Rrs Values Research Articles

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.

Respiratory function testing for guiding ventilator mode conversion in congenital diaphragmatic hernia.

For patients with a congenital diaphragmatic hernia, conventional mechanical ventilation (CMV) and high-frequency oscillatory ventilation (HFOV) are used in initial ventilatory management. HFOV has recently been recommended as a rescue therapy; however, we use HFOV for initial ventilation management, with a preoperative challenge test for CMV conversion and respiratory function testing at the time of CMV conversion. We aimed to compare patient characteristics between CMV conversion- and HFOV-preferred treatment groups. Ventilator settings and blood gases were retrospectively evaluated pre- and post-CMV conversion, and respiratory function tests for compliance of the respiratory system (Crs) and for resistance of the respiratory system (Rrs) were performed during the trial to CMV conversion. No differences were observed between the CMV conversion- and HFOV-preferred groups regarding gestational age, birth weight, and observed/expected lung area-to-head circumference ratios. The median Crs (ml/cmH2 O/kg) and Rrs (cmH2 O･kg/L/s) in the CMV conversion- and HFOV-preferred groups was 0.42 versus 0.53 (p = .44) and 467 versus 327 (p = .045), respectively. The pre and posttrial amount of change in blood gas levels and ventilator parameters in the CMV conversion- and HFOV-preferred groups were as follows: mean airway pressure, -2.0 versus 0 cmH2 O; partial pressure of carbon dioxide, 6.1 versus 2.9 Torr; alveolar-arterial oxygen difference, -39.5 versus -50 Torr; and oxygenation index, -1.0 versus -0.6; respectively. Respiratory function tests were useful in tailoring ventilator settings. Patients with high Rrs values responded better to CMV conversion.

Circulating Oncometabolite 2-hydroxyglutarate as a Potential Biomarker for Isocitrate Dehydrogenase (IDH1/2) Mutant Cholangiocarcinoma.

Isocitrate dehydrogenase (IDH) enzymes catalyze the decarboxylation of isocitrate to alpha-ketoglutarate (αKG). IDH1/2 mutations preferentially convert αKG to R-2-hydroxyglutarate (R2HG), resulting in R2HG accumulation in tumor tissues. We investigated circulating 2-hydroxyglutate (2HG) as potential biomarkers for patients with IDH-mutant (IDHmt) cholangiocarcinoma (CCA). R2HG and S-2-hydroxyglutarate (S2HG) levels in blood and tumor tissues were analyzed in a discovery cohort of patients with IDHmt glioma and CCA. Results were validated in cohorts of patients with CCA and clear-cell renal cell carcinoma. The R2HG/S2HG ratio (rRS) was significantly elevated in tumor tissues, but not in blood for patients with IDHmt glioma, while circulating rRS was elevated in patients with IDHmt CCA. There were overlap distributions of circulating R2HG and total 2HG in patients with both IDHmt and wild-type (IDHwt) CCA, while there was minimal overlap in rRS values between patients with IDHmt and IDHwt CCA. Using the rRS cut-off value of 1.5, the sensitivity of rRS was 90% and specificity was 96.8%. Circulating rRS is significantly increased in patients with IDHmt CCA compare with patients with IDHwt CCA. Circulating rRS is a sensitive and specific surrogate biomarker for IDH1/2 mutations in CCA. It can potentially be used as a tool for monitoring IDH-targeted therapy.

Atmospheric correction under cloud edge effects for Geostationary Ocean Color Imager through deep learning

