled or longer wavelength SWIR bands, with negligible water reflectance, must be used. For extremely turbid waters, modelling of non-zero NIR water reflectance becomes uncertain because the spectral slopes of water and aerosol reflectance in the NIR become similar, making it difficult to distinguish between them. In such waters the use of SWIR bands is definitely preferred and the use of the MODIS bands at 1240 nm and 2130 nm is clearly established although, on many sensors such as the Ocean and Land Colour Instrument (OLCI), such SWIR bands are not included. Instead, a new, cheaper SWIR band at 1016 nm is available on OLCI with potential for much better atmospheric correction over extremely turbid waters. That potential is tested here. In this work, we demonstrate that for spectrally-close band triplets (such as OLCI bands at 779–865–1016 nm), the Rayleigh-corrected reflectance of the triplet’s “middle” band after baseline subtraction (or baseline residual, BLR) is essentially independent of the atmospheric conditions. We use the three BLRs defined by three consecutive band triplets of the group of bands 620–709–779–865–1016 nm to calculate water reflectance and hence aerosol reflectance at these wavelengths. Comparison with standard atmospheric correction algorithms shows similar performance in moderately turbid and clear waters and a considerable improvement in extremely turbid waters.

Vicarious calibration of GOCI for the SeaDAS ocean color retrieval

ABSTRACTA key on-orbit calibration step for satellite remote sensing of ocean color is the vicarious calibration. This establishes the final gains for each spectral band on the sensor that minimize bias in the retrieved ocean color signal. The vicarious calibration is specific to the instrument and the atmospheric correction algorithm. The vicarious calibration gains for the Geostationary Ocean Color Imager (GOCI) are presented here, which were derived to optimize the performance of NASA’s standard atmospheric correction algorithm as implemented in the l2gen code and distributed through the SeaDAS open-source software package. Following NASA’s protocols, the near-infrared (NIR) bands were calibrated first, and the visible bands were then calibrated relative to this fixed NIR calibration. The gain for the 745-nm NIR band was derived using a fixed aerosol model, which was chosen based on the Angstrom Coefficients derived from MODIS on Aqua (MODISA). For the vicarious gains of the visible bands, two sources for the target water-leaving radiances were tested: matchups from MODISA and climatological data from SeaWiFS. A validation analysis using AERONET-OC data shows an improvement in sensor performance when compared with results using the current vicarious gains and results using no vicarious calibration. Good agreement was found in vicarious gains derived using both concurrent MODISA and climatological SeaWiFS as vicarious calibration data sources. These results support the use of a concurrent sensor for the vicarious calibration when in situ data are not available and demonstrate that using climatology from a well-calibrated sensor like SeaWiFS for the vicarious calibration is a valid alternative when it is not possible to use a concurrent sensor or in situ data. We recommend using the gains derived from concurrent GOCI matchups with MODISA for GOCI processing in SeaDAS/l2gen.

Atmospheric Correction Using the Information From the Short Blue Band

We describe our effort to use normalized water-leaving radiance at the short blue band 410 nm, nLw (410), derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership to improve the VIIRS ocean color products over coastal and inland waters, in particular, over the regions contaminated by strongly absorbing aerosols. The current standard atmospheric correction algorithm has significant issues when dealing with cases of strongly absorbing aerosols, e.g., dust, smoke, and air pollution from nearby cities. For such cases, satellite-derived normalized water-leaving radiance spectra nLw ( $\lambda $ ) are usually biased low and may be negative, particularly for the short blue bands, e.g., for the VIIRS nLw (410). In addition, for extremely turbid waters and waters dominated by colored dissolved organic matter (CDOM) that is strongly absorbing at the short blue band, slightly negative nLw (410) data are also sometimes observed. Obviously, cases with nLw (410) nLw (410) nLw (410) information. Specifically, for cases with nLw (410) nLw (410) $\ge 0$ [e.g., assuming nLw (410) = 0], thereby removing unphysical retrievals for VIIRS-derived nLw ( $ \lambda $ ) spectra. Using this technique, VIIRS-derived nLw ( $ \lambda $ ) spectra are now all ≥ 0, showing considerable improvements for VIIRS ocean color products, particularly for biological and biogeochemical products [e.g., chlorophyll-a concentration and diffuse attenuation coefficient at 490 nm $K_{d}$ (490)] which are derived using VIIRS nLw ( $\lambda $ ) spectra. Several detailed examples from VIIRS measurements over various coastal and inland waters are provided and discussed. The new technique has been implemented in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, which has been used for the routine production of VIIRS global ocean color products.

