Water Quality Index (WQI) Estimation using Raw Landsat-8 OLI Data, NDVI and Surface Temperature for Water Quality Index Assessment

Water Quality Index (WQI) Estimation using Raw Landsat-8 OLI Data, NDVI and Surface Temperature for Water Quality Index Assessment

Irshad. S. Shaikh

Research Scholar, Microwave and Imaging Spectroscopy Lab, Milya College, Beed.

Shafiyoddin B. Sayyed

HOD, Computer Science, Microwave and Imaging Spectroscopy Lab, Milya College, Beed.

Anjum Z. Shaikh

Assistant Professor, Dept. of CS, IT and Animation, Deogiri College Chh. Sambhajinagar.

Sharad. G. Gaikwad

Groundwater Surveys and Development Agency, Government of Maharashtra, India.

Irshad I. Kureshi

Geology Department, Deogiri College Chh. Sambhajinagar, India.

Imran D. Sayad

S.E.S. College, Sakherkheda taluka, Sindkhed Raja, Dist.Buldhana, India.

Kailash R. Aher

Groundwater Surveys and Development Agency, Government of Maharashtra, India.

ABSTRACT – Landsat 8 was launched in 2013 by the National Aeronautics and Space Administration (NASA). On board the Landsat 8 is the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Data for visible, panchromatic band, short-wave infrared spectral bands are collected by the OLI, while TIRS collect images in the thermal region. As data for Landsat 8 is available to be used for public, researchers have utilised the data for numerous applications. However, to the best of our knowledge, there is yet no research paper on the various applications of Landsat 8 data. Hence, this paper presented an innovative survey on Landsat 8 data in the application of water quality index, agriculture and forestry, land use and mapping, geology, hydrology, coastal resources and environmental detection of water quality parameters using imaging is based on the presence of pollutants in water and the absorption of the incoming solar radiation.

In this study, by considering data from conventional water quality testing, a monitoring. As Landsat 8 data is predicted to be available for many years to come, this paper provides insight for researchers to utilize the data better for their research.

The conventional approach of water quality assessment via sampling followed by laboratory measurement methods comprises the analysis of different properties, such as chemical, physical. The main idea behind this was considered. Based on the analysis, an approach was made to find the relation between the Water Quality Index and spatial parameters. Further, a model was established to estimate WQI from spatial data.

An attempt is made to identify the association between the laboratory results and the indices and bands values obtained

from spatial data, to determine their applicability in water quality estimation and prediction. The study makes use of two types of data, visual, spatial and non-spatial data. The spatial data used was Landsat-8 OLI, from which the water index was calculated.

Keywords: Water pollutants; spatial estimation; Regression analysis; Water quality index; anthropogenic waste.

INTRODUCTION

Drinking-water supply agencies are usually required to verify that the quality of water supplied to the consumers meets specific numerical standards. Yet, by the time water quality analysis is completed and results indicate that the water is not safe to drink, thousands of people may have consumed that water, putting them at risk. Moreover, even with frequent monitoring, the vast majority of water distributed to consumers will never be tested. Therefore, reliance on only end-of-pipe monitoring is inadequate to address the prIn

above context, it is relevant to quote some sentences from Guidelines for Drinking-water Quality, Fourth Edition, published by the World Health Organization (WHO) which states that The most effective means of consistently ensuring the safety of a drinking-water supply is through the use of comprehensive risk assessment and risk management approach that encompasses all steps in water supply from catchment to consumer. in 2011. Such approaches are termed a Water Safety Plan (WSP). The purpose of a Water

Safety Plan (WSP) is to consistently ensure the safety and acceptability of a drinking-water supply. This is done by eliminating/minimising the potential risk of contamination in raw water sources, water treatment plants, catchment, distribution network, storage, collection and handling. WSP is an essential tool in providing safe water to the people for all types of water supply systems, i.e. large piped drinking water supplies, small community supplies, stand-alone household systems such as wells and also in rain harvesting

systems. Water safety plan aims to minimise risks of contamination via sanitary surveillance and can be conjoined with water quality monitoring for ensuring safe water to the communities. This means that the water quality data is useful along with Water Safety Plans (WSP) for preventive and curative management measures.

A conjoined approach of using WSP with Water Quality Monitoring is an important tool that extends its application beyond the creation of a water quality database.

This can be achieved by shifting the emphasis of drinking- water quality management to a holistic risk-based approach. Such an approach is called WSPs. Widespread implementation of WSPs can contribute to reducing the portion of the disease burden attributed to poor quality of drinking water.

Water quality monitoring, resulting in identifying sources of contamination and implementation of corrective actions and subsequent verification, comprises components of the water safety plan. In this case, the utility of water quality monitoring is extended to the provision of safe water to the community. A framework of water safety in the rural context is prepared and presented below.

About Marathwada Region

Marathwada region comprises of eight districts. The regional headquarter is at Aurangabad. The region lies between 17037 to 20039 north latitude and 74033 to 78022 east longitude. The geographical area is 6413 Sq. Km. The region comprises of 76 tahsils (Fig.1)The region is situated on plateau having plain terrain with undulations. The region is located in the centre of the Maharashtra state. It is drained by the main river Godavari popularly known as Deccan Ganges and comprises many large and small projects. The region also experiences the extension of Ajantha and Balaghat hill ranges. The region is bounded by Amarawati region in the north, Nashik region in the west, Pune region in the south and

it is bounded by Karnataka and Andhra state in the south and south east. The region receives very erratic rainfall. There is variation in quantity and intensity of short rainfall and sometimes there is dry spell between the two successive rains.

