Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Wang H

PROVIDER: S-EPMC7957495 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Wang Hanghang H Wang Jie J Cui Yuhuan Y Yan Shijiang S

Sensors (Basel, Switzerland) 20210228 5

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 re ...[more]

PMID: 33670917

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Project description:Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

Project description:BackgroundEvidence from various epidemiological studies has shown an association between particulate matter 2.5 (PM2.5) and diabetes mellitus. A prospective study from the United States reported that exposure to PM2.5 alters endothelial function, and leads to insulin resistance and reduction in peripheral glucose uptake. There is a paucity of data on the relation between air pollution and diabetes in low- and middle-income countries.ObjectivesTo estimate the prevalence of type 2 diabetes among people living in areas with higher exposures of suspended PM2.5 compared to people living in areas with lower exposures in Chennai, Tamil Nadu, India.MethodsA cross-sectional study was carried out in two areas of Chennai city. The PM2.5 affecting vulnerable areas were stratified from a list of air quality monitoring stations in Tamil Nadu Pollution Control Board and Central Pollution Control Board. The highest and lowest areas of exposure were selected from the list. Households were randomly selected for the study. A total of 201 (67 male, 134 female) individuals from a high exposure area (HEA) and 209 (76 male 133 female) individuals from a low exposure area (LEA) were recruited for the study. Adults over 18 years of age were screened for random capillary blood glucose (RCBG) by glucometer (OneTouch Ultra).ResultsThe prevalence of diabetes (34.8% vs 19.6% p =0.001) was 77.5% higher among people living in areas of high particulate matter exposure compared to people living in less exposed areas. Multivariable logistic regression analysis showed that age, gender, residential area, and family history of diabetes were significantly associated with the prevalence of diabetes (p<0.05).ConclusionsThe present study indicates a link between high levels of exposure to PM2.5 and diabetes mellitus. Further prospective studies on populations exposed to elevated pollution are needed to establish whether this association has a causative link.Participant consentObtained.Ethics approvalThe study was approved by the Ethics Committee of the Prof. M Viswanathan Diabetes Research Centre, Chennai, India.Competing interestsThe authors declare no competing financial interests.

Dataset Information

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

Publications

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

Similar Datasets

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