Atmospheric Correction Algorithm Research Articles

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 to 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Construction of Aerosol Model and Atmospheric Correction in the Coastal Area of Shandong Peninsula

Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance (Rrs) products in the nearshore waters of the Shandong Peninsula, this study develops an aerosol model based on aerosol data collected from the Mu Ping site in the coastal area of the Shandong Peninsula, enabling tailored atmospheric correction for this specific region. Given the pronounced seasonal variations in aerosol optical properties, monthly aerosol models were developed. The monthly aerosol model is derived using the average values of aerosol microphysical properties. Compared to the standard aerosol model, this model is more effective in characterizing the absorption and scattering characteristics of aerosols in the study area. Corresponding lookup tables for the aerosol model were created and integrated into the NIR-SWIR atmospheric correction algorithm. According to the accuracy evaluation indexes of RMSD, MAE, and UPD, it can be found that the atmospheric correction results of the aerosol model established in this paper are better than those of the standard aerosol model, especially in the 547 nm band. It demonstrates that the new aerosol model outperforms the standard model in atmospheric correction performance. With the increasing availability of aerosol observational data, the aerosol model is expected to become more accurate and applicable to other satellite missions.

Quantifying decadal stability of lake reflectance and chlorophyll-a from medium-resolution ocean color sensors

Multi-decadal time-series of Lake Water-Leaving Reflectance (LWLR), part of the Lakes Essential Climate Variable, have typically been interrupted for the 2012–2016 period due to lack of an ocean color sensor with capabilities equivalent to MERIS (2002−2012) and OLCI (2016 - present). Here we assess, for the first time, the suitability of MODIS/Aqua to estimate LWLR and the derived concentration of chlorophyll-a (Chla) at the global scale across optically complex water types, in an effort to fill these information gaps for climate studies. We first compare the normalized water-leaving reflectance (Rw) derived from two atmospheric correction algorithms (POLYMER and L2gen) against in situ observations. POLYMER shows superior performance, considering the agreement with in situ measurements and the number of valid outputs. An extensive assessment of nine Chla algorithms is then performed on POLYMER-corrected Rw from MODIS observations. The algorithms are tested both in original parameterizations and following calibration against in situ measurements of Chla. We find that the performance of algorithms parameterized per Optical Water Type (OWT) allows considerable improvement of the global Chla retrieval capability. Using 3 years of overlapping observations between MODIS/Aqua and MERIS (2009–2011) and OLCI (2017–2019), respectively, MODIS-derived reflectance and Chla products showed a reasonable degree of long-term stability in 48 inland water bodies. These water bodies, therefore, mark the candidates to study long-term environmental change.

Using the automated HYPERNETS hyperspectral system for multi-mission satellite ocean colour validation in the Río de la Plata, accounting for different spatial resolutions

Validation of water reflectance using in situ data is essential to ensure the quality of ocean colour satellite-derived products useful for water quality monitoring, like turbidity and chlorophyll-a concentration. Since December 2021, the HYPERNETS automated hyperspectral system has been collecting data in the optically complex and highly turbid waters of the Río de la Plata, an ideal scenario for testing atmospheric correction algorithms’ performance. The site, located 60 km south of Buenos Aires (Argentina), is described in relation to the water reflectance spectral features and variability using high spatial resolution imagery and a methodology is proposed to objectively select a sensor-specific location of a reference pixel for satellite validation. Six months of data is used to evaluate surface water reflectance operational products from multi-spectral systems like Landsat 8&9/OLI (L89/OLI), Sentinel-2/MSI (S2/MSI) & Sentinel-3/OLCI (S3/OLCI), and PlanetScope SuperDoves (PS/SD), and also non standard products for Aqua/MODIS (Aqua/MODIS) and SNPP&JPSS1/VIIRS (SJ/VIIRS) missions. Moreover, the standard surface water reflectance product from the hyperspectral PRISMA mission could also be evaluated. The matchups show general good results when in situ measurements are compared to L2 standard products of high spatial resolution sensors that use land-based atmospheric correction approach, if sun glint contamination is avoided. Low mean relative percentage difference was found for S2/MSI (2.45%) and L89/OLI (−3.52%), but higher for PS/SD (30.7%). In turn, S3/OLCI medium resolution also showed low mean relative differences (2.31%), while SJ/VIIRS and Aqua/MODIS showed larger and negative differences (−16.35 for SJ/VIIRS and −35.6% for Aqua/MODIS) which showed a clear increase towards the shortest blue bands. The results show the great potential of the HYPERNETS automated system to provide high quality and quantity of data for validation of satellite data at all visible and near infrared (VNIR, 400–900 nm) wavelengths in a multi-mission perspective.