Geology and Hydrogeology

The region is peculiarly occupied by the Deccan Traps. However, there are exposures of granite and Vindhyans in the Nandeddistrict.The Deccan traps are of the age upper Cretaceous to lower Eocene. The thickness of each lava flow varies from few meters to 40-45 meters. The basalts are of vesicular, zeolitic, jointed, columnar weathered in nature. At places the alluvial patches are found near the bank of the

river. The groundwater mainly occurs in voids the joints, fractures and the weathered portion of the basaltic lava flows(Fig.2). Generally, the water levels are up to eight to nine meters during the summer. According to the sixth groundwater assessment there are 348535 irrigation wells in the region having 323038.93-ham draft.

Fig.1 Map showing district and taluka of Aurangabad region

Fig.2. Geological map of Aurangabad region.

Historical

The region is traceabl as far back as the time of Ramayana and Mahabharata, particularly in Latur, Jalna, etc. The empire of Ashoka ruled the region for a long time. After that, the reign of Satwahana and Rashtrakut came into existence. The region tells its own history through various caves, temples, monuments and forts etc. During the reign of Nizamuddin, the Marathwada region was part of the Hyderabad state, and a non-violent movement led by Swami Ramanand Tirth resulted in the formation of the Marathwada region as part of the state of Maharashtra, and the region celebrates the day on 17th September of every year.

Water quality index is crucial for improving water quality and clean water supply to achieve sustainable development goals directly related to water, agriculture, biodiversity, health, and climate actions. Water quality index examines the vital relationship between water supply and demand, focusing on the critical role that water quality (WQ) plays in sustainable development and integrated environmental management. This study evaluates the methodology and limitations of several studies by doing a thorough examination of regional and global WQ indices and synthesising the results. Water Quality Indices (WQIs) have been used to measure WQ since the 1960s, offering a mechanism for changes in WQ at specific needs and environmental challenges. It aims to provide a detailed analysis of various WQIs utilised across the globe. The WQIs stated WQ.

Cultural

The region has a great cultural heritage. Siddheshwar yatra in the Latur district, Marathi Sahitya Sammelan in the district Nanded and the Ellora Festival in the District Chh.SambhajiNagar (Aurangabad) are the main highlight of the cultural activities in the region.

Hot Spots

The earthquake recorded on 13th September 1993 at Killari, Dist. Latur rock the country and it had a major impact human life. The earthquake recorded was of the 7 to 8 Richter scale. According the sixth ground water assessment there are 6 over developed, 2 critical, and 32 semi critical watershed in the region. These watersheds mainly fall in the Latur, Osmanabad, Jalna, Aurangabad Districts and faces acute water scarcity period. The region also experiences floods in the districts like Aurangabad, Jalna andParbhani due to the water release through the Jayakwadi dam.

Groundwater quality

Ground water is an important part of environment & integral part of hydrological cycle & considered a dependable source of contaminated water. Ground water is widely used for drinking as well as for irrigation purpose in a rural area & urban area. Pollution of air, water & land has an effect on pollution and contamination of ground water keeping in view

the importance of water quality project. Water quality monitoring stations were fixed considering the following objectives.

Objectives

1. To study chemical quality water and to compare it with the standards prescribed by ISO of Indian standards this will be give information about the water quality.
2. To study / monitor groundwater quality periodically (pre and post-monsoon seasons along with the statistical information.
3. Details of findings of pollutants like arsenic, fluoride, nitrate, iron, salinity in ground water.

Materials and Methods Groundwater Sample Collection

In pre-monsoon season (March 2019 to September 2019)total 13498 watersamples and in post-monsoon season (October 2019 to February 2020) total 20559 water samples were collected from Dug well, bore well, PWS well, Handpump, Tanker (deployed during Scarcity period), and Solar pump etc.Each sample was collected by 1000 ml acid- washedpolyethylene HDPE bottle. The bottle was completelyfilled with water taking care that no air bubble was trapped within thewater sample. Then to prevent evaporation, double plastic caps bottleswere sealed. Precaution was also taken to avoid sample agitation duringtransfer to the laboratory. The samples were well stored prior to analysis in the laboratory.

Laboratory Measurements

Samples were analysed in the laboratory for the physico- chemicalattributes like Temperature, Colour, Odour, Taste, Turbidity, pH, electrical conductivity (EC), total hardness (TH),total dissolved solids (TDS) and major cations like calcium (Ca),and anions like bicarbonate (HCO3), carbonate (CO3),chloride (Cl), nitrate (NO3),sulphate (SO4), Fluoride

(F) and Iron (Fe), as well as microbiological parameter such as Total Coliforms, Faecal Coliforms/ E.coli were determined in the laboratory using the standard methods given by the American Public Health Association (APHA, 2012). Standard Methods and Chemicals Required for each parameter are given in Table 1.

