scholarly journals Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

2018 ◽  
Vol 10 (11) ◽  
pp. 1841 ◽  
Author(s):  
Quang Pham ◽  
Nguyen Ha ◽  
Nima Pahlevan ◽  
La Oanh ◽  
Thanh Nguyen ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Suspended Sediment Concentration ◽  
Atmospheric Correction ◽  
Sediment Concentration ◽  
Suspended Load ◽  
Red River ◽  
Landsat 8 ◽  
Band Ratio ◽  
Radiometric Data

Analyzing the trends in the spatial distribution of suspended sediment concentration (SSC) in riverine surface water enables better understanding of the hydromorphological properties of its watersheds and the associated processes. Thus, it is critical to identify an appropriate method to quantify spatio-temporal variability in SSC. This study aims to estimate SSC in a highly turbid river, i.e., the Red River in Northern Vietnam, using Landsat 8 (L8) images. To do so, in situ radiometric data together with SSC at 60 sites along the river were measured on two different dates during the dry and wet seasons. Analyses of the in situ data indicated strong correlations between SSC and the band-ratio of green and red channels, i.e., r-squared = 0.75 and a root mean square error of ~0.3 mg/L. Using a subsample of in situ radiometric data (n = 30) collected near-concurrently with one L8 image, four different atmospheric correction methods were evaluated. Although none of the methods provided reasonable water-leaving reflectance spectra (ρw), it was found that the band-ratio of the green-red ratio is less sensitive to uncertainties in the atmospheric correction for mapping SSC compared to individual bands. Therefore, due to its ease of access, standard L8 land surface reflectance products available via U.S. Geological Survey web portals were utilized. With the empirical relationship derived, we produced Landsat-derived SSC distribution maps for a few images collected in wet and dry seasons within the 2013–2017 period. Analyses of image products suggest that (a) the Thao River is the most significant source amongst the three major tributaries (Lo, Da and Thao rivers) providing suspended load to the Red River, and (b) the suspended load in the rainy season is nearly twice larger than that in the dry season, and it correlates highly with the runoff (correlation coefficient = 0.85). Although it is demonstrated that the atmospheric correction in tropical areas over these sediment-rich waters present major challenges in the retrievals of water-leaving reflectance spectra, the study signifies the utility of band-ratio techniques for quantifying SSC in highly turbid river waters. With Sentinel-2A/B data products combined with those of Landsat-8, it would be possible to capture temporal variability in major river systems in the near future.

scholarly journals Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

2021 ◽  
Vol 13 (10) ◽  
pp. 1927
Author(s):  
Fuqin Li ◽  
David Jupp ◽  
Thomas Schroeder ◽  
Stephen Sagar ◽  
Joshua Sixsmith ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Correction Method ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Correction Algorithm ◽  
Simple Method ◽  
North East ◽  
The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

scholarly journals ESTIMATION Of SUSPENDED SEDIMENT CONCENTRATION USING VNREDSAT – 1A MULTISPECTRAL DATA, A CASE STUDY IN RED RIVER, HANOI, VIETNAM

2018 ◽  
Vol 11 (3) ◽  
pp. 49-60 ◽  
Author(s):  
Le Hung Trinh ◽  
V. R. Zablotskii ◽  
Thi Giang Le ◽  
Thi Thu Hien Dinh ◽  
Thi Trinh Le ◽  
...  
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Surface Water Quality ◽  
Ground Truth ◽  
Monitoring And Evaluation ◽  
Sediment Concentration ◽  
Red River ◽  
Multispectral Data ◽  
Synoptic View

The traditional methods for measuring water quality variables are timeconsuming and do not give a synoptic view of a water body or, more significantly, a synoptic view of different water bodies across the landscape. However, remote sensing technology with advantages such as wide area coverage and short revisit interval have been effectively used for environmental pollution applications, such as for monitoring water quality parameters. Many studies around the world show that optical satellite imagery can be used effectively in evaluating suspended sediment concentration. This article presents results of monitoring suspended sediment concentration in Red River, Hanoi, Vietnam through ground truth measurements and VNREDSat-1A multispectral data. The results obtained in the study can be used to serve the management, monitoring and evaluation of surface water quality.