Atmospheric correction based on the optimal solution of aerosol mix ratio: A case study for Pearl River Estuary

In ocean color remote sensing, the presence of absorbing aerosols and turbid waters will lead to increased errors in atmospheric correction (AC) for coastal regions. In order to reduce these errors, an alternative AC algorithm has been proposed based on the in situ measurements of the Pearl River Estuary (PRE) and the candidate aerosol models including continental, maritime, and absorbing urban aerosols. The new AC algorithm optimally solved the equation set which is constructed by the multi-spectral relationships of remote sensing reflectance (Rrs ) and the aerosol reflectance of each aerosol model. The performances of the new AC were evaluated using the simulated dataset and the in situ Rrs . The results indicated that this algorithm can effectively reduce the overestimation of aerosol reflectance caused by absorbing aerosols, and can retrieve reasonable Rrs values in PRE.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

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.

A new combined atmospheric correction algorithm for GOCI-2 Data over coastal waters assessed by long-term satellite ocean color platforms

ABSTRACT Accurate atmospheric correction (AC) is essential in retrieving ocean and coastal water ecosystem products from ocean colour remote sensing data. Generally, the atmospheric correction algorithms that are based on Near Infrared (NIR) bands are effective in clear water, whereas the algorithm based on ultraviolet (UV) bands deliver accurate results in turbid water. A practical approach is to employ a UV-NIR combined algorithm that uses both UV and NIR bands for clear and turbid water, respectively. However, currently, the UV-NIR algorithm generates a data discontinuity pattern when a single threshold value is used, as the coastal water exhibits a continuous variability between clear and turbid states. To address this issue, we propose a ‘clear and turbid water transition evenly combination’ algorithm that utilizes UV and NIR bands of the Second Geostationary Ocean Colour Imager (GOCI-2). We evaluated the new algorithm’s performance by comparing GOCI-2 images with in-situ data, and the results showed good agreement between the retrieved Remote Sensing Reflectance ( R rs ) values at visible bands and the in-situ observations. The new AC algorithm can achieve better precision, with Mean Absolute Percentage Difference (MAPD) reduced by 6.6 95.3% and 5.2 18.9% compared with UV AC and the standard AC algorithm employed by the Korea Ocean Satellite Center (KOSC).

UAV and satellite remote sensing for inland water quality assessments: a literature review.

High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.

Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca

The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouth of the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastal system defined by its high suspended loads and relatively low colored dissolved organic matter. The typically high turbidity of these waters makes the BB-AERONET-OC distinctive within the AERONET-OC network stations, providing exceptional opportunities not only for the validation of atmospheric correction algorithms but also for the development of regional algorithms for coastal complex waters. A SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM) instrument was deployed in January 2020 in the upper rail of a Mareograph Tower, a 15 m tall structure, located 10 miles away from the coast in optically deep waters. In this work we used the remote sensing reflectance (Rrs) derived from the BB-AERONET-OC measurements along with in situ hyperspectral radiometric data to classify optical water types (OWTs). We assigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., and OWTs were further characterized with the concentrations of optically significant components (chlorophyll-a and suspended particulate matter) and inherent optical properties (absorptions of phytoplankton, non-algal particles, and dissolved organic matter), retrieved from water samples obtained simultaneously with radiometric spectra. Based on a match-up exercise with in situ data, different schemes of atmospheric correction methods were applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operational product OLCI Level 2 European Space Agency (ESA) standard (hereafter referred to as “Standard Neural Network (NN)”) proves to be the most suitable atmospheric correction algorithm, which was then used to describe spatial patterns and temporal variability of the different OWTs in the region. The BB-AERONET-OC site is located in a sharp transition between estuarine and coastal waters that present contrasting optical conditions: OWT 4 dominates over time (73.72% of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and 5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5, which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts the eutrophic coastal waters of the inner shelf. The variability of OWTs and the relative contribution of organic and inorganic compounds to the suspended material would be mostly related with the prevalence of northwest winds in the area, which would drive the export of estuarine sediments to the shelf.

Validation of satellite-derived water-leaving reflectance in contrasted French coastal waters based on HYPERNETS field measurements

Since 2021, two autonomous HYPERNETS (A new hyperspectral radiometer integrated in automated networks of water and land bidirectional reflectance measurements for satellite validation) stations are operated in contrasted French coastal waters: one in the center of an optically complex coastal lagoon and one at the mouth of a highly turbid estuary. These stations perform predefined sequences of above-water hyperspectral radiometric measurements following a strict viewing geometry. The data recorded by the ®HYPSTAR radiometer is automatically transmitted to servers for quality-controls then computation of the water-leaving reflectance signal. Numerous matchups were identified with high (Sentinel2-MSI and Landsat8/9-OLI) and medium (Sentinel3-OLCI and Aqua-MODIS) spatial resolution satellite data and are analyzed to assess the performance of different atmospheric correction algorithms (Sen2Cor, ACOLITE, POLYMER, iCOR, C2RCC, GRS, BPAC, NIR-SWIR). Considering the specifications of each site (i.e., spatial and temporal variations of water optical properties), optimized matchup protocols are first established to guaranty high quality comparisons between satellite products and field measurements. The matchup results highlight the failure and limits of several atmospheric correction algorithms in complex/turbid coastal waters. The importance of accurate sun glint corrections in low to moderately-turbid waters (with the good performances of POLYMER, C2RCC and GRS processors, e.g., errors (MAPE) lower than 25% in the green spectral region) is also shown while the use of dark targets and spectral fitting to estimate the aerosol contributions is proved to be the most accurate method in the case of turbid waters (with Sen2Cor and ACOLITE errors (MAPE) lower than 20% in the visible and near-infrared spectral regions).

Superyachts could support satellite ocean colour validation

Visible spectral radiometry of seawater, often referred to as ocean colour, from space, provides a synoptic view of surface phytoplankton, and other optically-active constituents, at high temporal resolution, that is unsurpassed by any other technology. Yet, in-situ observations of ocean colour are critical to the success of the satellite, tracking the calibration of the radiometers and validating atmospheric correction algorithms. Owing to the high cost of commercial field radiometers, as well as the high costs associated with ocean-based field work, ocean colour scientists are plagued by a sparsity of high quality in-situ radiometric observations, particularly in remote regions. In this perspective article, we highlight potential to increase the number of in-situ observations of ocean colour by harnessing superyachts. Using openly-available data processing software, we show that automated ocean colour data collected using a superyacht can be used for the validation of an ocean colour satellite, with comparable results to traditional validation studies. Reaching out to wealthy citizen scientists may help fill gaps in our ability to monitor the colour of the ocean.