In brief, water samples were collected from the entire Marathwada region area. pH, electrical conductivity (EC), and total dissolved solids (TDS) were measured using the pH meter, conductivity meter and TDS meter, respectively. Turbidity measured by Nephelometric method, Total hardness (TH) as CaCO3, Calcium (Ca++), were analysed titrimetric ally, using standard EDTA, carbonate (CO3-) and bicarbonate (HCO3-) were estimated by titrating with H2SO4, nitrate (NO3-), sulphate (SO4-), and fluoride (F-) were

Drinking Water Quality

The quality of groundwater varies from place to place,season to season, with the depth of water table and is primarilygoverned by the extent and composition of dissolved solids.The quality of groundwater is the main factor in determining its suitability for drinking, domesticpurposes. The physicochemical parameters of groundwater (Table 3) were compared with the BIS (2012) standards (Table 2). In pre-monsoon season (March 2019 to September 2019) total 13498 water samples and in post-monsoon season (October 2019 to February 2020) total 20559 water samples were collected analyses. The Table 3 gives the statistical information of different chemical parameters in eight (8) districts of Aurangabad region.

pH

In pre-monsoon seasons the pH ranges from 6.0 to 11.3, with mean 11.3 maximum value found is 11.3 from taluka Khultabad of Aurangabad district and minimum value found is 6.0 from taluka Gangakhed of Parbhani district. While in post-monsoon seasons the Average value for pH is 7.6, Maximum value found is 9.6 from Taluka Ranapur of Latur district and Minimum value found is 6.1 from Palam Taluka of Parbhani District.The spatial distribution of pH concentration during pre- and post-monsoon season ingroundwater of study area is illustrated (Fig.3 and 3a) respectively.

Turbidity

In pre-monsoon seasons Turbidity values ranges from 0.01 to 11.02NTU with mean 1.0NTU, maximum value found is 11.0 from taluka Basmat of Hingoli district and minimum value

found is 0.1 from taluka Kej of Beed district and Nanded,Ardhapur,Mudkhedtalukaof Nanded district,while in post-monsoon seasons the Turbidity varies from 0.1 to 9.50 with mean 0.9 NTU, maximum value found is 9.50 from taluka Ambad of Jalna district and minimum valuefoundis 0.1 from Ahamdpur,Chakur,Renapur taluka of Latur district,Ambad,Badnapur,Ghansawgitaluka of Jalna district,Georaitaluka of Beed district, and Dharmabad, Umari taluka of Nanded district. The spatial distribution of Turbidity in groundwater of the study area is demonstrated in fig.4 and 4a.

Electrical conductivity (EC)

In pre-monsoon seasons Electrical conductivity varies from

18 to 8580 µ/cm with average value 1217 µ/cm. The maximum value found is 8580 from taluka Manvat of Parbhani district and minimum value found is 18 from taluka Loha of Nanded district.While in post-monsoon seasons,the average value for EC is 1217µ/cm, maximum value found is 9600 from taluka Selu of Parbhani district and minimum value found is 27 from taluka Ambajogai of Beed district.Spatial distribution of EC in the study area shows its higher concentration in the central parts of Marathwada region (Fig.5 and 5a) in both seasons.

Total dissolved solids (TDS)

In pre-monsoon seasons TDS values ranges from 12 to 5590 mg/L with mean 875 mg/L. The maximum value found is 5590 mg/L from Manvat taluka of Parbhanidistrict and minimum value found is 12 mg/L from Lohataluka of Nanded district.While in post-monsoon seasons. The average value for TDS is 815 mg/L. The Maximum value found is 5900

mg/l from Selu taluka of Parbhanidistrict and the minimum value found is 18 mg/L from Ambajogaitalukaof Beed district.The spatial distribution ofTDSin groundwater of the study area is demonstrated in fig.6 and 6a.

Total hardness (TH)

In pre-monsoon seasons total hardness varies from 2 to 2256 with an average value 317 mg/L. The maximum value for TH found is 2256 mg/L from taluka Manvat of Parbhani district and the minimum value found is 2 mg/L from taluka Loha of Nanded district. While in post-monsoon season. The Maximum value for TH found is 2376 mg/L from taluka Selu of Parbhanidistrict and the minimum value found is 2 mg/L from Taluka Kinwat of Nanded district with Average value 343 mg/L.Samples with higherTHvalues aredistributed in the part of Parbhani. Nanded and Aurangabad district (Fig.7 and 7a).

Calcium (Ca)

In pre-monsoon seasons calcium values ranges from

0.1 to 692 mg/L with mean 85 mg/L. The maximum value for Calcium found is 692 mg/L from taluka Selu of Parbhani district and the minimum value found is 0.1 mg/L from taluka Bhokar of Nanded district.While in post-monsoon seasons the maximum value for Calcium found is 744 mg/l from taluka Selu of Parbhanidistrict and the minimum value found is 0.1 mg/l from taluka Loha of Nanded district with average value 86 mg/L.The spatial distribution of Ca in the groundwater isgiven in Fig. 8 and 8a.