The Geostationary Ocean Color Imager (GOCI) is widely employed in tracking diurnal dynamics of oceanic conditions. However, the current atmospheric correction (AC) algorithms for GOCI often mask pixels around cloud edges to exclude pixels contaminated by cloud edge effects (CEEs; including stray light, cloud shadows, and cloud adjacent effects (AEs)), which results in massive data loss. In this paper, we propose a novel AC algorithm to correct these CEE-affected pixels based on deep learning (namely, DLACC) to achieve a comparable quality level to those pixels far away from cloud edges. We used the standard near-infrared iterative AC algorithm in SeaDAS (namely, NIR) to obtain Rayleigh-corrected reflectance (Rrc) and remote sensing reflectance (Rrs). Then, high-quality Rrs values were extracted using a quality assurance (QA) algorithm. Next, we obtained 2,821,668 matchups by matching CEE-free noontime Rrs values with CEE-affected Rrc values in a time window of 1 h. These matchups were used to develop DLACC. Validations using in situ data from Aerosol Robotic Network-Ocean Color (AERONET-OC) stations showed that more matchups were obtained with the DLACC model than with the NIR algorithm and Korea Ocean Satellite Center standard AC algorithm in GDPS 2.0 (KOSC), and the accuracies were similar. More importantly, the DLACC algorithm is more tolerant of AEs and reduces AEs in a 2-pixel distance from cloud edges by 10% in the blue bands and by 50% in the green band. As a result, more valid observations are obtained with the DLACC algorithm, with the daily percentage of valid observations (DPVOs) increasing by 71% and 62% over those with the NIR algorithm and the KOSC algorithm, respectively. These increases in valid observations could further result in more consistent patterns in terms of space and time. Our DLACC algorithm can not only be used to process GOCI images but also provide an open framework to develop corresponding deep-learning models for other geostationary satellites.

Atmospheric correction of absorbing aerosols for satellite ocean color remote sensing over coastal waters

The atmospheric correction (AC) of satellite ocean color remote sensing, which is affected by the vertical distribution of absorbing aerosols (AA), is prone to large uncertainties in the waters with AA, especially for remote sensing reflectance (Rrs(λ)) retrievals in the blue and ultraviolet bands. In this study, a new AC algorithm called the Ocean Color–XGBoost with radiative transfer (RT) simulation Retrieval Tool (OC-XGBRT), which is based on extensive radiative simulation and machine learning model, was developed to retrieve the Rrs(λ) at the short wavelength bands of visible light in the presence of AA while considering their vertical distributions. The goal of the OC-XGBRT AC algorithm is to improve the quality of ocean color satellite products over inland and coastal waters with AA and heavy aerosol loadings. The images acquired by Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite (MODIS-Aqua) over six regions with the cases of AA were utilized to assess the performance of OC-XGBRT. The Rrs(λ) retrieved by OC-XGBRT were validated with in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and the SeaWiFS Bio-optical Archive and Storage (SeaBASS). The results showed that OC-XGBRT provided more accurate Rrs(412 nm) and Rrs(443 nm) products than the National Aeronautics and Space Administration (NASA) SeaWiFS Data Analysis System (SeaDAS) v7.5 standard near-infrared (NIR) AC algorithms, the POLYnomial-based approach established for the atmospheric correction of MERIS data (PLOYMER), and the Ocean Color-Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) over coastal and inland waters, with mean absolute percentage deviation (APD) and root mean square error (RMSE) values less than ∼36.9% and ∼ 5.5 × 10−4 sr−1, respectively. Application results of OC-XGBRT in six typical regions further demonstrated that the quality of retrieved Rrs(412 nm) and Rrs(443 nm) had substantially improved compared with the original satellite products under the condition of AA. Moreover, the implementation of the OC-XGBRT contributes to a significant enhancement in the spatial coverage of utilizable Rrs(λ) values at blue bands. Our proposed OC-XGBRT AC algorithm has the potential to process ocean color satellite data under the condition of AA.

A novel method to calculate compliance and airway resistance in ventilated patients