Variation of Satellite-Based Suspended Sediment Concentration in the Ganges–Brahmaputra Estuary from 1990 to 2020

The Ganges–Brahmaputra estuary, located in the northern Bay of Bengal, is situated within the largest delta in the world. This river basin features a complex river system, a dense population, and significant variation in watershed vegetation cover. Human activities have significantly impacted the concentration of total suspended matter (TSM) in the estuary and the ecological environment of the adjacent bay. In this study, we utilised the Landsat series of satellite remote sensing data from 1990 to 2020 for TSM retrieval. We applied an atmospheric correction algorithm based on the general purpose exact Rayleigh scattering look-up-table (LUT) and the shortwave-infrared (SWIR) bands extrapolation to Landsat L1 products to obtain high-precision remote sensing reflectance. In conjunction with the normalised difference vegetation index (NDVI), precipitation, and discharge data, we analysed the variation and influencing mechanisms of TSM in the Ganges–Brahmaputra estuary and its surrounding areas. We revealed notable seasonal variation in TSM in the estuary, with higher concentrations during the wet season (May–October) compared to the dry season (the rest of the year). Over the period from 1990 to 2020, the NDVI in the watershed exhibited a significant upward trend. The outer estuarine regions of the Hooghly River and Meghna River displayed significant decreases in TSM, whereas the Baleswar River, which flows through mangrove areas, showed no significant trend in TSM. The declining trend in TSM was mainly attributed to land-use changes and anthropogenic activities, including the construction of embankments, dams, and mangrove conservation efforts, rather than to runoff and precipitation. Surface sediment concentration and chlorophyll in the northern Bay of Bengal exhibited slight increases, which means the limited influence of terrestrial inputs on long-term change in surface sediment concentration and chlorophyll in the northern Bay of Bengal. This study emphasises the impact of human activities on the river–estuary–coast continuum and sheds light on future sustainable management.

Assessing and Improving the Accuracy of Visible Infrared Imaging Radiometer Suite Ocean Color Products in Environments with High Solar Zenith Angles

Utilizing in situ measurement data to assess satellite-derived long-term ocean color products under different observational conditions is crucial for ensuring data quality and integrity. In this study, we conducted an extensive evaluation and analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing reflectance (Rrs) products using long-term OC-CCI in situ data from 2012 to 2021. Our research findings indicate that, well beyond its designed operational lifespan, the root mean square difference accuracy of VIIRS Rrs products across most spectral bands remains superior to 0.002 (sr−1). However, VIIRS Rrs products in shorter wavelength bands (e.g., at 412 nm) have exhibited significantly lower accuracy and a long-term bias in recent years. The annual precision of VIIRS Rrs products demonstrated a declining trend, particularly in coastal or eutrophic waters. This degradation in accuracy highlights the imperative for continuous monitoring of VIIRS performance and further advancements in the atmospheric correction algorithm, especially to address satellite records at high solar zenith angles (SZAs) and observation zenith angles (OZAs). Our analysis indicates that, in observation environments with high SZAs (greater than 70°), the accuracy of VIIRS Rrs products has declined by nearly 50% compared to typical solar zenith angle observation conditions. To address the challenge of declining accuracy under large observation geometries, we introduced the neural network atmospheric correction model (NN-V). Developed based on meticulously curated VIIRS products, the NN-V model exhibits outstanding performance in handling VIIRS data in conditions of extensive observation geometries. During the winter season in high-latitude marine regions, the NN-V model demonstrates a remarkable enhancement in ocean color product coverage, achieving an increase of nearly 20 times compared to traditional methods.

Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

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

Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 μg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 μg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 μg·m-3 to 112.780 μg·m-3 and the lowest values ranging from 5.000 μg·m-3 to 7.899 μg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 μg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Intelligent Atmospheric Correction Algorithm for Polarization Ocean Color Satellite Measurements Over the Open Ocean

Intelligent Atmospheric Correction Algorithm for Polarization Ocean Color Satellite Measurements Over the Open Ocean

A Neural Network-Based Atmospheric Correction Algorithm for GOCI Imagery Over Coastal Waters

A Neural Network-Based Atmospheric Correction Algorithm for GOCI Imagery Over Coastal Waters