Iron (Fe)

In pre-monsoon seasonsthe analysis result revealed that the iron found in range of 2.82 to 0.1 mg/l with average value

0.19 mg/l. The maximum value (2.82 mg/l) is found in Osamanabad taluka of Osamanabad district and the minimum value (0.1 mg/l) is found from Auragabad, Khultabad, Phulambri, Sillod talukas of Aurangabad district, Ambejogai, Beed, Dharur, Georai, Kaij, Majalgaon, Parli, Patoda etc talukas of Beed district , Badnapur, Mantha and Partur talukas of Jalna district Nanded , Mudkhed, Loha, Umri, Manvat, Kandhar, Ardhapur, Hadgaon, Himaytnagar, Kinwat,Mahoor taluka of Nanded district,Omarga, Washi taluka of Osamanabad district,Aundha, Basmat, Kalmnuri taluka ofHingoli district, Ausa taluka of Latur district. with average value 0.51 mg/l. While in post-monsoon seasons the analysis, result revealed that the iron found in range of 2.82 to 0.1 mg/L with Average value 0.23 mg/L. The Maximum value (2.0 mg/L) is found in Kalamnuri taluka of Hingoli district and the minimum value (0.07 mg/L) is found from Ashti, Patoda and Shirur taluka of Beed district. The spatial distribution of Fe in the groundwater is given in Fig. 9 and 9a.

Total Alkalinity (TA)

In pre-monsoon seasons Total alkalinity varies from 2 to 1600 mg/L with mean value 296 mg/L. the maximum

value for TA found is 1600 mg/L from Taluka Kannad of Aurangabad district, and the minimum value found is 2 mg/L from Taluka Loha of Nanded district. While in the post- monsoon season the maximum value for TA found is 1722 mg/L from taluka Ambajogai of Beed district and the minimum value found is 6 mg/L from taluka Aurangabad of Aurangabad district with average value 238 mg/L. The spatial distribution of total alkalinity in the selected area is given(Figs. 10 and 10a).

Chloride (Cl)

In pre-monsoon seasons chloride varies from 1 to 1760 mg/L with mean 336 mg/L. The maximum values found for chloride is 1760 mg/L from Jalna taluka of Jalna district and the minimum value found is 1 mg/L from Loha taluka of Nanded district and average value 336 mg/L. While in post- monsoon season the maximum values found for Chloride is 1800 mg/L from Renapurtaluka of Latur district and the minimum value found is 2 mg/L from Lohataluka of Nanded district and average value 140 mg/L. The spatial distribution of Cl in the groundwater isgiven in Fig. 11 and 11a.

Fluoride (F)

In pre-monsoon seasons fluoride varies from 0.1 to 5.6 with mean 1.13 mg/L. The maximum value found is 5.6 mg/L from taluka Dharmabadof district and the minimum value found is

0.1 mg/L from Beed, Ambajogai,Parli taluka of Beed district, Washi,Kallam taluka of Osamanabad district, Basmat taluka of Hingoli district and Ausataluka of Latur.While in post- monsoon season the average value for total samples analysed for fluoride is 0.54 mg/L. The maximum value found is 3.04 mg/L from taluka Dharmabad of Nanded district and the minimum value found is 0.1 mg/L, from Ambajogai,Parli, Shirur, Patoda, Ashti, Georai, Kaijtaluka of Beed district, Ausa, Udgirtaluka of Latur district, Hingoli,Kalamnuri, Basmattaluka of Hingolidistrict, Ghansangvi, Jalna, Badanapurtaluka of Jalna district, Tuljapurtaluka of Osmanabad district, Biloli, Kinwat, Nanded,Himayatnagar,Bhokartaluka of Nanded district. The spatial distribution of F in the groundwater isgiven in Fig. 12 and 12a.

Nitrate (NO3)

In pre-monsoon seasons nitrate varies from 0.1 to 275 mg/L with mean value 41 mg/L. The maximum value found is 275 mg/L from Nanded taluka of Nandedand the minimum value found is 0.1 mg/L from Kallamb, Washi taluka of Osamanabad district, Badnapur,Jalna taluk of Jalna district Kinwattaluka of Nanded district. While in post-monsoon season the average value for Nitrate is 38 mg/L. The Maximum value found is 473 mg/L from Nanded taluka of Nanded and the Minimum value found is 0.1 mg/L from Shirur, Patoda, Ashti, Georai and Kejtaluka of Beed district, Nanded taluka of Nanded and Renapurtaluka of

Latur district.The spatial distribution of NO3in the groundwater isgiven in Fig. 13 and 13a.

Sulphate (SO4)

The maximum values found for chloride is 496 mg/L from Biloli taluka of Nanded district and the minimum value found is 0.1 mg/L from Kallamb, Washi taluka of Osamanabad

district and average value 58.05 mg/L.While in post-monsoon season the maximum values found for Sulphate is 684 mg/L from Nanded taluka of Nanded district and the minimum value found is 0.1 mg/L from Ardhapur,Nanded taluka of Nanded district and Patoda,kejtaluka of Beed district and average value 60 mg/L.The spatial distribution of SO4 in the groundwater isgiven in Fig. 14 and 14a.

