scholarly journals Monitoring Water Transparency in Shallow and Eutrophic Lake Waters Based on GOCI Observations

2020 ◽  
Vol 12 (1) ◽  
pp. 163 ◽  
Author(s):  
Shuying Bai ◽  
Jixi Gao ◽  
Deyong Sun ◽  
Meirong Tian
Keyword(s):  
Remote Sensing ◽  
Lake Taihu ◽  
Eutrophic Lake ◽  
Temporal Variations ◽  
Water Transparency ◽  
Percentage Error ◽  
Study Region ◽  
Water Column Stability ◽  
Lake Waters

Water transparency represented by the Secchi disk depth (Zsd) plays an important role in understanding water ecology environment variations, especially for optically complex and shallow lake waters. In this study, using in situ measured remote sensing reflectance (Rrs), diffuse attenuation coefficient (Kd), and Zsd data collected in Lake Taihu (China), a regional algorithm for estimating Kd from Rrs was designed, and the semi-analytical model proposed by Lee et al. (2015) (hereafter called Lee_2015 model) was refined using a linear scaling correction for remote sensing of Zsd. The results showed that a good agreement between the derived Kd and in situ measured data (mean absolute percentage error (MAPE) = 26% for Kd(490); MAPE < 5% for Kd at 443, 555, and 660 nm). The in situ Rrs-derived Zsd results using the refined Lee_2015 model compared well with the in situ measured Zsd (R2 = 0.72 and MAPE = 36%), which was an obvious improvement over the Lee_2015 model in our study region. Subsequently, the refined Lee_2015 model was applied to the geostationary ocean color imager (GOCI) observations between 2012 and 2018 to yield the spatial and temporal variations of water transparency in the Lake Taihu waters. The long-term mean distribution of Zsd revealed that water transparency values in the northeastern Lake Taihu were generally higher than those in the southwest part. Monthly climatological Zsd patterns suggested that the Zsd distributions had large temporal variability, and distinct monthly patterns of Zsd existed in different subregions of Lake Taihu. The significant interannual variations of Zsd in Lake Taihu are probably affected by a combination of the water column stability mainly caused by wind, water temperature, human activity, and riverine discharge. The present study can provide a new approach for quantifying water visibility and serve for water-color remote sensing of optically complex and highly turbid waters.

scholarly journals Inherent Optical Properties in Lake Taihu Derived from VIIRS Satellite Observations

2019 ◽  
Vol 11 (12) ◽  
pp. 1426 ◽  
Author(s):  
Wei Shi ◽  
Menghua Wang ◽  
Yunlin Zhang
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Interannual Variability ◽  
Near Infrared ◽  
Lake Taihu ◽  
Temporal Variations ◽  
Satellite Observations ◽  
Seasonal And Interannual Variability ◽  
Remote Sensing Reflectance

Using in situ remote sensing reflectance and inherent optical property (IOP) measurements, a near-infrared (NIR)-based IOP algorithm is developed and tuned for Lake Taihu, in order to derive the particle backscattering coefficient bbp(λ), total absorption coefficient at(λ), dissolved and detrital absorption coefficient adg(λ), and phytoplankton absorption coefficient aph(λ), with satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). The IOP algorithm for Lake Taihu has a reasonably good accuracy. In fact, the determination coefficients between the retrieved and in situ IOPs are 0.772, 0.638, and 0.487 for at(λ), adg(λ), and aph(λ), respectively. The IOP products in Lake Taihu that have been derived from VIIRS-SNPP observations show significant spatial and temporal variations. Southern Lake Taihu features enhanced bbp(λ) and adg(λ), while northern Lake Taihu shows higher aph(λ). The seasonal and interannual variability of adg(λ) and bbp(λ) in Lake Taihu is quantified and characterized with the highest bbp(λ) and adg(λ) in the winter, and the lowest in the summer. In the winter, bbp(443) and adg(443) can reach over ~1.5 and ~5.0 m−1, respectively, while they are ~0.5–1.0 and ~2.0 m−1 in the summer. This study shows that in Lake Taihu adg(λ) is the most significant IOP, while aph(λ) is the least in terms of the IOP values and contributions to remote sensing reflectance. The highest bbp(λ) and adg(λ) occurred in the winter between 2017–2018, and the lowest bbp(λ) and adg(λ) occurred in the summer of 2014. In comparison, the seasonal and interannual variability of mean aph(λ) for Lake Taihu is less significant, even though enhanced seasonal and interannual variability can be found in some parts of Lake Taihu, such as in the northern Lake Taihu region.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

2019 ◽  
Vol 11 (17) ◽  
pp. 2001 ◽  
Author(s):  
Qing Zhu ◽  
Fang Shen ◽  
Pei Shang ◽  
Yanqun Pan ◽  
Mengyu Li
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Transfer Learning ◽  
Natural Waters ◽  
Hyperspectral Remote Sensing ◽  
Sensor Technology ◽  
Percentage Error ◽  
Phytoplankton Species ◽  
Phytoplankton Species Composition