BackgroundThe respiratory system’s static compliance (Crs) and airway resistance (Rrs) are measured during an end-inspiratory hold on volume-controlled ventilation (static method). A numerical algorithm is presented to calculate Crs and Rrs during volume-controlled ventilation on a breath-by-breath basis not requiring an end-inspiratory hold (dynamic method).MethodsThe dynamic method combines a numerical solution of the equation of motion of the respiratory system with frequency analysis of airway signals. The method was validated experimentally with a one-liter test lung using 300 mL and 400 mL tidal volumes. It also was validated clinically using airway signals sampled at 32.25 Hz stored in a historical database as 131.1-s-long epochs. There were 15 patients in the database having epochs on volume-controlled ventilation with breaths displaying end-inspiratory holds. This allowed for the reliable calculation of paired Crs and Rrs values using both static and dynamic methods. Epoch mean values for Crs and Rrs were assessed by both methods and compared in aggregate form and individually for each patient in the study with Pearson’s R2 and Bland–Altman analysis. Figures are shown as median[IQR].ResultsExperimental method differences in 880 simulated breaths were 0.3[0.2,0.4] mL·cmH2O−1 for Crs and 0[− 0.2,0.2] cmH2O·s· L−1 for Rrs. Clinical testing included 78,371 breaths found in 3174 epochs meeting criteria with 24[21,30] breaths per epoch. For the aggregate data, Pearson’s R2 were 0.99 and 0.94 for Crs and Rrs, respectively. Bias ± 95% limits of agreement (LOA) were 0.2 ± 1.6 mL·cmH2O−1 for Crs and − 0.2 ± 1.5 cmH2O·s· L−1 for Rrs. Bias ± LOA median values for individual patients were 0.6[− 0.2, 1.4] ± 0.9[0.8, 1.2] mL·cmH2O−1 for Crs and − 0.1[− 0.3, 0.2] ± 0.8[0.5, 1.2] cmH2O·s· L−1 for Rrs.DiscussionExperimental and clinical testing produced equivalent paired measurements of Crs and Rrs by the dynamic and static methods under the conditions tested.ConclusionsThese findings support to the possibility of using the dynamic method in continuously monitoring respiratory system mechanics in patients on ventilatory support with volume-controlled ventilation.

Estimation of euphotic zone depth in shallow inland water using inherent optical properties and multispectral remote sensing imagery

Euphotic zone depth (Zeu) is a key indicator of underwater light environment that regulates different biogeochemical and physical processes in aquatic ecosystem. Remote sensing approaches offer a unique opportunity to frequently monitor Zeu, although its estimation from diffuse attenuation coefficient (Kd) requires depth profiles of the down-welling irradiance (Ed). Measuring Ed values at different water depths (i.e., Ed profiling) in shallow waterbodies is a challenging task with the available profiling equipment. In this study, Kd values were estimated from measured absorption coefficient (a) data at multiple locations in the Chilika lagoon, which is a shallow waterbody located in the eastern coast of India and is a prestigious Ramsar site. Specifically, remote sensing reflectance (Rrs), a values (wavelength: 400–715 nm at 4 nm interval), Secchi disc depth (ZSD), suspended particulate matter (SPM) and chlorophyll-a (Chl-a) contents were measured during Oct. 07–08, 2018 and Feb. 02–08, 2019 in the Chilika lagoon. Atmospherically corrected Sentinel 2A/2B (S2A/S2B) MSI- Rrs data (level 2A) were then used to develop algorithms for mapping Zeu for the entire lagoon. Modeling results showed that Rrs values at band 4 (665 nm) or band 5 (704 nm) extracted from the S2A/S2B MSI data may be used to estimate Kd of photosynthetically active radiation (PAR) (observed range: 1.29 m−1 to 10.74 m−1) with the root-mean-squared error (RMSE) as low as 0.81 m−1. However, uncertainties associated with atmospheric correction of S2A/S2B MSI-single band Rrs values are high when compared with in situ Rrs. Thus, a multivariate modeling approach was adopted with Rrs- band ratio and band difference variables selected by stepwise, forward, and backward variable selection method with p < 0.05. Modeling results showed that the band differences between band 2 and band 4 and band 3 and band 5 may be used to estimate Kd(PAR) with improved mean absolute percentage difference (MAPD) while ensuring low uncertainty associated with atmospheric correction. Spatio-temporal dynamics from S2A/S2B MSI for 85 different dates during Jan. 2017 to June 2021 provided seasonal patterns for Zeu in the Chilika lagoon.