Table 4. BIS standards for groundwater quality

Table.5Source Analysis target as per IMIS and % of achievement

,

Water Quality Index

WQI is defined as an index reflecting the composite influence of different water quality parameters which is considered and taken for calculation of water quality index. In the first step each of the ten chemical parameters like pH, TDS, TH, Ca, TA, Cl, SO4, F, NO3 and Fe were assigned weights (w

_i) ranging from 2 to 5, and its selection depends on their

Where (W_i) is the relative weight, (w_i) is the weight of each parameter and (n) is the number of parameters Table 1. In the third step, quality rating scale calculation (Qi) for each individual parameter is computed by dividing its concentration for each groundwater sample with drinking water quality standards of BIS (2012) and then multiplied by 100 using equation (2).

significance in quality of water for drinking purposes (Table 6). In the second step is relative weights (W_i) are calculated through equation (1). standards for drinking purposes as recommended by Bureau of Indian Standards (BIS, 2012) have been used for the calculation of WQI, which involves three steps.

where Q_i is the quality rating, C_i is the concentration of each chemical parameter in

each water sample in milligrams per litre (mg/L) and S_(i ) is the Indian drinking water (BIS, 2012) guidelines for each chemical parameter. Eventually, water quality sub-index (SIi) for each chemical parameter was computed by equation (3), and the whole WQI was determined by equation (4).

Where,

SI_i is the sub-index of the ith parameter,

Q_i is the rating based on the concentration of ith parameter, and n is the total number of parameters.

The water quality index represents the integrated effects of the relevant water quality variables. The WQI can be classified as excellent water type, if the WQI values are less Measurements are converted into a single number, which are categorised as poor, marginal, fair, excellent, and exceptional, to depict changes clearly and understandably in WQ. However, region-specific WQIs are required due to the variety of standards established by national and international organisations, as well as different pollution prevention elements. Thus, there is continual interest in developing exact

than 150; good water type, it the WQI values range from 150 to 120; poor water type, if the WQI values range

WQIs suitable for a region or geographic area. Still, between 200 and 250; very poor water type, if the WQI values ranged from 250 to 300 and unsuitable for drinking water type, if the WQI values were larger than 300.

Water Quality Index.

The water quality index (WQI) is an important tool to determine the drinking water quality in urban, rural and industrial area. Groundwater samples (n = 13498 in pre and n=20559 in post-monsoon season) and its WQI values, as well as their types, are presented in Table 7 and fig.16 and 16a. During pre-monsoon, 13.25% (1789 samples) of groundwater samples were excellent; 69.11 % (9328 samples) were good; and 17.05% (2302 samples) were poor for drinking purpose; 0.53% (71 samples) were very poor and

0.05 % (8 samples) were unsuitable for drinking purposes, similarly in post-monsoon, 13.05% (2682 samples) of water samples were excellent; 69.54% (14296 samples) were good and 16.93% (3481 samples) were poor; 0.45% (93 samples) were very poor and 0.03% (7 samples ) were unsuitable for drinking purpose. (Table 7 and Fig. 15, 15a, 16 and 16a).

Background of the Study Landsat 8 has a solar synchronisation orbit with a nominal spacecraft altitude of 705 km, with the ability to orbit the Earth every 98.9 minutes. 400 scenes are captured by the Landsat 8, and it is downlinked to the USGS data archives. The scenes acquired by Landsat 8 are 185 km cross-track by 180 km along track with a regression cycle of 16 days. The design life of the Landsat 8

is 5 years and has 10 years of fuel carried on board. For commanding and housekeeping telemetry operations, S-band is used, whereas for instrument data downlink, X-band is used.

Landsat 8 Ground Stations are situated in these five locations:

1) Landsat Ground Station in South Dakota, 2) Svalbard Ground Station in Norway, 3) Alice Springs Ground Station in Australia, 4) Neustrelitz ground station in Germany and 5) Gilmore Creek ground station in Alaska. The functions of the ground system are to command and control the Landsat 8 observatory in orbit as well as to manage the data transmitted from the observatory.

Landsat 8 was launched from Vandenberg Air Force Base, California, on an Atlas-V 401 rocket. It carried onboard two

sensors: Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), as opposed to prior Landsat, where the thermal and reflective band images were acquired with the same sensor. OLI is a sensor equipped with a four-mirror telescope and a quantisation of 12 bits. It has the ability to collect data for various spectra, including visible (VIS), near infrared (NIR), short wave infrared (SWIR) bands and a panchromatic band at 0.4-2.5 µm spectrum. On the other hand, TIRS collect images in the thermal region at 10-12.5µm spectrum. The specifications of OLI/TIRS are shown in Table 1.

Coastal/aerosol, Band 1 senses deep blues and violets, where it is used to image shallow water and track fine particles such as dust and smoke. The output of Band 1 is similar to Band 2; however, the differences can be observed by contrasting and highlighting areas with deeper blue. With the request for higher resolution for ocean colour investigations, Band 1 was equipped in Landsat 8 [2]. Bands 2, 3, and 4 are visible blue, green, and red.

Table showing Specification of OLI/TIRS

Fig Time line and Histoty of Landsat Mission (adapted from [1]).

NIR, Band 5 is a spectroscopic method that uses the near- infrared region of the electromagnetic spectrum to obtain the normalised difference vegetation index (NDVI), which is a crude estimation of vegetation health and a means of monitoring changes in vegetation over time. Healthy vegetation reflects very well in the NIR part of the spectrum. NDVI is achieved by using two bands: the VIS band and the NIR band by using Equation 1.

SWIR is covered in both Band 6 and Band 7 to differentiate wet earth and dry earth, as well as rocks and soil. On the other hand, panchromatic is covered in Band 8, where it collects visible colours separately and combines them into one channel, making it black and white. With a resolution of 15 m, panchromatic is the sharpest of all bands. Hence, combining high-resolution panchromatic with lower resolution multispectral imagery in a process called pan sharpening will produce a single high-resolution colour image. The changes done in Landsat 8 compared to Landsat 7 are that at Band 4 8, bandwidth refinemnts are made to avoid atmospheric absorption features.