Sensor performance requirements for atmospheric correction of satellite ocean color remote sensing.

We analyze the effects of the sensor signal-to-noise ratio (SNR) requirements for atmospheric correction of satellite ocean color remote sensing using the near-infrared (NIR) and shortwave infrared (SWIR) bands. Using the Gaussian noise model for the sensor noise distribution in the NIR and SWIR bands, some extensive simulations have been carried out to evaluate and assess the effects of sensor NIR and SWIR SNR values on the retrieved normalized water-leaving reflectance spectra ρwN(λ), which are used to derive all ocean or inland water biological and biogeochemical property data. The standard atmospheric correction algorithm for global oceans and inland waters using the two NIR bands, i.e., Gordon and Wang (1994) [Appl. Opt.33, 443 (1994)Appl. Opt.46, 1535 (2007)], is assumed in the evaluation. Specifically, the minimum and goal SNR requirements for the NIR and SWIR bands for atmospheric correction are estimated. The minimum SNR values are those with which sufficiently accurate ρwN(λ) can be derived, while the goal SNR requirements are those with which the atmospheric correction algorithms reach to their corresponding inherent limitations (or inherent errors), i.e., no gains can be achieved with further increase of SNR values in the NIR and SWIR bands. Evaluation results show that the minimum SNR requirement for the two NIR bands is ~200-300, while the minimum SNR requirement for the three SWIR bands is ~100. For the goal SNR requirements, the recommendations are SNR's of ~600 and ~200 for the two NIR bands and three SWIR bands, respectively.

Atmospheric correction over coastal waters using multilayer neural networks

Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water-leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) method. We use a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and train a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The method is validated using both a synthetic dataset and Aerosol Robotic Network – Ocean Color (AERONET–OC) measurements. The SeaDAS NIR algorithm, the SeaDAS NIR/SWIR algorithm, and the MODIS version of the Case 2 regional water - CoastColour (C2RCC) algorithm are also included in the comparison with AERONET–OC measurements. The performance of the AC algorithms is evaluated with four statistical metrics: the Pearson correlation coefficient (R), the average percentage difference (APD), the mean percentage bias, and the root mean square difference (RMSD). The comparison with AERONET–OC measurements shows that the MLNN algorithm significantly improves retrieval of normalized Lw in blue bands (412nm and 443nm) and yields minor improvements in green and red bands compared with the other three algorithms. On a global scale, the MLNN algorithm reduces APD in normalized Lw by up to 13% in blue bands and by 2–7% in green and red bands when compared with the standard SeaDAS NIR algorithm. In highly absorbing coastal waters, such as the Baltic Sea, the MLNN algorithm reduces APD in normalized Lw by more than 60% in blue bands compared to the standard SeaDAS NIR algorithm, while in highly scattering coastal waters, such as the Black Sea, the MLNN algorithm reduces APD by more than 25%. These results indicate that the MLNN algorithm is suitable for application in turbid coastal waters. Application of the MLNN algorithm to MODIS Aqua images in several coastal areas also shows that it is robust and resilient to contamination due to sunglint or adjacency effects of land and cloud edges. The MLNN algorithm is very fast once the neural network has been properly trained and is therefore suitable for operational use. A significant advantage of the MLNN algorithm is that it does not need SWIR bands.

