Correlation of turbidity with Indian Remote Sensing Satellite-1A data

Choubey, V. K.

doi:10.1080/02626669209492573

Cited by 25 publications

(6 citation statements)

References 7 publications

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“…Hence, many studies estimated the turbidity using the remote sensing approach of Landsat TM-bands (Chebud et al, 2012;Song et al, 2011;Zhao et al, 2011), AVHRR (Woodruff et al, 1999;Aguirre-Gomez, 2000), and MODIS bands . The and NIR (wavelength=772-898 nm) bands of Landsat (Bustamante et al, 2009;Brezonik, 2005), red band of LISS-I (wavelength=620-680 nm) (Choubey, 1992), and MODIS band of wavelength 645 nm are found to be strongly correlated with the turbidity concentration.…”

Section: Introductionmentioning

confidence: 94%

Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries

Swain

Sahoo

2017

Journal of Environmental Management

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For river water quality monitoring at 30m × 1-day spatio-temporal scales, a spatial and temporal adaptive reflectance fusion model (STARFM) is developed for estimating turbidity (T), total suspended solid (TSS), and six heavy metals (HV) of iron, zinc, copper, chromium, lead and cadmium, by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat (L) spectral bands. A combination of regression analysis and genetic algorithm (GA) techniques are applied to develop spectral relationships between T-L, TSS-T, and each HV-TSS. The STARFM algorithm and all the developed relationship models are evaluated satisfactorily by various performance evaluation measures to develop heavy metal pollution index-based vulnerability maps at 1-km resolution in the Brahmani River in eastern India. The Monte-Carlo simulation based analysis of the developed formulations reveals that the uncertainty in estimating Zn and Cd is the minimum (1.04%) and the maximum (5.05%), respectively. Hence, the remote sensing based approach developed herein can effectively be used in many world rivers for real-time monitoring of heavy metal pollution.

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Section: Introductionmentioning

confidence: 94%

Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries

Swain

Sahoo

2017

Journal of Environmental Management

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“…Along with chl‐ a , they have also recommended parameters such as Secchi disk depth or turbidity, two popularly measured water clarity indicators, as proxies for cyanoHABs (Chorus & Welker, 2021). Turbidity has been measured successfully through satellite remote sensing for decades (Choubey, 1992; Dogliotti et al., 2015; Moore, 1980; Nechad et al., 2009). While this study only focused on chl‐ a and CIs as proxies, future research could also investigate the utility of SRS‐based turbidity for recreational alerts, and potentially a combined alert system that leverages both SRS‐based chl‐ a and turbidity.…”

Section: Discussionmentioning

confidence: 99%

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir

Lopez Barreto,

Hestir,

Lee

et al. 2024

GeoHealth

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Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll‐a (chl‐a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl‐a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl‐a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel‐2 (S2) and Sentinel‐3 (S3) to map chl‐a and cyanobacteria, respectively, classified chl‐a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%–79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%–80% false positive rate and 0%–38% false negative rate, depending on algorithm. Using SRS‐based chl‐a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

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“…Remote sensing (RS) is a powerful tool that has been applied to regional water quality monitoring and assessment (Chopra et al 2001;Choubey 1992;Kloiber et al 2002;Nelson et al 2003;Olmanson et al 2002a, b). Studies have demonstrated reliable relationships between water quality parameters, such as total phosphorus, total nitrogen, dissolved oxygen, pH, salinity, Secchi depth, sodium and potassium, and radiance data from the satellites (Alparslan et al 2007;Dewidar and Khedr 2001;Fuller et al 2002a, b).…”

Section: Introductionmentioning

confidence: 99%

Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data

Mabwoga

Chawla

Thukral

2009

Environ Monit Assess

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This study aims at the classification and water quality assessment of Harike wetland (Ramsar site) in India using satellite images from the Indian Remote Sensing satellite, Resourcesat (IRS P6). The Harike wetland is a converging zone of two rivers, Beas and Sutlej. The satellite images of IRS Linear Imaging Self Scanner (LISS) IV multispectral sensor with three bands (green, red, and near infrared (NIR)) and a spatial resolution of 5.8 m were classified using supervised image classification techniques. Field points for image classification and water sampling were recorded using a Garmin eTrex Global Positioning System. The water quality parameters assessed were dissolved oxygen, conductivity, pH, turbidity, total and suspended solids (SS), chemical oxygen demand, and Secchi disk transparency (SDT). Correlations were established between turbidity and SS, SS and SDT, and total solids and turbidity. Using reflectance values from the green, red, and NIR bands, we then plotted the water quality parameters with the mean digital number values from the satellite imagery. The NIR band correlated significantly with the water quality parameters, whereas, using SDT values, it was observed that the green and the red reflectance bands were able to distinguish the waters from the two rivers, which have different water qualities.

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Correlation of turbidity with Indian Remote Sensing Satellite-1A data

Cited by 25 publications

References 7 publications

Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries

Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir

Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data

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