INFLUENCE OF URBAN LAND EXPROPRIATION ON CRIME RATE BASED ON AFFECTIVE DISORDER AND ANXIETY FACTORS

INFLUENCE OF URBAN LAND EXPROPRIATION ON CRIME RATE BASED ON AFFECTIVE DISORDER AND ANXIETY FACTORS

IMPROVED RRS AND TURBIDITY RETRIEVAL FROM OLI IMAGES IN COMPLEX INLAND WATERS: A CASE STUDY FOR LAGUNA DE BAY

Abstract. Spatiotemporal monitoring of water quality parameters such as turbidity in inland waters is desirable to better understand productivity and mitigate the negative impacts of pollution induced by increasing anthropogenic activities. However, precise retrieval of water quality parameters in complex turbid waters from the remote sensing reflectance Rrs, remains a challenging task due to the varying optical complexity of the water body. In this study, a modified version of the Atmospheric Correction for OLI-lite (ModACO) scheme for turbid inland waters, which implements a linear extrapolation of NIR aerosol reflectance in the visible bands instead of a nonlinear function, is presented. The performance of the proposed method and other existing algorithms such as the Atmospheric Correction for OLI-lite (ACOLITE), Management Unit of the Noth Seas Mathematical Models (MUMM) scheme, SeaDAS standard processing, and SWIRE were evaluated. The Rrs retrievals from these models were then used as input for turbidity estimation and mapping of Laguna de Bay. Results show lowest Rrs error in all five spectral bands (443, 482, 561, 655, and 865 nm) on ModACO-based retrievals. Relative to the ACOLITE and other atmospheric correction schemes, the proposed method reduced the Rrs retrieval errors in terms of RMSE and MAPE by more than 50%. Similarly, significant improvements in turbidity retrievals were achieved from ModACO-based Rrs values, wherein comparable accuracy was observed from red/green ratio and the single NIR band turbidity models. Turbidity maps of Laguna de Bay show elevated values from the mid of dry season, which may be associated with point source discharge and wind-induced resuspension of bottom sediments. The lake turbidity then drops by the end of dry season, which is linked to the absence of prevailing strong winds that may increase in-water mixing. Using the aforementioned method, accurate monitoring of turbidity can be done to determine and mitigate possible degradation on the water quality of Laguna de Bay and other productive turbid inland waters.

Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

The MultiSpectral Instrument (MSI) on-board Sentinel-2 provides satellite images at spatiotemporal resolutions suitable for chlorophyll a (Chla) retrieval from inland and coastal waters. Machine-learning (ML) algorithms including light gradient boosting machine (LGBM) were employed for Chl a retrieval from MSI. The study area encompasses 78 lakes and estuaries located across four major river watersheds in South Korea. Matchup data between MSI overpass and near-concurrent in situ Chl a measurements from December 2018 to April 2021 were included. The remote sensing reflectance (Rrs) values of six single spectral bands and four two-band ratios were used as the input features. Despite the difficulty in Chla estimation in optically complex waters, ML algorithms showed overall reasonable accuracy. Among the ML algorithms, LGBM exhibited the best performance (R2 = 0.75, bias = -0.15, slope = 0.73, RMSE = 15.15 mg·m-3, MAE = 9.49 mg·m-3) over a wide range of trophic states. Post-hoc interpretations of the best performing LGBM using Shapley additive explanations indicated that Rrs(704)/Rrs(665) was the most important feature, while Rrs(739)/Rrs(704) and Rrs(492)/Rrs(560) played auxiliary roles in Chl a retrieval through interaction with Rrs(704)/Rrs(665). Among-lake spatial variations of Chla were explained by percent forest and agricultural area within the buffer zone at multiple scales (buffer widths of 50 m and 500 m). The associations between the modeled Chla and buffer land cover types, that is, increase in Chla concentration with increase in percent forest and decrease in percent agricultural area, were consistent with the established ecological knowledge. Overall, the model interpretations and spatial variations in Chla within and among lakes confirmed the validity of LGBM for retrieving MSI-derived Chla from lakes and estuaries. Our study can serve as the reference for evaluating the validity of complex ML models for inland water remote sensing.