Real-Time Atmospheric Correction of AVIRIS-NG Imagery

We demonstrate real-time model-based atmospheric correction onboard the Next Generation Airborne Visible/Infrared Imaging Spectrometer. We achieve a reduction in processing time from hours or days to seconds by modifying a standard physics-based atmospheric correction algorithm to support real-time execution. We achieved this reduction by modifying the physics-based ATmospheric REMoval algorithm to leverage a large lookup table of precomputed scattering and transmission coefficients, indexed by parameters specifying the aircraft operating conditions at capture time. Interpolation among the precomputed coefficients allows surface reflectance retrieval at the sensor acquisition rate of 500 Mb/s. Our system produced science-quality reflectance products during over 30 test flights and, to our knowledge, is the first reported demonstration of real-time model-driven visible shortwave infrared atmospheric correction onboard an aircraft.

Bayesian methodology for inverting satellite ocean-color data

The inverse ocean color problem, i.e., the retrieval of marine reflectance from top-of-atmosphere (TOA) reflectance, is examined in a Bayesian context. The solution is expressed as a probability distribution that measures the likelihood of encountering specific values of the marine reflectance given the observed TOA reflectance. This conditional distribution, the posterior distribution, allows the construction of reliable multi-dimensional confidence domains of the retrieved marine reflectance. The expectation and covariance of the posterior distribution are computed, which gives for each pixel an estimate of the marine reflectance and a measure of its uncertainty. Situations for which forward model and observation are incompatible are also identified. Prior distributions of the forward model parameters that are suitable for use at the global scale, as well as a noise model, are determined. Partition-based models are defined and implemented for SeaWiFS, to approximate numerically the expectation and covariance. The ill-posed nature of the inverse problem is illustrated, indicating that a large set of ocean and atmospheric states, or pre-images, may correspond to very close values of the satellite signal. Theoretical performance is good globally, i.e., on average over all the geometric and geophysical situations considered, with negligible biases and standard deviation decreasing from 0.004 at 412nm to 0.001 at 670nm. Errors are smaller for geometries that avoid Sun glint and minimize air mass and aerosol influence, and for small aerosol optical thickness and maritime aerosols. The estimated uncertainty is consistent with the inversion error. The theoretical concepts and inverse models are applied to actual SeaWiFS imagery, and comparisons are made with estimates from the SeaDAS standard atmospheric correction algorithm and in situ measurements. The Bayesian and SeaDAS marine reflectance fields exhibit resemblance in patterns of variability, but the Bayesian imagery is less noisy and characterized by different spatial de-correlation scales. Experimental errors obtained from match-up data are similar to the theoretical errors determined from simulated data. Regionalization of the inverse models is a natural development to improve retrieval accuracy, for example by including explicit knowledge of the space and time variability of atmospheric variables.

A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations

A novel algorithm is developed and validated to estimate phycocyanin (PC) pigment concentrations of the most common cyanobaterium, Microcystis aeruginosa, in Taihu Lake (China's third largest freshwater lake) using data from the Medium Resolution Imaging Spectrometer (MERIS). First, a PC index (PCI) is defined as remote-sensing reflectance (Rrs, sr−1) at 620nm normalized against a baseline formed linearly between Rrs(560) and Rrs(665). Because PC has a local absorption peak at ~620nm, PCI is shown in both theory and model simulations to exhibit a monotonic functional relationship with PC concentration. Field measurements also support this hypothesis, from which an empirical algorithm is developed to estimate PC concentrations between ~1 and 300μg/L with unbiased RMS uncertainties of 58%. Radiative transfer simulations further show that such a field-based algorithm can be applied directly to MERIS Rayleigh-corrected reflectance (Rrc) after adjusting algorithm coefficients. Spectral analyses and image comparisons indicate that such an algorithm is nearly immune to perturbations from thick aerosols, thin clouds, significant sun glint, and extreme water turbidity. Mean usable data coverage increases from <1% to >50% when using Rrc as compared to Rrs processed using standard atmospheric correction algorithms in SeaDAS. The robust algorithm performance and significantly improved data coverage lead to the establishment of a long-term (2002–2012) time-series of PC concentrations in Taihu Lake, which show both seasonal and inter-annual changes. Test of the algorithm over other lakes, including Dianchi Lake (a typical plateau lake of China), suggests that the same band-subtraction approach might be applicable to other inland water bodies, although local bio-optical conditions due to different sediment and phytoplankton compositions need to be considered when applying such an empirical approach.

