Noninvasive In Vivo Estimation of Blood-Glucose Concentration by Monte Carlo Simulation.

Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte Carlo photon simulation-based model to estimate blood-glucose concentration via photoplethysmography (PPG) on the fingertip. A heterogeneous finger model was exposed to light at 660 nm and 940 nm in the reflectance mode of PPG via Monte Carlo photon propagation. The bio-optical properties of the finger model were also deduced to design the photon simulation model for the finger layers. The intensities of the detected photons after simulation with the model were used to estimate the blood-glucose concentrations using a supervised machine-learning model, XGBoost. The XGBoost model was trained with synthetic data obtained from the Monte Carlo simulations and tested with both synthetic and real data (n = 35). For testing with synthetic data, the Pearson correlation coefficient (Pearson's r) of the model was found to be 0.91, and the coefficient of determination (R2) was found to be 0.83. On the other hand, for tests with real data, the Pearson's r of the model was 0.85, and R2 was 0.68. Error grid analysis and Bland-Altman analysis were also performed to confirm the accuracy. The results presented herein provide the necessary steps for noninvasive in vivo blood-glucose concentration estimation.

Quantifying the Impact of Linear Regression Model in Deriving Bio-Optical Relationships: The Implications on Ocean Carbon Estimations.

Linear regression is widely used in applied sciences and, in particular, in satellite optical oceanography, to relate dependent to independent variables. It is often adopted to establish empirical algorithms based on a finite set of measurements, which are later applied to observations on a larger scale from platforms such as autonomous profiling floats equipped with optical instruments (e.g., Biogeochemical Argo floats; BGC-Argo floats) and satellite ocean colour sensors (e.g., SeaWiFS, VIIRS, OLCI). However, different methods can be applied to a given pair of variables to determine the coefficients of the linear equation fitting the data, which are therefore not unique. In this work, we quantify the impact of the choice of "regression method" (i.e., either type-I or type-II) to derive bio-optical relationships, both from theoretical perspectives and by using specific examples. We have applied usual regression methods to an in situ data set of particulate organic carbon (POC), total chlorophyll-a (TChla), optical particulate backscattering coefficient (bbp), and 19 years of monthly TChla and bbp ocean colour data. Results of the regression analysis have been used to calculate phytoplankton carbon biomass (Cphyto) and POC from: i) BGC-Argo float observations; ii) oceanographic cruises, and iii) satellite data. These applications enable highlighting the differences in Cphyto and POC estimates relative to the choice of the method. An analysis of the statistical properties of the dataset and a detailed description of the hypothesis of the work drive the selection of the linear regression method.

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery.

Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr-1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56-76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data.

Water optical properties over or around coral reefs in the coastal region: implications for coral reef remote sensing.

Coral reefs inhabit clear oceanic-type waters to turbid coastal regimes. For shallow coastal water environments, the coral reefs from remote sensing will be substantially influenced by the complexity and heterogeneity of the optical properties of water. Through a set of bio-optical and chemical measurements in July 2019 around Xidao Island, Sanya, China, we explored spatial variations of water components' concentrations (including suspended solids, phytoplankton, and dissolved organic carbon) and the optical absorptions (i.e., particulate pigments, detritus, and colored dissolved organic matter) in waters over or around coral reefs; further analysis of their influences upon the remote sensing reflectance (Rrs(λ)) of water was performed. It was observed that the bio-optical and chemical properties of the waters over or around coral reefs were spatially and vertically heterogeneous for different sampling sites. As expected, the suspended solids dominated the optical properties of coral reefs waters in areas of Xidao Island, which evidently influenced the Rrs(λ), especially for the surface waters (Pearson r > 0.60, p < 0.01). In addition, the dissolved organic carbon concentrations exhibited significant relations to the Rrs(λ) both in surface and bottom water layers, whereas the colored dissolved organic matter showed a weak negative correlation with the Rrs(λ). These findings will support the mapping and monitoring benthic habitats with remote sensing imagery in coastal regions, especially when a removal of the influence by particulate sediments was available.

Phytoplankton dynamics based on satellite inherent optical properties and oceanographic conditions in a patagonian gulf frontal system in relation to the adjacent continental shelf waters.

The dynamics of phytoplankton across a seasonal frontal system formed in San José Gulf (SJG, Patagonia Argentina) and in neighbouring shelf waters was assessed based on bio-optical satellite data (2003-2018) and spring and summer in situ samplings. Bio-optical properties of the water masses on the eastern (ED) and western (WD) domains of the seasonal frontal system of SJG showed clear differences: the year-round-vertically-mixed waters from the WD, strongly connected with the adjacent shelf waters, evidenced a brief and strong single phytoplankton bloom, while those from the ED, showing lower exchange with shelf waters and a strong vertical stratification during the warm season, displayed an earlier and long-lasting spring phytoplankton bloom, followed by a late-summer and autumn bloom, both associated with the development and erosion of the seasonal thermocline. Waters from the entire system are optically influenced by the absorption of coloured dissolved organic matter and detritus (cdom + detritus), suggesting a strong sediment load contribution from the continent and the seabed. To remark, a strong correlation between satellite chlorophyll-a (Chla-sat) and absorption by phytoplankton (aphy443) in the outer shelf waters differs from the weak correlation of those variables in the gulf's water masses, whose optical parameters are more complex. In situ Chla records may indicate wind-driven upwelling and downwelling areas in the northern and southern coasts of the ED. Dissolved nitrogen was identified as the limiting macronutrient for phytoplankton growth in the ED during summer. This work contributes relevant ecological information that may support management actions on the SJG shellfish artisanal fishery.