Band 9 covers a thin slice of wavelengths: 1370 ± 10 nanometres. Atmosphere absorbs almost the entire spectrum; however, Landsat 8 turns this into an advantage by using it to capture clouds, especially Cirrus clouds. This band is added only in Landsat 8 to detect Cirrus contamination in other channels. Bands 10 and 11 are the TIR band, where they sense heat on the ground. Band 10 is usually used to estimate surface brightness temperature in conjunction with an atmospheric model. However, Band 11 as according to notice from USGS, Jan. 6, 2014, has high ambiguity in calibration, hence it is suggested to refrain from relying on this band. These two bands are also newly added in Landsat 8.

Level 1 Landsat 8 archive data is open for public and can be downloaded for free from USGSs Earth Explorer website, and bulk downloading is supported from the Global Visualisation Viewer or Landsat Look Viewer websites. Level 2 data can also be downloaded from Earth Explorer or

EROS Science Processing Architecture (ESPA) on-demand interface websites.

The data have been widely used by researchers in numerous applications, such as agriculture, forestry, and land use. However, to the best of our knowledge, Hence, this paper discusses for the first time a survey on Landsat 8 data applications.

1. Application of Landsat 8

   Landsat was earlier used especially for remote sensing; however, it has evolved into a more diverse field, such as water quality index, agriculture, forestry and range resources, land use and mapping, geology, hydrology, coastal resources and environmental monitoring in recent years.

   1. Water Quality Index:

      The conventional approach of water quality assessment via sampling followed by laboratory measurement methods comprises the analysis of different properties, such as chemical, physical. The main idea behind the detection of water quality parameters using imaging is based on the presence of pollutants in water and the absorption of the incoming solar radiation. In this study, by considering data of conventional water quality testing, an attempt is made to identify the association between the laboratory results and the indices and bands values obtained from spatial data, to determine their applicability in water quality estimation and prediction. The study makes use of two types of data, visual, spatial and non-spatial data. The spatial data used was Landsat-8 OLI, from which the water index was calculated. While under non-spatial data, ancillary information and water parameters were considered. Based on the analysis, an approach was made to find the relation between the Water Quality Index and spatial parameters. Further, a model was established to estimate WQI from spatial data.

2. MATERIALS AND METHODS

4.1. Methodology

In this study two different types of data, visually, spatial and non-spatial data were used. The spatial data used was Landsat-8 OLI from which water index was calculated and

water body area was extracted from the raw data using shape file. While under non-spatial data ancillary information and water parameters were considered. Based on the analysis, an approach was made to find the relation between the Water Quality Index and spatial parameters. Further, a model was established to estimate WQI from spatial data.

for the current study. SMI is derived from these images, and the SMI map is generated using QGIS version 3.22.2 (Fig. 2).

1. 1. Study Area

      This investigation is performed at Paithankheda (Bidkin) village in Taluka Paithan, District Chh SambhajiNagar, and Maharashtra. The Chh SambhajiNagar district is situated in the centre of the Chhatrapati Sambhajinagar division of the state, between Open location codes 7JFQQ723+4P. From this region, Paithankheda (Bidkin) is chosen for the current investigation. (Fig.1) whose latitude is Latitude19. °4459 northLongitude75° or 75° 15 11 east.

   2. Remote Sensing Satellite Data Used

      Digital data from the LANDSAT-8 OLI is acquired through the official website of the United States Geological Survey (USGS), a NASA platform. A NASA American Earth observation satellite called Landsat-8 ensures that the Landsat program will continue to be acquired and made available. It carries two sensor payloads, OLI and TIRS, which together capture data in nine shortwave spectral bands and two longwave thermal bands (Sutariya et al., 2021). The USGS Earth Explorer website is utilised to obtain Landsat-8 imagery

      Fig. 2. Landsat-8OLI image covering the study area

   3. Water Quality (WQ) Sample Collection

      (WQ) Samples are obtained near-synchronously with the Landsat-8 acquisition date. The test fields are covered with maize vegetation, and no rainfall has been recorded in the study area during the sampling period. Samples are taken from the existing well soil with different depth, and they are then safely Collected in Regional Water Testing Lab(RWTL)stored in Plastic Bottels. These samples are then transported to the laboratory (RWTL) and Analze thereg. A total of 40 soil samples were collected from pre-determined sample points within the selected study area. In the laboratory, the samples are stored to ensure consistency for further analysis.

   4. (WQ) Water quality Determination using Field Data Water Quality Index

WQI is defined as an index reflecting the composite influence of different water quality parameters which is considered and

taken for calculation of the water quality index. The standards for drinking purposes as recommended by the Bureau of Indian Standards (BIS, 2012) have been used for the calculation of WQI, which involves three steps.In the first step each of the ten chemical

maximum value of the parameter, and minA = minimum value of the parameter.

This step involves assigning weights to each parameter. Previously, WQI calculation involved assigning unequal weights to parameters [6] or giving equal weights or no weights [7]. Usually, this is accomplished by assigning a 15 range to the variables. The high-priority variables are given a weighting of five, and low priority variables a value of one. Then, relative weights are computed. This method is known as the ranking method.