An Antisubmarine Detection Method Using IR Spectrometer in Ocean Remote Sensing

According to the reduction of submarine noise level, Non-acoustics antisubmarine detection method becomes more important for the ocean remote sensing, especially infrared (IR) imaging remote sensing detection method. Conventional IR imaging remote sensing antisubmarine detection is more difficult because modern advanced submarine IR thermal radiance is not obvious. In this paper, our main purpose is to develop the advanced IR imaging remote sensing antisubmarine detection approach by using infrared spectrometer. The IR spectrum information derived from IR spectrometer in sea water and then retrieves the water-leaving spectra by the standard atmospheric correction algorithm. The submarine is detected by analyzing the water-leaving spectrum information. Results of comparisons with conventional IR imaging remote sensing antisubmarine detection, the modified approach is available to estimate the spectrum properties and effective to antisubmarine detection in sea water

Atmospheric Correction Over Coastal Turbid Waters of Bay of Bengal Using OCEANSAT-I Ocean Colour Monitor (OCM) Data

Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules, ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived at 0.945, which is an improvement over the standard atmospheric correction procedure.

Ocean color products retrieval and validation around China coast with MODIS

Waters along China coast are very turbid with high concentrations of suspended sediment nearly all the time, especially at the Hangzhou Bay, the Changjiang (Yangtze) River Estuary and the shoal along Jiangsu Province. In these turbid and optically complex waters, the standard MODIS ocean color products tend to have invalid values. Because the water-leaving radiances in the near-infrared (NIR) are significant resulting from the strong scattering of suspended particles, the standard MODIS atmospheric correction algorithm often gets no results or produces significant errors. And because of the complex water optical properties, the OC3 model used in the standard MODIS data processing tends to get extremely high chlorophyll-a (Chl-a) concentrations. In this paper, we present an atmospheric correction approach using MODIS short wave infrared (SWIR) bands based on the fact that water-leaving radiances are negligible in the SWIR region because of the extreme strong absorption of water even in turbid waters. A regional Chl-a concentration estimation model is also constructed for MODIS from in situ data. These algorithms are applied to MODIS Aqua data processing in the China coastal regions. In situ data collected in the Yellow Sea and the East China Sea in spring and autumn, 2003 are used to validate the performance. Reasonably good results have been obtained. It is noted that water-leaving reflectance in the NIR bands are significant in waters along the China coast with high sediment loadings. The satellite derived and in-situ reflectance spectra can match in the turbid waters along China coast, and there is relatively good linear relationship between satellite derived and in-situ reflectance. The RMSE value of Rrs(λ) is 0.0031 sr−1 for all the nine ocean color bands (412 to 869 nm). The satellite-derived Chl-a value is in the reasonable range and the root mean square percentage difference is 46.1%.

On the long-term variability of normalized radiance of the Black Sea

Basin-wide distributions of normalized spectral radiances averaged over different time periods were plotted based on the estimates of the average radiance for 20 × 20 km squares in 482 digital images of the Black Sea obtained with a SeaWiFS color scanner in 1998–2004. Significant distinctions of the annual average distributions in the amplitude, localization, and configuration of basin-scale inhomogeneities allowed us to consider these distributions as yearly spectral-radiance portraits of the Black Sea that reflect specific features of the annual behavior of the hydrometeorological conditions over the Black Sea region. Seasonal features of the radiance distributions related to the wind field were discovered. Substantial interannual variability in the errors of the standard atmospheric correction algorithm resulting in an underestimation of the shortwave radiances were revealed. It is shown that the river discharge and the stratification of the layer sensed substantially influence the yearly spectral-radiance portraits of the Black Sea.

