Atmospheric Correction Algorithm Research Articles

The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth

Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring the surface-based PM2.5 concentration due to their extensive spatial and temporal coverage. Satellite-derived PM2.5 estimations strongly rely on an accurate representation of the relationship between ground-level PM2.5 and satellite aerosol optical depth (AOD). Due to the limitations of satellite AOD data, most studies have examined the relationship at a coarse resolution (i.e., ≥ 10 km); thus, more effort is still needed to better understand the relationship between “in situ” PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Therefore, considerable uncertainty still exists in satellite-derived PM2.5 estimations due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD as well as its plausible impact factors, including meteorological parameters and topography, in mainland China during 2019, at various spatial and temporal scales. The coefficient of variation, the Pearson correlation coefficient and the slope of the linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China, and the linear slope was larger on average in northern inland regions than in other areas. Temporally, the PM2.5–AOD correlation peaked at noon and in the afternoon, and reached a maximum in winter. Simultaneously, considering relative humidity (RH) and the planetary boundary layer height (PBLH) in the relationship can improve the correlation, but the effect of RH and the PBLH on the correlation varied spatially and temporally with respect to both strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as the Sichuan Basin, the Shanxi–Shaanxi basins and the Junggar Basin. MAIAC 1 km AOD can better represent the ground-level fine particulate matter in most domains with exceptions, such as in very high terrain (i.e., Tibetan Plateau) and northern central China (i.e., Qinghai and Gansu). The findings of this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status of mainland China.

Long-term changes in aerosol loading over the ‘BIHAR’ State of India using nineteen years (2001–2019) of high-resolution satellite data (1 × 1 km2)

Indo-Gangetic Plain (IGP) in the Indian sub-continent faces massive aerosol loading, for which it is regarded as a global air pollution hotspot. This study examined the spatial variation in columnar aerosol loading (2001–2019) over the eastern state of Bihar and its 38 administrative districts affected by transboundary transport and local emissions. In the current study, aerosol optical depth (AOD0.55μm) retrieved at 1 × 1 Km2 resolution by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor using the Multi-Angle Implementation of Atmospheric Correction algorithm (MCD19A2, Collection 6) was analyzed. A significant increase in AOD from 0.49 to 0.68, with an inter-annual variability of 12.0%, was observed. The highest seasonal aerosol loading of the range 0.51–0.75 was observed during the post-monsoon (OND) and winter (JF) seasons due to long-range transport of pollutants from upper and central IGP regions and aerosols emitted locally, which further was aggravated by poor metrological conditions. AOD was not found to vary significantly with the land use pattern (e.g., urban, rural, and background), implying the substantial influence of regional transport. A significant increase in the annual AOD trend (0.0106 year−1) and seasonal trends (0.0072–0.0182 year−1) was observed for the analysis period. Districts located along the Ganga river stretch exhibited the highest annual AOD rate with an overall percentage increase of 40–50%. Low to moderate model performance (R2:0.25–0.65; MAPE: 15.2–26.7%) was exhibited by Auto-Regressive Integrated Moving Average (ARIMA) time series model over the study area for the successive two years (2020 and 2021), emphasizing the districts with a potential for high aerosol loading that requires immediate addressing under Business as usual (BAU) scenarios. The overall study augments policymakers with decision-making support to instigate air quality measures at the state epicenters. The study also recommends inter-state coordination to develop an integrated airshed management approach for holistic improvement in state air quality.

A Novel Framework of Integrating UV and NIR Atmospheric Correction Algorithms for Coastal Ocean Color Remote Sensing

Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV- and NIR-based atmospheric correction (AC) algorithms for coastal ocean color remote sensing. The new approach is called UV-NIR combined AC algorithm. The performance of the new algorithm is evaluated based on match-ups between GOCI images and the AERONET-OC dataset. The results show that the values of retrieved Rrs (Remote Sensing Reflectance) at visible bands agreed well with the in-situ observations. Compared with the SeaDAS (SeaWiFS Data Analysis System) standard NIR algorithm, the new AC algorithm can achieve better precision and provide more available data.