Satellite retrieval of benthic reflectance by combining lidar and passive high-resolution imagery: Case-I water

Under the background of global change, increasing attention has been paid to the changes of benthic habitats in shallow ocean ecosystems (e.g., seagrass beds and coral reefs). Optical satellite remote sensing via both active and passive methods plays an important role in monitoring the health of benthic habitats by retrieving benthic reflectance spectra, but it remains difficult to accurately retrieve benthic reflectance spectra from only active or passive remote sensing because of the coupling between water column scattering and benthic reflectance. Here, we developed a semi-analytical model to retrieve benthic reflectance spectra in Case-I waters by combining active lidar and passive high-resolution imagery. Based on two-stream radiative transfer theory, the analytical relationship among the remote sensing reflectance (Rrs) and water column reflectance (Rw), benthic reflectance (Rb), diffuse attenuation coefficient (Kd), and water depth was established. The lidar data at a certain wavelength were applied to derive the water depth and the chlorophyll concentration (chl) along the lidar track. Then, the values of Kd at different wavelengths were estimated from the derived chl. In addition, we established a look-up-table (LUT) for the relationship between Rw and chl and water depth using Hydrolight simulation, and the Rw values at different wavelengths were then estimated by the lidar-derived chl and water depth. Finally, Rb(λ) values at different wavelengths along the lidar track were retrieved from the Rrs(λ) values observed by passive high-resolution imagery and the values of Rw(λ), Kd(λ), and water depth derived by lidar observation. The accuracy of the model was verified by using the Hydrolight simulated datasets, and the high correlation coefficient (R) revealed promising model performance for different benthic habitats, e.g., R > 0.9 for typical clean shallow water (chl = 0.5 mg/m3, H < 4 m) for the wavelength range of 400–640 nm. The model was further applied to real satellite data from ICESat-2 lidar and passive high-resolution satellite imagery (multispectral imagery of Sentinel-2 and hyperspectral imagery of Zhuhai−1) at two different benthic habitat sites (seagrass beds in Xincun Bay and coral reefs in the Huaguang Reef) in the South China Sea, and the results revealed that the model could reproduce the benthic spectra in both magnitude and shape. Overall, the proposed model can reliably yield benthic reflectance spectra along the lidar track without any requirement on prior knowledge, which should be beneficial for further benthic habitat health monitoring.

A Survey on the Use of Spirometry in Small Animal Anaesthesia and Critical Care.

Simple SummarySpirometry can be used to monitor airway pressures, flows, and volumes. Its relevance in small animal anaesthesia is documented. The way the Veterinary Anaesthesia and Intensive Care communities use spirometry was not found in the literature. The objective was to document the use of spirometry and ventilation settings in small animal anaesthesia and intensive care through a descriptive, open, online, anonymous survey. The survey was advertised on social media and via email. Participation was voluntary. The google forms platform was used. It consisted of eight sections in English. Simple, descriptive analyses were applied. There were 128 respondents. Respondents used spirometry more when dogs were mechanically ventilated as opposed to spontaneously breathing. Over 3/4 of the respondents considered spirometry essential in “selected” (43%) or “most” cases (33%). Multiple devices and technologies were used. The majority of the respondents were not directly involved in or informed about the calibration of their equipment. More information on variables monitored and technical background on spirometers is required.The objective was to document the use of spirometry and ventilation settings in small animal anaesthesia and intensive care through a descriptive, open, online, anonymous survey. The survey was advertised on social media and via email. Participation was voluntary. The google forms platform was used. It consisted of eight sections in English: demographic information, use of spirometry in spontaneously ventilating/mechanically ventilated dogs, need for spirometry, equipment available and calibration status, ventilation modes, spirometry displays, compliance (CRS) and resistance (RRS) of the respiratory system. Simple descriptive analyses were applied. There were 128 respondents. Respondents used spirometry more in ventilated dogs than during spontaneous breathing. Over 3/4 of the respondents considered spirometry essential in “selected” (43%) or “most” cases (33%). Multiple devices and technologies were used. The majority of the respondents were not directly involved in or informed about the calibration of their equipment. Of all displays, pressure-volume loops were the most common. Values of CRS and RRS were specifically monitored in more than 50% of cases by 44% of the respondents only. A variety of ventilation modes was used. Intensivists tend to use smaller VT than anaesthetists. More information on reference intervals of CRS and RRS and technical background on spirometers is required

Radiometric Calibration Scheme for COCTS/HY-1C Based on Image Simulation From the Standard Remote-Sensing Reflectance

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997-2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

Interference of CDOM in remote sensing of suspended particulate matter (SPM) based on MODIS in the Persian Gulf and Oman Sea