Water quality index represents the integrated effects of the relevant water quality variables. The WQI can be classified as excellent water type, if the WQI values are less than 150; good water type, if the WQI values range from 150 to 120; poor water type, if the WQI values range between 200 and 250; very poor water type, if the WQI values range from 250 to 300, and unsuitable for drinking water type, if the WQI values are larger than 300.

Normalized Difference Vegetation Index (NDVI) Map

Study Area:

Paithan Kheda is a Town in Paithan Taluka in Aurangabad District District of Maharashtra State, India. It belongs to the Marathwada region . It belongs to Aurangabad Division. It is located 51 KM towards South from District headquarters Aurangabad. It is a Taluka head quPaithan Kheda Pin code is 431107 and postal head office is Paithan.

Rangar Hati Ward ( 3 KM ) , Imali Park Ward ( 3 KM ) , Johri ( 3 KM ) , Jama Masjid Ward ( 4 KM ) , Pategaon ( 4 KM ) are the nearby Villages to Paithan Kheda. Paithan Kheda is

surrounded by Shevgaon Taluka towards South , Ambad Taluka towards East , Pathardi Taluka towards South , Aurangabad Taluka towards North.

Paithan, Pathardi, Aurangabad, and Jalna are the nearby Cities to Paithan Kheda.

Paithan kheda 2011 Census Details:

Paithan Kheda Local Language is Hindi. Paithan kheda town Total population is 1705 and number of houses are 344. Female Population is 47.8%. town literacy rate is 60.9% and the Female Literacy rate is 25.0%.

Specific, real-time water quality dta for Paithan Kheda is not readily available in the provided search results, but general information indicates that groundwater in the wider Aurangabad district, which includes Paithan, is affected by high concentrations of Nitrate and, in some areas, Fluoride. Studies of the Paithan industrial area show some variation in conductivity and Total Dissolved Solids (TDS), but the lowest TDS values recorded in a specific investigation were below the permissible limit for drinking water.

General water quality observations for the Paithan region

Groundwater: The groundwater in the Aurangabad district (which includes Paithan) can have high levels of nitrates and some areas have high fluoride concentrations.

Groundwater: The groundwater in the Aurangabad district (which includes Paithan) can have high levels of nitrates and some areas have high fluoride concentrations.

Paithan Industrial Area: A study in the Paithan industrial area noted variations in electrical conductivity, with the maximum

value recorded at one station and the minimum at another. A separate investigation in the same area found low TDS values, which were below the permissible limit for drinking water.

Water quality standards: For drinking water, key parameters often tested include pH, TDS, turbidity, fluoride, and E. coli to ensure they

Fig:Geopharic form of water quality index

CONCLUSION:

In the Marathwada region of Maharashtra, India, people depend on groundwater, and it is the only source of water for drinking needs. Therefore, it is essential to execute the hydro geochemical studies with an objective of inferring water quality with respect of drinking. During pre-monsoon, 13.25% (1789 samples) of groundwater samples were excellent; 69.11 % (9328 samples) were good; and 17.05%

(2302 samples) were poor for drinking purpose; 0.53% (71 samples) were very poor and 0.05 % (8 samples) were unsuitable for drinking purposes, similarly in post-monsoon, 13.05% (2682 samples) of water samples were excellent; 69.54% (14296 samples) were good and 16.93% (3481 samples) were poor; 0.45% (93 samples) were very poor and 0.03% (7 samples) were unsuitable for drinking purpose. Water quality management is essential in the study area, which implies utilisation and development of water in a way that maintains its quality at optimum level for the present and potential future users. Similarly, if rainwater is harvested and conserved properly, it will also help to reduce the concentrations of drinking water parameter well below the

unsafe limits for drinking purposes. In order to maintain the quality of water for its further use, preventive steps like water treatment, improvement of local sanitation system, training and public awareness programmes are recommended.

LIMITATIONS

Gravimetric method and in-situ sensors like tensiometers, time domain reflectometry (TDR), neutron probes & dielectric sensors are traditional methods to calculate soil moisture. While these techniques are highly accurate, they have limitations, such as being labour-intensive, time- consuming and often costly. Therefore, remote sensing is the best alternative to overcome these limitations.

DISCLAIMER (ARTIFICIAL INTELLIGENCE)

Author(s) hereby declare that NO generative AI technologies such as Large Language Models (ChatGPT, COPILOT, etc) and text to image generators have been used during writing or editing of this manuscript.

The authors are thankful to the RWTL (GSDA) District and DD office administration for providing the necessary

infrastructure to complete this work. The authors are also thankful to DEOGIRI (PG) College.

COMPETING INTERESTS

Authors have declared that no competing interests exist.

REFERENCES

1. Alobaidy, A. H. A. J., Abid, H. S., and Maulood, B. K. (2010). Application of water quality index for assessment of Dokan lake ecosystem, Kurdistan region. Journal of WaterResource and Protection, 2, 792-798.
2. Ashok, L., Sharma, T. C., and Jean-Francois, B. (2011). A review of genesis and evolution
3. of water quality index (WQI) and some future directions.Water Quality Exposure andHealth, 3, 11-24.
4. APHA (American Public Health Association) (2005). Standard Methods for the Examinationof Water and Wastewate. Washington D. C., USA.
5. Brezonik, P., Menken, K. D., and Bauer, M. (2005). Landsat- based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake and Reservoir Management, 21, 373

   382.