Atmospheric Correction of DSCOVR EPIC: Version 2 MAIAC Algorithm

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) provides multispectral images of the sunlit disk of Earth since 2015 from the L1 orbit, approximately 1.5 million km from Earth toward the Sun. The NASA’s Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been adapted for DSCOVR/EPIC data providing operational processing since 2018. Here, we describe the latest version 2 (v2) MAIAC EPIC algorithm over land that features improved aerosol retrieval with updated regional aerosol models and new atmospheric correction scheme based on the ancillary bidirectional reflectance distribution function (BRDF) model of the Earth from MAIAC MODIS. The global validation of MAIAC EPIC aerosol optical depth (AOD) with AERONET measurements shows a significant improvement over v1 and the mean bias error MBE = 0.046, RMSE = 0.159, andR= 0.77. Over 66.7% of EPIC AOD retrievals agree with the AERONET AOD to within ± (0.1 + 0.1AOD). We also analyze the role of surface anisotropy, particularly important for the backscattering view geometry of EPIC, on the result of atmospheric correction. The retrieved BRDF-based bidirectional reflectance factors (BRF) are found higher than the Lambertian reflectance by 8–15% at 443 nm and 1–2% at 780 nm for EPIC observations near the local noon. Due to higher uncertainties, the atmospheric correction at UV wavelengths of 340, 388 nm is currently performed using a Lambertian approximation.

Inferring iron oxides species content in atmospheric mineral dust from DSCOVR EPIC observations

Abstract. The iron-oxide content of dust in the atmosphere and most notably its apportionment between hematite (α-Fe2O3) and goethite (α-FeOOH) are key determinants in quantifying dust's light absorption, its top of atmosphere UV radiances used for dust monitoring, and ultimately shortwave dust direct radiative effects (DRE). Hematite and goethite column mass concentrations and iron-oxide mass fractions of total dust mass concentration were retrieved from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) measurements in the ultraviolet–visible (UV–Vis) channels. The retrievals were performed for dust-identified aerosol plumes using aerosol optical depth (AOD) and spectral imaginary refractive index provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over six continental regions (North America, North Africa, West Asia, Central Asia, East Asia, and Australia). The dust particles are represented as an internal mixture of non-absorbing host and absorbing hematite and goethite. We use the Maxwell–Garnett effective medium approximation with carefully selected complex refractive indices of hematite and goethite that produce mass fractions of iron oxides species consistent with in situ values found in the literature to derive the hematite and goethite volumetric/mass concentrations from MAIAC EPIC products. We compared the retrieved hematite and goethite concentrations with in situ dust aerosol mineralogical content measurements, as well as with published data. Our data display variations within the published range of hematite, goethite, and iron-oxide mass fractions for pure mineral dust cases. A specific analysis is presented for 15 sites over the main dust source regions. Sites in the central Sahara, Sahel, and Middle East exhibit a greater temporal variability of iron oxides relative to other sites. Niger site (13.52° N, 2.63° E) is dominated by goethite over Harmattan season with median of ~2 weight percentage (wt.%) of iron-oxide. Saudi Arabia site (27.49° N, 41.98° E) over Middle East also exhibited surge of goethite content with the beginning of Shamal season. The Sahel dust is richer in iron-oxide than Saharan and northern China dust except in Summer. The Bodele Depression area shows a distinctively lower iron-oxide concentration (~1 wt. %) throughout the year. Finally, we show that EPIC data allow to constrain the hematite refractive index. Specifically, we select 5 out of 13 different number of hematite refractive indices widely variable in published laboratory studies by constraining the iron-oxide mass ratio to the known measured values. Provided climatology of hematite and goethite mass fractions across main dust regions of the Earth will be useful for dust shortwave DRE studies and climate modeling.

Using Satellite Data for Environmental Justice Efforts in California

BACKGROUND AND AIM: Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations, but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high spatial resolutions, compared to air pollution exposure estimates based on ground monitors alone. METHODS: Daily PM₂.₅ estimates for 2015-2018 were estimated at a 1 km² resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, and compared to federal ground monitors in order to assess the utility of highly-refined spatiotemporal air pollution data in the 13 communities included in the California Community Air Protection Program. RESULTS:Results suggest that regulatory monitoring networks are unable to identify pollution hotspots within these communities without the addition of high-resolution satellite data. However, day-to-day temporal variability is generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. CONCLUSIONS:These findings can help inform strategies for use of remote sensing data in environmental justice efforts including the screening of locations with air pollution exposures that are not well-represented by existing ground-based air pollution monitors. KEYWORDS: Air Pollution, Environmental Disparities, Environmental Justice, Exposure Assessment, Particulate Matter

Comparison of thresholding methods for shoreline extraction from Sentinel-2 and Landsat-8 imagery: Extreme Lake Salda, track of Mars on Earth