The spatial and temporal variability of suspended particulate matter (SPM) in the Persian Gulf and Oman Sea coastal waters has remained challenging to understand among researchers. Here, for the first time in the region, we parametrized SPM concentration in the study area utilizing derived remote sensing reflectance (Rrs) values from Moderate-resolution Imaging Spectroradiometer (MODIS), using 555 and 667 nm wavelengths. Likewise, the findings showed that the developed optical model based on the optical ratio of Rrs (667)/Rrs (555) was sensitive to the concentration of Chromophoric dissolved organic matter (CDOM) in the seawater, within the visible wavelengths less than 600 nm. Comparing the new estimates of the SPM concentration with in situ measurements by Spearman's Rank correlation for validation revealed that the association between estimated and measured SPM concentration would be considered statistically significant (ρ up to 0.86, p < 0.05). This study increased the average accuracy of the estimates up to 73%.

Atmospheric correction over coastal waters with aerosol properties constrained by multi-pixel observations

We propose an innovative multi-pixel atmospheric correction approach (MPACA) to process high-spatial-resolution satellite measurements over coastal waters based on a revised POLYMER model. MPACA assumes the aerosol type to be uniform within a relatively small region, while the aerosol load and water properties are allowed to vary. Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA. The retrieved remote sensing reflectance (Rrs(λ)) by MPACA is validated with in situ matchups obtained from two sources: ship-based field campaigns and the AERONET-OC networks. It is found that, at each of OLI's four visible bands, MPACA provided accurate Rrs(λ) products over such coastal environments, with the Root Mean Square Difference (RMSD) and Mean Absolute Percentage Difference (MAPD) less than 0.0006 sr−1 and 16.2%, respectively. In contrast, the Rrs(λ) values retrieved with NASA's SeaDAS (v7.5), where each pixel was treated independently, showed RMSD and MAPD as ~0.0018 sr−1 and ~38.8%, respectively. Acolite-DSF, which assumed some spatial dependency, obtained MAPD almost two times that of SeaDAS for each visible band. Further, it appears that Acolite-EXP did not perform well for this evaluation dataset, where RMSD is ~0.0062 sr−1 and MAPD is ~228.2%. These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters, especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain.

Remote Sensing of Dispersed Oil Pollution in the Ocean-The Role of Chlorophyll Concentration.

In the contrary to surface oil slicks, dispersed oil pollution is not yet detected or monitored on regular basis. The possible range of changes of the local optical properties of seawater caused by the occurrence of dispersed oil, as well as the dependencies of changes on various physical and environmental factors, can be estimated using simulation techniques. Two models were combined to examine the influence of oceanic water type on the visibility of dispersed oil: the Monte Carlo radiative transfer model and the Lorenz–Mie model for spherical oil droplets suspended in seawater. Remote sensing reflectance, Rrs, was compared for natural ocean water models representing oligotrophic, mesotrophic and eutrophic environments (characterized by chlorophyll-a concentrations of 0.1, 1 and 10 mg/m3, respectively) and polluted by three different kinds of oils: biodiesel, lubricant oil and crude oil. We found out that dispersed oil usually increases Rrs values for all types of seawater, with the highest effect for the oligotrophic ocean. In the clearest studied waters, the absolute values of Rrs increased 2–6 times after simulated dispersed oil pollution, while Rrs band ratios routinely applied in bio-optical models decreased up to 80%. The color index, CI, was nearly double reduced by dispersed biodiesel BD and lubricant oil CL, but more than doubled by crude oil FL.

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.

Remotely estimating total suspended solids concentration in clear to extremely turbid waters using a novel semi-analytical method