Australian aquatic bio-optical dataset with applications for satellite calibration, algorithm development and validation.

The authors present bio-optical data spanning 316 sets of observations made at 34 inland waterbodies in Australia. The data was collected over the period 2013-2021 and comprise radiometric measurements of remote sensing reflectance (Rrs), diffuse attenuation extinction coefficient (Kd); optical backscattering; absorption of coloured dissolved organic matter (aCDOM), phytoplankton (aph) and non-algal particles (aNAP); HPLC analysis of algal pigments including chlorophyll-a (CHL-a); organic and inorganic total suspended solids (TSS); and total and dissolved organic carbon concentration. Data collection has been timed to coincide with either Landsat 8 or Sentinel-2 overpasses. The dataset covers a diverse range of optical water types and is suitable for algorithm development, satellite calibration and validation as well as machine learning applications.

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges.

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

An assessment of natural and manmade hazard effects on the underwater light field of the Doce River continental shelf.

Natural and manmade disasters have occurred more frequently due mainly to climate change and human pressure for productivity. One of the world's vastest disasters in the mining industry occurred due to the collapse of the Fundao dam, Brazil, which discharged about 43 million m3 of iron tailings at the Doce River basin. Extreme natural events also affect this region and provoke substantial mass movement and substantial floods in the Doce River basin, and flow of anomalous volumes of sediments in its mouth. The extent of tailings and the sediment flow in these events were approached in previous research. However, their effects on the penetration of sunlight into the water column in the coastal region are unknown. Here, we evaluate the effects of an extreme natural event and a manmade disaster on the light regime of the water column at the Doce River mouth, using remote sensing data. In both events, the spatial and temporal distribution of suspended particulate matter (SPM), diffuse light attenuation coefficient (Kd490) and Euphotic Zone (Zeu) were analyzed. During the natural event, light penetration into the water column was strongly attenuated (Kd490: 0.35 m-1; SPM: 8.81 g/m3) but re-established after 1 month due to sediment deposition. In the case of the dam collapse, the attenuation of light penetration was also intense along the event (Kd490: 0.34 m-1; SPM: 13.87 g/m3); however, sediment deposition occurred sooner. Re-suspension of sediments due to wind action was recurrent after 8 months of the dam collapse, in contrast to the natural event, where re-suspension was not perceptible in satellite images. The results indicate that both events have considerable effects on the penetration of light in the water column, but with different intensity and length.

In situ spectral response of the Arabian Gulf and Sea of Oman coastal waters to bio-optical properties.

Mapping of Chlorophyll-a (Chl-a) over the coastal waters of the Arabian Gulf and the Sea of Oman using the satellite-based observations, such as MODIS (Moderate Resolution Imaging Spectro-radiometer), has shown inferior performance (Chl-a overestimation) than that of deep waters. Studies in the region have shown that this poor performance is due to three reasons: (i) water turbidity (sediments re-suspension), and the presence of colored dissolved organic matter (CDOM), (ii) bottom reflectance and (iii) incapability of the existing atmospheric correction models to reduce the effect of the aerosols from the water leaving radiance. Therefore, this work focuses on investigating the sensitivity of the in situ spectral signatures of these coastal waters to the algal (chlorophyll: Chl-a), non-algal (sediments and CDOM) and the bottom reflectance properties, in absence of contributions from the atmosphere. Consequently, the collected in situ spectral signatures will improve our understanding of Arabian Gulf and Sea of Oman water properties. For this purpose, comprehensive field measurements were carried out between 2013 and 2016, over Abu-Dhabi (Arabian Gulf) and Fujairah (Sea of Oman) where unique water quality data were collected. Based on the in situ water spectral analysis, the bottom reflectance (water depth<20m) are found to degrade the performance of the conventional ocean color algorithms more than the sediment-laden waters where these waters increase the Rrsat the blue and red ranges. The increasing presence of CDOM markedly decreases the Rrsin the blue range, which is conflicting with the effect of Chl-a. Given the inadequate performance of the widely used ocean-color algorithms (OC3: ocean color 3, OC2: ocean color 2) in retrieving Chl-a in these very shallow coastal waters, therefore, a new algorithm is proposed here based on a 3-bands ratio approach using [Rrs (656) -1-Rrs (506) -1]×Rrs (661). The selected optimum bands (656nm, 506nm, and 661nm) from this approach can be used to retrieve the Chl-a more accurately in these coastal Case II (turbid) waters which are close to the bands of the current missions such as Sentinel-3 OLCI (Ocean and Land Colour Instrument), MODIS, VIIRS (Visible Infrared Imaging Radiometer Suite) and LandSat 8. However, more uniformly distributed data over the Arabian Gulf is required to have a highly accurate regional model for Chl-a retrieval.