Lake Salda's extreme environment is geologically similar to Jezero Crater paleolake on Mars due to the formation of stromatolites and its extremely alkaline and cold water. It is critical to accurately determine the shoreline in the littoral zone where stromatolite formation occurs, in alkaline clean lakes like Salda, which contain traces of life on Mars, and in monitoring the change that occurs with climate and anthropogenic effect. The performance of global automatic thresholding algorithms on shoreline determination from NDWI and mNDWI water indices is compared in this study using Sentinel-2 and Landsat-8 images atmospherically corrected by different algorithms. Satellite images data acquired on August 12, 2020 for Sentinel-2 and August 11, 2020 for Landsat-8 on Lake Salda were used to determining the shoreline. The shoreline data measured in situ concurrently with the Sentinel-2 satellite acquisition was used as reference data. In the accuracy analysis, ground control points created inside and outside the lake at a distance of 1 pixel and 0.5 pixel to the reference shoreline for each satellite image were used. The performance of the optimal threshold values determined by each thresholding algorithm in the water index images was assessed using Kappa coefficient, Overall Accuracy (OA), %OA of Inside (%OAinside) and %OA of Outside (%OAoutside) statistics metrics. The optimal threshold values vary depending on the image and the atmospheric correction algorithm applied to the image. The NDWI index produces more accurate results in both Sentinel-2 and Landsat-8 satellite images. While atmospheric correction algorithms do not affect the results in Landsat-8 images, the Sen2Cor algorithm outperforms iCOR in Sentinel-2 images. For thresholding algorithms to be used in different water index and satellite images, Intermode, Isodata, IJ_Isodata, Minimum and Otsu algorithms in Landsat-8_LaSRC_NDWI and Landsat-8_iCOR_NDWI images, and Intermode, Minimum and Huang algorithms in Sentinel-2_Sen2Cor_NDWI images produce the best results. Because the Minimum algorithm causes significant gaps in the lake surface, the Huang and Intermode algorithms should be used for Sentinel-2_Sen2Cor_NDWI images. The 0 (zero) threshold value in the water indices images has a high accuracy only in the NDWI water indices generated from the Landsat-8 image.

Uncertainty of atmospheric correction algorithms for chlorophyll α concentration retrieval in lakes from Sentinel-2 data

One of the largest uncertainties in remote sensing data comes from atmospheric influence. This research aims to explain the uncertainties emanating from atmospheric correction (AC) product selection and how they influence chlorophyll α concentration retrieval in lakes in eastern Lithuania. We tested seven products from six AC processors (Acolite, Acolite Rayleigh, iCOR, Sen2Cor, C2RCC, C2X, and POLYMER) and 10 chlorophyll α retrieval algorithms with different architectures. The uncertainty of AC products transferred to chlorophyll α concentrations, and large differences in the chlorophyll α concentrations retrieved using different AC products were observed. The match-up analysis showed that chlorophyll α algorithms based on band difference performed best in terms of a high coefficient of determination and the lowest median bias when used with image-based, Sen2Cor, and TOA data. The results of this study highlight the uncertainties of AC products as well as how the selection of the chlorophyll α retrieval algorithm can mitigate the influence of AC selection.

Influence of atmospheric adjacency effect on top-of-atmosphere radiances and its correction in the retrieval of Lambertian surface reflectivity based on three-dimensional radiative transfer

In satellite-based and airborne imagery, the observed radiances reflected by a certain pixel at the surface are additionally influenced by reflections from the neighboring surface pixels and multiple scatterings due to atmospheric components (mainly cloud and aerosol particles) into the observational solid angle of the imaging camera. This phenomenon is commonly referred to as the atmospheric adjacency effect. This three-dimensional (3D) radiative transfer effect is caused by spatial inhomogeneities of the surface reflectivity and the atmospheric properties. Based on the recently published 3D radiative transfer code LEIPSIC (Light Estimator Including Polarization, Surface Inhomogeneities, and Clouds), a new atmospheric correction (AC) algorithm is proposed to consider for the atmospheric adjacency effect when estimating the surface reflectivity from satellite or airborne imagery. The effectiveness of the new AC algorithm is quantified and compared to the results based on the independent pixel approximation (IPA) radiative transfer approach. It is shown that the image blurring caused by the atmospheric adjacency effect and the error of reflectivity retrievals are reduced by 80% using the new AC algorithm. Furthermore, the simulations demonstrate that the vertical profile of the atmospheric properties is crucial in determining the quality of the AC.