Total suspended solids (TSS) concentration is an important biogeochemical parameter for water quality management and sediment-transport studies. In this study, we propose a novel semi-analytical method for estimating TSS in clear to extremely turbid waters from remote-sensing reflectance (Rrs). The proposed method includes three sub-algorithms used sequentially. First, the remotely sensed waters are classified into clear (Type I), moderately turbid (Type II), highly turbid (Type III), and extremely turbid (Type IV) water types by comparing the values of Rrs at 490, 560, 620, and 754 nm. Second, semi-analytical models specific to each water type are used to determine the particulate backscattering coefficients (bbp) at a corresponding single wavelength (i.e., 560 nm for Type I, 665 nm for Type II, 754 nm for Type III, and 865 nm for Type IV). Third, a specific relationship between TSS and bbp at the corresponding wavelength is used in each water type. Unlike other existing approaches, this method is strictly semi-analytical and its sub-algorithms were developed using synthetic datasets only. The performance of the proposed method was compared to that of three other state-of-the-art methods using simulated (N = 1000, TSS ranging from 0.01 to 1100 g/m3) and in situ measured (N = 3421, TSS ranging from 0.09 to 2627 g/m3) pairs of Rrs and TSS. Results showed a significant improvement with a Median Absolute Percentage Error (MAPE) of 16.0% versus 30.2–90.3% for simulated data and 39.7% versus 45.9–58.1% for in situ data, respectively. The new method was subsequently applied to 175 MEdium Resolution Imaging Spectrometer (MERIS) and 498 Ocean and Land Colour Instrument (OLCI) images acquired in the 2003–2020 timeframe to produce long-term TSS time-series for Lake Suwa and Lake Kasumigaura, Japan. Performance assessments using MERIS and OLCI matchups showed good agreements with in situ TSS measurements.

Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from In Situ and Sentinel-2 Remote Sensing Data

Monitoring suspended sediments through remote sensing data in black-water rivers is a challenge. Herein, remote sensing reflectance (Rrs) from in situ measurements and Sentinel-2 Multi-Spectral Instrument (MSI) images were used to estimate the suspended sediment concentration (SSC) in the largest black-water river of the Amazon basin. The Negro River exhibits extremely low Rrs values (&lt;0.005 sr−1 at visible and near-infrared bands) due to the elevated absorption of coloured dissolved organic matter (aCDOM at 440 nm &gt; 7 m−1) caused by the high amount of dissolved organic carbon (DOC &gt; 7 mg L−1) and low SSC (&lt;5 mg L−1). Interannual variability of Rrs is primarily controlled by the input of suspended sediments from the Branco River, which is a clear water river that governs the changes in the apparent optical properties of the Negro River, even at distances that are greater than 90 km from its mouth. Better results were obtained using the Sentinel-2 MSI Red band (Band 4 at 665 nm) in order to estimate the SSC, with an R2 value greater than 0.85 and an error less than 20% in the adjusted models. The magnitudes of water reflectance in the Sentinel-2 MSI Red band were consistent with in situ Rrs measurements, indicating the large spatial variability of the lower SSC values (0 to 15 mg L−1) in a complex anabranching reach of the Negro River. The in situ and satellite data analysed in this study indicates sedimentation processes in the lower Negro River near the Amazon River. The results suggest that the radiometric characteristics of sensors, like sentinel-2 MSI, are suitable for monitoring the suspended sediment concentration in large tropical black-water rivers.

Uncertainty assessment of unattended above-water radiometric data collection from research vessels with the Dynamic Above-water Radiance (L) and Irradiance (E) Collector (DALEC).

We used above- and below-water radiometry measurements collected during a research voyage in the eastern Indian Ocean to assess uncertainties in deriving the remote sensing reflectance, Rrs, from unattended above-water radiometric data collection with the In-Situ Marine Optics Pty. Ltd. (IMO) Dynamic Above-water Radiance (L) and Irradiance (E) Collector (DALEC). To achieve this, the Rrs values derived from using the latest version of this hyperspectral radiometer were compared to values obtained from two in-water profiling radiometer systems of rather general use in the ocean optics research community, i.e., the Biospherical Instruments Inc. Compact Optical Profiling System (C-OPS) and the Seabird HyperPro II. Our results show that unattended, carefully quality-controlled, DALEC measurements provide Rrs for wavelengths < 600 nm that match those derived from the in-water systems with no bias and a dispersion of about 8%, provided that the appropriate technique is used to quantify the contribution of sky light reflection to the measured signal. The dispersion is larger (25-50%) for red bands, which is expected for clear oligotrophic waters as encountered during the voyage, where ∼2 10-5 < Rrs < ∼2 10-4 sr-1. For comparison, the two in-water systems provided Rrs in agreement within 4% for wavelengths < 600 nm.