6. Brivio, P. A., Giardino, C., and Zilioli, E. (2001). Validation of satellite data for qualityassurance in lake monitoring applications. Science of the Total Environment, 268,318.
7. Burden, F. R., Mc. Kelvin, I., Forstuner, U., and Guenther, A. (2002). Environmental Monitoring Handbook. Mc Graw-Hill handbooks, New York, 3.1-3.21.
8. De, A. K. (2003). Environmental Chemistry, 5th Edition. New Age International Publisher, New- Delhi, 190,215,242-244.
9. Dekker, A. G. and Peters, S. W. M. (1993). The use of the thematic mapper for the analysis of eutrophic lakes: a case study in the Netherlands. International Journal of RemoteSensing, 14, 799 821.
10. Dor, I. and Ben-Yosef, N. (1996). Monitoring effluent quality in hypertrophic wastewater reservoirs using remote sensing. Water Science and Technology, 33, 2329.
11. Garaba, S. P., Friedrichs, A., Voß, D., and Zielinski, O. (2015). Classifying natural waters with the forel-ule colour index system: results, applications, correlations and crowdsourcing.
12. International Journal of Environmental Research and Public Health, 12,
13. 1609616109.
14. Gholizadeh, M., Melesse, A., and Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16, 12981306.
15. Gupta, N., Pande, P., and Hussain, J. (2017). Effect of physciochemical and biological parameters on the quality of river water of Narmada, Madhya Pradesh, India. Water
16. Science, 31, 11-33
17. Horton, R. K. (1965). An index number system for rating quality. Journal of Water Pollution Control Federation, 37, 300-305.
18. KC, A., Chalise, A., Parajuli, D., Dhital, N., Shrestha, S., and Kandel, T. (2019). Surface water quality assessment using remote sensing, gis and artificial intelligence. Nepal Engineers Association, Gandaki Province ISSN: 2676-1416, 1, 113-122.
19. LANDSAT 8 (L8) data users handbook (2015), Department of the Interior U.S. Geological Survey. Version 1. Available from:
20. http://landsat.usgs.gov/documents/Lan dsat8DataUsersHandbook.pdf
21. Leo, M. L. and Dekkar, M. (2000). Hand Book of Water Analysis. Marcel Dekker, New York, 1-25,115- 117,143,175,223-

    226,261,273,767

22. Lu, D., Mausel, P., Brondizio, E., and Moran, E. (2002). Assessment of atmospheric correction methods for Landsat TM data

    applicable to Amazon basin LBA ` research. International Journal of Remote Sensing, 23, 2651-2671

23. Ouma, Y. O., Waga, J., Okech, M., Lavisa, O., and Mbuthia, D. (2018). Estimation of reservoir bio-optical water quality parameters using smartphone sensor apps and landsat ETM+: review and comparative experimental results. Journal of Sensors, 2018.
24. Pawan, K. S. and Pradeep, S. (2015). Analysis of water quality of river Narmada. Intrnational
25. Journal of Current Research, 7, 24073-24076.
26. Pizani, F. M. C., Maillard, P., Ferreira, A. F. F., and Amorim, C. C. (2020). Estimation of water quality in a reservoir from Sentinel-2 and Landsat-8 OLI sensors. ISPRS Annals
27. of the Photogrammetry, Remote Sensing and Spatial Information Science, 3, 401-408.
28. Ritchie, J. C., Zimba, P. V., and Everitt, J. H. (2003). Remote sensing techniques to assess water quality. Photogrammetric Engineering and Remote Sensing, 69, 695704
29. Sawyer, C. N., McCarthy, P. L., and Parkin G. F. (1994). Chemistry for EnvironmentalEngineering, 4th Edition. Mc Graw- Hill International Edition, New York, 365-577.
30. Shah, K. A. and Joshi, G. S. (2016). Development of water pollution model: A case study
31. of Mahi river basin, Gujarat, India. Journal of Environmental Science, Toxicology andFood Technology, 10, 59-64.
32. Water quality report of Maharashtra Pollution Control Board (MPCB)-2023-2024.
33. Water Quality report of RWTL (GSDA)
34. Govt of Mahashtra 2019-2020.
35. Simoes, F. S., Moreira, A. B., Bisinoti, M. C., Gimenez, S. M. N., and Yabe, M.J.S. (2008). Water quality index as simple indicator of aquaculture effects on aquatic bodies. Ecological
36. Indicators, 8, 476-484.
37. Singh, A. K. and Jayakumar, S. (2016). Water quality assessment of Kanwar lake, Begusarai, Bihar, India. Imperial Journal of Interdisciplinary Research, 2, 793-803.
38. Sudarshan, P., Mahesh, M. K., and Ramachandra, T. V. (2018). Assessment of seasonalvariation in water quality and Water Quality Index (WQI) of Hebbal Lake, Bangalore,
39. India. Journal of Environment and Ecology, 37, 309-317.
40. Trivedy, R. K. and Goel, P. K. (1986). Chemical and Biological Methods for Water Pollution
41. Studies. Environmental Publication, India.
42. Xu, H. Q. (2006). Modification of normalised difference water index (NDWI) to enhance
43. open water features in remotely sensed imagery. International Journal Remote Sensing, 27, 30253033.