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.

Making Landsat 5, 7 and 8 reflectance consistent using MODIS nadir-BRDF adjusted reflectance as reference

The Landsat satellite data applications have been broadened by the improvement in data consistency, e.g., advances in calibration and atmospheric correction algorithms. However, there are still inter-sensor inconsistencies of surface reflectance caused by differences in sensor spectral response and residuals in calibration and atmospheric correction. Such inconsistencies tend to entangle with the land surface change, especially for the subtle changes such as vegetation responses to climate change. We propose a Bayesian approach to reduce inter-sensor inconsistencies by making Landsat 5, 7 and 8 reflectance consistent with MODIS nadir BRDF-adjusted reflectance (NBAR). The Bayesian MODIS-consistent Landsat reflectance should be similar to the MODIS NBAR when it is degraded to the MODIS resolution considering the point spread function (PSF). The MODIS NBAR PSF is modelled as a Gaussian function with empirically derived and band specific parameters. The relationships between the MODIS-consistent Landsat reflectance and the two available reflectance (i.e., the MODIS NBAR and observed Landsat reflectance) are built in the Bayesian framework. The Bayesian MODIS-consistent Landsat reflectance is then derived using the computationally efficient conjugate gradient descent method. The algorithm was applied to Landsat 5, 7 and 8 surface reflectance over five study areas in Brazil, China, France, Russia and USA, and compared with a linear regression-based reflectance adjustment method. Visual difference was evident in the MODIS NBAR and the observed Landsat reflectance and the relative root-mean-squared differences (rRMSD) ranged from 3.16% to 67.72% and R2 from 0.037 to 0.934. Both the Bayesian and linearly MODIS-consistent Landsat reflectance are visually more consistent with the MODIS NBAR. The average rRMSD and R2 improvements of the Bayesian MODIS-consistent Landsat reflectance over the observed Landsat reflectance were 21.36% and 0.19, 3.38% and 0.11, 5.25% and 0.07, 3.14% and 0.11, 3.49% and 0.08, and 5.72% and 0.09 for the blue, green, red, near infrared, and two shortwave infrared bands, respectively. The linearly MODIS-consistent Landsat reflectance had less rRMSD improvements: 18.26%, 1.46%, 3.02%, 1.86%, 1.97% and 3.36% for the six bands respectively, and had no R2 improvements. The linear regression derived using the whole image pixels has low performance in adjusting some extreme reflectance values that are not well represented in the image. In contrast, the Bayesian method derives the reflectance image that is consistent to MODIS at pixel level.

Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.

An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image

The adjacency effect can blur and reduce the contrast of satellite images. For low visibility, the adjacency effect has a crucial impact on submeter-scale spatial resolution optical (SM) satellite images. Therefore, SM satellite images are preprocessed with an atmospheric correction before performing visual interpretation and quantitative research. This atmospheric correction includes the adjacency effect (expressed by the average background reflectance, ABR) correction and atmospheric intrinsic reflectance correction. ABR is related to the spatial distance from the central pixel to its surrounding pixels (SDCS) and is affected by the reflectance difference between the central pixel and its surrounding pixels (RDCS). Existing methods to calculate ABR do not include the effect of RDCS. Therefore, in this study, we use equivalent average background reflectance (EABR) instead of ABR used in the 6S model. EABR considers the effects of the strengths of the aerosol optical depth (AOD), SDCS, and RDCS on the adjacency effect. Compared with ABR, EABR can better represent the adjacency effect in an SM satellite image. We develop an adaptive atmospheric correction algorithm (called adaptive-AC) based on EABR. The adaptive characteristic of adaptive-AC is that it can adjust the contribution factor (or weighted value) of the background pixel to the adjacency effect according to the SDCS and RDCS. The application of adaptive-AC to a panchromatic band image of WorldView-3 (WV-3) reveals that adaptive-AC can significantly improve image quality, with the reflectance of pixels in the corrected image processed by adaptive-AC being close to the actual reflectance. Comparison with the results of the atmospheric correction algorithm in the 6S model (6S-AC) indicates that adaptive-AC is more suitable for SM satellite image preprocessing than 6S-AC approaches. There is no clear theory or formula available to calculate the exact value of the adjacency effect range (Rad). Therefore, different Rad values are used in applying the adaptive-AC and 6S-AC. The results show that the larger is the Rad used to construct the adjacency effect model, the more satisfactory is the atmospheric correction.

Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing

In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.

Tide-Induced Variability and Mechanisms of Surface Suspended Sediment in the Zhoushan Archipelago along the Southeastern Coast of China Based on GOCI Data

The variations and dynamics of suspended sediment in the coastal waters around the Zhoushan Archipelago are complex due to strong tidal dynamics. This study aims to delineate the tide-induced variability of surface suspended sediment and reveal its mechanisms in the Zhoushan Archipelago based on Geostationary Ocean Color Imager (GOCI) data. Suspended sediment concentration (SSC) estimations through the ultraviolet wavelength atmospheric correction (UV-AC) algorithm and a tuned empirical inversion algorithm using extensive in situ measurements are presented. GOCI-processed remote sensing reflectance (Rrs) is validated using field data and is cross-validated with Landsat 8 Operational Land Imager (L8/OLI) measurements, respectively. The estimated SSC is validated with independent datasets. The validations reveal that GOCI-processed Rrs is reasonable and that the GOCI-retrieved SSC is accurate and can be used to quantify SSC distributions and variations in the Zhoushan Archipelago. The variations of the SSC in the study area are closely related to the temporal variations of the tidal level. High SSCs often occur in the middle of ebb or flood tides due to large tidal discharge and high tidal velocity. Significant hourly variations of the SSC are mainly controlled by tidal forces. Dynamic mechanism analysis indicates that during neap and middle tides, the local high SSC is mainly attributed to the sediment resuspension process driven by tidal currents; in addition, during the spring tide, the variations of the SSC are simultaneously modulated by sediment resuspension and horizontal advection processes.

Consistency of Suspended Particulate Matter Concentration in Turbid Water Retrieved from Sentinel-2 MSI and Landsat-8 OLI Sensors.

Research on the consistency of suspended particulate matter (SPM) concentration retrieved from multisource satellite sensors can serve as long-time monitoring of water quality. To explore the influence of the atmospheric correction (AC) algorithm and the retrieval model on the consistency of the SPM concentration values, Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Imager (MSI) images acquired on the same day are used to compare the remote sensing reflectance (Rrs) SPM retrieval values in two high-turbidity lakes. An SPM retrieval model for Shengjin Lake is established based on field measurements and applied to OLI and MSI images: two SPM concentration products are highly consistent (R2 = 0.93, Root Mean Squared Error (RMSE) = 20.67 mg/L, Mean Absolute Percentage Error (MAPE) = 6.59%), and the desired results are also obtained in Chaohu Lake. Among the four AC algorithms (Management Unit of the North Seas Mathematical Models (MUMM), Atmospheric Correction for OLI’lite’(ACOLITE), Second Simulation of Satellite Signal in the Solar Spectrum (6S), Landsat 8 Surface Reflectance Code & Sen2cor (LaSRC & Sen2cor)), the two Rrs products, as well as the final SPM concentration products retrieved from OLI and MSI images, have the best consistency when using the MUMM algorithm in SeaWIFS Data Analyst System (SeaDAS) software. The consistency of SPM concentration values retrieved from OLI and MSI images using the same model or same form of models is significantly better than that retrieved by applying the optimal models with different forms.

Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery

Remote monitoring of chlorophyll-a (chla) has been widely used to evaluate the trophic state of inland and coastal waters, however, there is still much uncertainty in the algorithms applied in different optical water types. The influence of different atmospheric correction (AC) processors, which can also provide correction for sunglint and adjacency effects, on the retrieved chla is poorly understood. In this study, state-of-the-art atmospheric correction and chla algorithms are evaluated using Sentinel-2 MSI imagery in the Mundaú-Manguaba Estuarine-Lagoon System (MMELS), a productive tropical system that consists of two turbid lagoons of different optical water types (OWT). We compared the performance of six AC processors, with the addition of sunglint correction for two of them, with field measured water reflectance. There was difficulty in correcting for the atmospheric effects, especially for bands 2, 3 and 8A. Overall, C2X showed the best performance over MMELS, but with sunglint correction, ACOLITE and GRS provided the most consistent water reflectance (ρw). Sunglint correction might be essential for retrieving accurate ρw in most low-latitude water bodies. We also found that in Mundaú, the dense urban area surrounding it likely caused heavy adjacency effects in the satellite-retrieved reflectance, and thus correction for it is necessary. We also compared the performance of six chla algorithms recommended for the OWTs present in MMELS in addition to a widely applied and a global chla algorithm in retrieving this variable using both field and satellite reflectance, in this case corrected with the three best performing processors. For the in situ data, most algorithms performed well in Manguaba lagoon, while in Mundaú lagoon the semi-analytical NIR-red ratio (2SAR) algorithm was the most consistent model, and in both cases the locally calibrated algorithms outperformed the global algorithm. When retrieving chla with the satellite-derived ρw, considerably poorer results were produced, especially in Mundaú lagoon. The global algorithm was found to be especially sensitive to the atmospheric effects. We also found that the quality of AC provided by the algorithms is not a general predictor of the performance of the chla models, even when analysing individual bands separately, while the relationship between chla concentration and the ratio of bands used by most algorithms can be. Despite containing distinct water characteristics, chla can be modelled using a single algorithm, 2SAR, calibrated for MMELS as a whole, with r2 of 0.77 and nRMSE of 38.7%, and we consider that 2SAR has the potential to be a global algorithm for these OWTs, provided that it is recalibrated for a large dataset of satellite-derived BOA reflectance. We recommend that further studies explore the impacts of AC, sunglint and adjacency effects on the performance of chla algorithms, in order to delineate the most suitable combinations of AC + chla algorithms for the variable OWTs, in an effort to provide the basis for global-scale retrievals of this pigment using medium-resolution sensors such as MSI and OLI.

Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters

The performance of different atmospheric correction algorithms for the Ocean and Land Colour Instrument (OLCI) on board of Sentinel-3 (S3) is evaluated for retrieval of water-leaving radiance reflectance, and derived parameters chlorophyll-a concentration and turbidity in turbid coastal waters in the Belgian Coastal Zone (BCZ). This is performed using in situ measurements from an autonomous pan-and-tilt hyperspectral radiometer system (PANTHYR). The PANTHYR provides validation data for any satellite band between 400 and 900 nm, with the deployment in the BCZ of particular interest due to the wide range of observed Near-InfraRed (NIR) reflectance. The Dark Spectrum Fitting (DSF) atmospheric correction algorithm is adapted for S3/OLCI processing in ACOLITE, and its performance and that of 5 other processing algorithms (L2-WFR, POLYMER, C2RCC, SeaDAS, and SeaDAS-ALT) is compared to the in situ measured reflectances. Water turbidities across the matchups in the Belgian Coastal Zone are about 20–100 FNU, and the overall performance is best for ACOLITE and L2-WFR, with the former providing lowest relative (Mean Absolute Relative Difference, MARD 7–27%) and absolute errors (Mean Average Difference, MAD -0.002, Root Mean Squared Difference, RMSD 0.01–0.016) in the bands between 442 and 681 nm. L2-WFR provides the lowest errors at longer NIR wavelengths (754–885 nm). The algorithms that assume a water reflectance model, i.e. POLYMER and C2RCC, are at present not very suitable for processing imagery over the turbid Belgian coastal waters, with especially the latter introducing problems in the 665 and 709 nm bands, and hence the chlorophyll-a and turbidity retrievals. This may be caused by their internal model and/or training dataset not being well adapted to the waters encountered in the BCZ. The 1020 nm band is used most frequently by ACOLITE/DSF for the estimation of the atmospheric path reflectance (67% of matchups), indicating its usefulness for turbid water atmospheric correction. Turbidity retrieval using a single band algorithm showed good performance for L2-WFR and ACOLITE compared to PANTHYR for e.g. the 709 nm band (MARD 15 and 17%), where their reflectances were also very close to the in situ observations (MARD 11%). For the retrieval of chlorophyll-a, all methods except C2RCC gave similar performance, due to the RedEdge band-ratio algorithm being robust to typical spectrally flat atmospheric correction errors. C2RCC does not retain the spectral relationship in the Red and RedEdge bands, and hence its chlorophyll-a concentration retrieval is not at all reliable in Belgian coastal waters. L2-WFR and ACOLITE show similar performance compared to in situ radiometry, but due to the assumption of spatially consistent aerosols, ACOLITE provides less noisy products. With the superior performance of ACOLITE in the 490–681 nm wavelength range, and smoother output products, it can be recommended for processing of S3/OLCI data in turbid waters similar to those encountered in the BCZ. The ACOLITE processor for OLCI and the in situ matchup dataset used here are made available under an open source license.