scholarly journals Developing of Total Suspended Sediment Model Using Landsat-8 Satellite Image and In-Situ Data at The Surabaya Coast, East Java, Indonesia

2017 ◽  
Vol 49 (1) ◽  
pp. 73 ◽  
Author(s):  
Teguh Hariyanto ◽  
Trismono C. Krisna ◽  
Khomsin Khomsin ◽  
Cherie Bhekti Pribadi ◽  
Nadjadji Anwar
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Coastal Water ◽  
Satellite Image ◽  
Coastal Region ◽  
Sediment Concentration ◽  
Landsat 8 ◽  
Total Suspended Sediment ◽  
Coastal Water Quality

The decrease of coastal-water quality in the Surabaya coastal region can be recognized from the conceentration of Total Suspended Sediment(TSS ) . As a result we need a system for monitoring sediment concentration in the coastal region of Surabaya which regularly measures TSS. The principle to model and monitor TSSconcentration using remote sensing methods is by the integration of Landsat-8OLI satellites image processing using some ofTSS-models then those are analyzed for looking its suitability with TSS value direcly measured in the field ( in-situ measurement). The TSS value modeled from all algorithms validated usingcorrelation analysis and linear regression . The result shows that TSS model with the highest correlation value is TSS algorithm by Budiman (2004)with r value 0.991. Hence this algorithm can be used to investigate TSS-distribution which represent the coastal water quality of Surabaya with TSS value between 75 mg/L to 125 mg/L.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan

2018 ◽  
Vol 10 (9) ◽  
pp. 1335 ◽  
Author(s):  
Meng Meng Yang ◽  
Joji Ishizaka ◽  
Joaquim I. Goes ◽  
Helga do R. Gomes ◽  
Elígio de Raús Maúre ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Red Tide ◽  
Atmospheric Correction ◽  
Ariake Bay ◽  
Band Ratio ◽  
Simple Method ◽  
Chl A ◽  
Switching Algorithm ◽  
Economic Significance

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

scholarly journals An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

2020 ◽  
Vol 143 ◽  
pp. 02003
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Kaiyuan Bai ◽  
Xiaojuan Li
Keyword(s):  
Chlorophyll A ◽  
Environmental Issue ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ratio Model ◽  
Landsat 8 Oli ◽  
Band Ratio ◽  
Chl A ◽  
Quality Indexes

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

scholarly journals Calibrations of Suspended Sediment Concentrations in High-Turbidity Waters Using Different In Situ Optical Instruments

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3296
Author(s):  
Yunwei Wang ◽  
Yun Peng ◽  
Zhiyun Du ◽  
Hangjie Lin ◽  
Qian Yu
Keyword(s):  
Suspended Sediment ◽  
Inflection Point ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Fluid Mud ◽  
Optical Instruments ◽  
Suspended Sediment Concentrations ◽  
High Turbidity ◽  
The Right

In environments of high suspended sediment concentration (SSC > 1 kg/m3), efficient measurements of SSC through accurate calibration relationships between turbidity and SSC are necessary for studies on marine sediment dynamics. Here, we investigated the performance of three types of optical instrument (OBS-3A, AQUAlogger 310TY, and RBRsolo3Tu with Seapoint sensor) in observations carried out at the middle of the Jiangsu coast, China. These instruments were calibrated in the lab using the water and suspended sediment samples collected from the observation site. It was found that both the calibration curves of OBS-3A and RBRsolo3Tu have an inflection point (at SSC of ca. 15 kg/m3 for OBS-3A and ca. 2 kg/m3 for RBRsolo3Tu), on either side of which turbidity increases (the left side) or decreases (the right side) with the increasing SSC. Only under SSCs smaller than the inflection point can OBS-3A and RBRsolo3Tu be applied to continuous SSC measurements at a fixed point. However, the turbidity output of AQUAlogger 310TY has always a positive correlation with SSC, which applies for SSC up to 40 kg/m3; thus, three fluid-mud events are quantified during this observation. AQUAlogger 310TY has important prospects for field applications in high-SSC environments.

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