Avantika Latwal supervised by Dr. Rehana Shaik received  her doctorate in  Spatial Informatics  (SI). Here’s a  summary of his research work on Detecting and Mapping of Water Quality Parameters in Tropical Reservoirs using Remote Sensing and Assessment of Contributing Catchment-Related Factors:

Water quality monitoring of inland waterbodies is currently a significant challenge worldwide due to limited monitoring stations and hydrological data. The lack of adequate infrastructure for data collection becomes a major limitation in promptly and effectively assessing and minimizing potential threats to water quality. In response to this challenge, remote sensing techniques have emerged as a robust tool for continuous real-time monitoring and assessment of inland water quality. In tropical regions, there has been a greater emphasis on remote sensing techniques for the continuous monitoring of water quality, mainly in eutrophic or hypereutrophic (highly contaminated) inland water bodies such as estuaries, rivers, and lakes, while reservoirs have received comparatively less attention. In contrast, tropical reservoirs face higher contamination risk compared to other inland water bodies (estuaries, rivers, and lakes) due to their multifunctional roles, i.e., drinking, irrigation, and hydropower generation and long periods of storage times without adequate outflows. These outflows from the reservoir play a crucial role in maintaining a sufficient flow of good quality, which enhances the downstream water quality. Furthermore, these reservoirs are surrounded by land, and thus, rapid changes in landscape characteristics of the contributing catchment can introduce contaminants such as sediments, nutrients, and heavy metals. These contaminants contribute to an escalation in the levels of physical, biological, and chemical characteristics of water quality parameters. Thus, identifying the critical importance of monitoring tropical reservoirs, the present study focuses on employing RS techniques to estimate water quality parameters, i.e., based on physio-biological characteristics, mainly in oligotrophic and mesotrophic reservoirs. Additionally, the study underscores the significance of catchment-related factors that contribute to the rise in contamination levels within reservoirs. This comprehensive approach is essential for maintaining optimal water quality levels, benefiting both local communities and the surrounding environment. The present study selected two tropical reservoirs, namely, Bhadra and Tungabhadra, situated within the same river system and experiencing the same climatic conditions but distinct landscape characteristics. The first aim is to develop a modeling framework to map and estimate spatiotemporal variability of various water quality parameters of reservoirs such as Chlorophyll-a (Chl-a), turbidity and surface water temperature (SWT), and associated water spread for the period 2016-2021 using remote sensing satellite data. The second aim is to examine interconnections among selected water quality parameters, to increase the accuracy of water quality assessment in reservoirs and to identify potential correlations and interdependencies. Recognizing the fact that the detection and estimation of water quality parameters using remote sensing techniques alone are insufficient for effective water quality monitoring, the study underscored the importance of considering critical factors such as contamination sources and behaviour. Such an approach helps to understand the underlying reasons for the variation in contamination levels and to enhance the ability to improve the water quality of the reservoirs. Thus, the third aim is to comprehend how the landscape (land use/land cover (LULC), slope, and aspect) and environmental characteristics (rainfall) of the contributing catchment, along with its associated determinants, impact the level of contamination loads within the reservoirs. To validate the results of the present study based on the remote sensing approach, the primary data include field-based observations measured from the Aquaread AP7000 instrument and secondary data include EOMAP (Earth Observing and Mapping) water quality derived datasets was employed to enhance accuracy and reliability. The Chl-a, which is considered as a proxy to analyse the nutrient contamination sourced by the agricultural pollution, estimation was done using the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data using Google Earth Engine (GEE) platform. The results indicate a consistent and extensive distribution of Chl-a across the entire water surface of the Tungabhadra reservoir, while the Bhadra reservoir exhibited a more limited distribution. During the post-monsoon period, a significant increase in Chl-a spread was observed in the Tungabhadra reservoir, primarily attributed to nutrient-rich water inflows This notable rise could be linked to the harvesting of Kharif crops, resulting in elevated nutrient concentrations. In contrast, the Bhadra reservoir, surrounded predominantly by forested areas, maintained water cleanliness and served as a riparian boundary. The Bhadra Reservoir, favourable condition can be attributed to its forested surroundings, whereas the Tungabhadra Reservoir faces challenges due to nutrient runoff from agricultural and urban areas. Additionally, the discharge of untreated sewage further contributes to the degradation of water quality in the Tungabhadra reservoir. This observed variation highlights the complex impact of changes in LULC on contamination dynamics. These findings contribute to a deeper understanding of the quantities of nutrients introduced through changes in LULC and the fundamental factors influencing contamination in tropical reservoirs. Along with Chl-a estimation, the turbidity water quality parameter was analysed. Turbidity is considered as a proxy for sediments, detect and map using Sentinel-2 data. This framework demonstrates a robust capability of Sentinel 2 in detecting, mapping spatiotemporal trends and classifying turbidity levels from low to high. The Normalised Difference Turbidity Index (NDTI) values have a strong correlation with in-situ data (EOMAP and field-based observations) having values > 10 NTU (nephelometric turbidity unit) with R2 = 0.81 and standard Error Estimate (SEE) of 5.3 respectively. In addition, the correlation between red band reflectance values with < 10 NTU, has demonstrated a strong relation with an R2 and SEE of 0.74 and 1.7 respectively. Thus, the combination of NDTI and single red band reflectance values were used further for classifying the different turbidity levels (low, moderate and high). It was also observed that diverse land use patterns, particularly high proportions of urban and agricultural area along with rainfall, impact the annual and seasonal fluctuation of turbidity level in the reservoirs. Surface water temperature (SWT) is a significant physical parameter that affects aquatic ecosystems of inland water bodies in terms of metabolic rates, nutrient cycling, and species distribution. The changes in SWT affect pH, salinity, pollutant transport, and interaction of contaminants, such as nutrients and suspended sediments. The study develops a modeling framework to investigate SWT variations using Landsat 8 satellite data (2016–2021) and the Radiative Transfer Equation (RTE) method, the framework effectively captured spatiotemporal and seasonal trends, while also assessing the influence of catchment characteristics, including LULC patterns, rainfall and air temperature. The validation results demonstrated the reliability of the method for estimating SWT showing a strong relationship with R2 = 0.62 and RMSE = 0.98. Further, analysis of the relationship between reservoir water spread area and SWT revealed that larger spread areas did not significantly reduce SWT, with average temperatures consistently ranging between 28–32°C. Additionally, statistical analysis highlighted that diverse land use patterns, particularly higher proportions of urban and agricultural areas, demonstrated greater sensitivity to rising air temperatures, significantly impacted the fluctuations of SWT dynamics in the reservoirs. Furthermore, positive interdependencies among Chl-a, turbidity, and SWT were identified, indicating that changes in one parameter often correspond with variations in the others. Spatial analysis revealed that adjacent land-use zones and proximity to inflow points significantly influenced localized water quality conditions, with inlet and peripheral regions exhibiting distinct patterns of degradation. The combined analysis of these parameters enhances the robustness of reservoir water quality assessments. Overall, the study underscores the utility of fine-resolution satellite data for continuous monitoring, and highlights the critical role of integrated catchment management in mitigating anthropogenic pressures and promoting sustainable reservoir ecosystem functioning. 

December 2025