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.

scholarly journals Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

2020 ◽  
Vol 12 (10) ◽  
pp. 1586
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
Rodrigo Sepúlveda ◽  
Sergio I. Martinez-Martinez ◽  
Markus Disse
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Learning Approaches ◽  
Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

Acclimated sediment microbial fuel cells from a eutrophic lake for the in situ denitrification process

RSC Advances ◽  
2016 ◽  
Vol 6 (83) ◽  
pp. 80079-80085 ◽  
Author(s):  
Yuan Wang ◽  
Jiangjun Hu ◽  
Longmian Wang ◽  
Dan Shan ◽  
Xu Wang ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Microbial Fuel Cells ◽  
Lake Taihu ◽  
Eutrophic Lake ◽  
The North ◽  
Denitrification Process

Sediment microbial fuel cells (SMFCs) with various external resistances were acclimated at the north of Lake Taihu for two months.

The responses of phytoplankton communities to elevated CO2 show seasonal variations in the highly eutrophic Lake Taihu

2016 ◽  
Vol 73 (5) ◽  
pp. 727-736 ◽  
Author(s):  
Xiaoli Shi ◽  
Xuhui Zhao ◽  
Min Zhang ◽  
Zhou Yang ◽  
Ping Xu ◽  
...  
Keyword(s):  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
Net Primary Production ◽  
Lake Taihu ◽  
High Growth ◽  
Eutrophic Lake ◽  
High Growth Rate ◽  
Four Seasons ◽  
Phytoplankton Communities

From April 2012 to January 2013 (over four seasons), in situ microcosm experiments were conducted in Lake Taihu, perturbed over a range of pCO2 scenarios (270, 380, and 750 μatm; 1 atm = 101.325 kPa). The influence of CO2 level on microcosms was greatest during the spring because of the high growth rate of phytoplankton. In this season, rising CO2 levels caused a pH reduction, and the maximum reduction was 0.6 units when CO2 level was enhanced from the present level to 750 μatm. The doubling of CO2 level could increase the net primary production (NPP) by 65% during spring when the concentrations of other nutrients were maintained. The rise of NPP could cause a decline of dissolved inorganic carbon (DIC) concentration, and CO2 enrichment might mitigate the extent of this decline. Meanwhile, higher CO2 may slow or prevent a loss of diversity of phytoplankton in microcosms in this season. During the other three seasons, Microcystis predominated, and the percentage of cyanobacteria did not alter with the change of CO2. We did not observe a significant increase in the abundance of any taxa with the rise of CO2 during the in situ microcosm experiments.

scholarly journals Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

2021 ◽  
Vol 13 (1) ◽  
pp. 143
Author(s):  
Ksenia Nazirova ◽  
Yana Alferyeva ◽  
Olga Lavrova ◽  
Yuri Shur ◽  
Dmitry Soloviev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Suspended Particulate Matter Concentration ◽  
Water Sampling ◽  
Study Region ◽  
Sentinel 2A ◽  
Remote Sensing Methods

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.

scholarly journals Phytoplankton biomass and species composition in relation to some physical and chemical characteristics of Lake Adale, Haramaya Woreda, Oromia Region, Ethiopia

2013 ◽  
Vol 13 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Zelalem Dessalegn Fayissa
Keyword(s):  
Species Composition ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Temporal Variations ◽  
East African ◽  
Physical Chemical ◽  
Chemical Conditions ◽  
Lake Waters ◽  
Physical And Chemical

Abstract Biomass and species composition of phytoplankton in relation to the physical and chemical conditions of the water in Lake Adale were studied from March, 2011 to September, 2011. All the physical, chemical and biological parameters measured varied temporally. The temperature of the lake was measured by digital oxygen meter and varied from 22°C to 26°C. The chemistry of the lake was basically similar to the dilute East African lake waters, with maximum pH values of 9.2 as measured in situ by a portable digital pH meter. The inorganic nutrients, which were of moderately high levels, varied temporally as a function of hydrological, hydrographic and biological conditions of the lake and were measured by a Hach kit (DR/2000 spectrophotometer) for nitrate, and colorimetrically with the ascorbic acid method for phosphate and molybdosilicate method for silica. The phytoplankton community, which was consistently dominated by cyanobacteria, exhibited low species diversity. Phytoplankton biomass measured as chlorophyll a varied from 23.35 to 55.18 mg m-3 .The concentration of chlorophyll a was calculated according to Talling and Driver (1963) using absorbance measurements made at 665 and 750 nm. The biomass of the lake indicates that the lake is a productive one. The factors responsible for the observed temporal variations in the physical, chemical and biological features of the lake are discussed.

Sign in / Sign up

Export Citation Format

Share Document