Abstract
Water quality assessment is a critical aspect of maintaining the health of aquatic ecosystem. The escalating issue of water pollution poses a significant threat to human well-being, necessitating the need for water quality evaluation. Geospatial technology, particularly GIS tools, plays a vital role in monitoring and map** water quality over larger spatial and temporal scales. This chapter explores the integration of geospatial technology and in situ observations to enhance the understanding of water quality dynamics in aquatic ecosystems. Geospatial technology, including remote sensing from satellites, offers broad-scale coverage and continuous monitoring, providing data on various optical and thermal properties of water bodies. In situ observations involve direct measurements taken at specific locations, providing ground truth data for calibration and validation. This chapter delves into the potential of machine learning and artificial intelligence (AI) techniques to process and analyze vast and diverse data sets, improving predictive modelling and parameter retrievals. It discusses challenges such as spatial and temporal resolutions, atmospheric interference, and data integration, along with solutions for data assimilation, sensor network optimization, and real-time monitoring. Overall, this chapter provides valuable insights into the integration of geospatial technology and in situ observations, offering practical guidance for researchers and water resource managers seeking to construct accurate and comprehensive water quality dynamics in aquatic ecosystems.
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Kumari, N., Kadave, K., Marandi, S., Pandey, S., Kumar, G. (2023). Constructing the Dynamics of Water Quality Parameters Using Geospatial Technology and In Situ Observations. In: Mushtaq, F., Farooq, M., Mukherjee, A.B., Ghosh Nee Lala, M. (eds) Geospatial Analytics for Environmental Pollution Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-45300-7_8
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