Impact of sea ice on the retrieval of water-leaving reflectance, chlorophyll a concentration and inherent optical properties from satellite ocean color data

Two physical phenomena by which satellite remotely sensed ocean color data are contaminated by sea ice at high latitudes are described through simulations and observations: (1) the adjacency effect that occurs along sea ice margins and (2) the sub-pixel contamination by a small amount of sea ice within an ocean pixel. The signal at the top of the atmosphere (TOA) was simulated using the 6S radiative transfer code that allows modeling of the adjacency effect for various types of sea ice surrounding an open water area. In situ sea ice reflectance spectra used in the simulations were measured prior to and during the melt period as part of the 2004 Canadian Arctic Shelf Exchange Study (CASES). For sub-pixel contamination, the TOA signal was simulated for various surface reflectances obtained by linear mixture of both sea ice and water-leaving reflectances ( ρ w). The standard atmospheric correction algorithm was then applied to the simulated TOA spectra to retrieve ρ w spectra from which chlorophyll a concentrations (CHL) and inherent optical properties (IOPs) were derived. The adjacency effect was associated with large errors (> 0.002) in the retrieval of ρ w as far as 24 km from an ice edge in the blue part of the spectrum (443 nm). Therefore, for moderate to high CHL (> 0.5 mg m − 3 ), any pixel located within a distance of ∼ 10–20 km from the ice edge were unreliable. It was also found necessary to consider the adjacency effect when the total absorption coefficient ( a t) was to be retrieved using a semi-analytical algorithm. a t(443) was underestimated by more than 35% at a distance of 20 km from an ice edge for CHL > 0.5 mg m − 3 . The effect on the retrieval of the particle backscattering coefficient ( b bp) was important only for clear waters (CHL ∼ 0.05 mg m − 3 ). In contrast, sub-pixel contamination by a small amount of sea ice produced systematic underestimation of ρ w in the blue because of incorrect interpretation of enhanced reflectance in the near infrared that is attributed to higher concentrations of atmospheric aerosols. In general, sub-pixel contamination was found to result in overestimations of CHL and a t, and underestimations of b bp. A simple method was proposed to flag pixels contaminated by adjacency effect.

Evaluation of ADEOS-II GLI ocean color atmospheric correction using SIMBADA handheld radiometer data

The performance of the “version 2” Global Imager (GLI) standard atmospheric correction algorithm, which includes empirical absorptive aerosol correction and sun glint correction, was evaluated using data collected with handheld above-water SIMBADA radiometers during 23 cruises of opportunity (research vessels, merchant ships), mostly in the North Atlantic and European seas. A number of 100 match-up data sets of GLI-derived and SIMBADA-measured normalized water-leaving radiance (nLW) and aerosol optical thickness (AOT) were sorted out, using objective selection criteria, and analyzed. The Root-Mean-Square (RMS) difference between GLI and SIMBADA nLW was about 0.32 µW/cm2/nm/sr for the 412 nm band, showing improvement by 30% in RMS difference with respect to the conventional “version 1” GLI atmospheric correction algorithm, and the mean difference (or bias) was reduced significantly. For AOT, the RMS difference was 0.1 between GLI estimates and SIMBADA measurements and the bias was small (a few 0.01), but the Angstrom exponent was systematically underestimated, by 0.4 on average, suggesting a potential GLI calibration offset in the near infrared. The nLW differences were not correlated to AOT, although performance was best in very clear conditions (AOT less than 0.05 in the 865 nm band). Despite the relatively large scatter between estimated and measured nLW, the derived chlorophyll-a concentration estimates, applying the same ratio algorithm (GLI OC4V4) to GLI and SIMBADA, were consistent and highly correlated in the range of 0.05–2 µg/l. The large variability in chlorophyll-a concentration estimate for clear clean water areas (e.g. with the concentration range lower than about 0.05 µg/l) turns out to be due to the nature of the “band ratio” based in-water algorithm.