Surface water presence varies widely, and various developments worldwide reflect human and environmental forces both directly and indirectly. For the survival of living beings, surface water is necessary and is an ideal predictor of the change of environment. Precise and up-to-date knowledge on the spatial water flow is a cornerstone for various science activities such as mapping surface water inventories, measuring water for drinking purposes and drainage, mapping and modifying land use/landscape. Remote sensing is a fast-expanding technique that provides accurate, low-cost, reliable, and even real-time data gathered for environmental change on the local, regional, and global scale. High-resolution mapping and long-term variations of worldwide surface water are provided by Jean-Francis Pekel et al. . The paper describes in detail the data sets available as part of the global Surface Water Dataset of the Joint Research Centre. It gives an objective, description, and symbology for each dataset for users to comprehend and utilize each dataset effectively and properly .
Ahmed Barakat et al.  evaluated the environmental effect of the remote sensing, geographical information system (GIS) and fuzzy analytical hierarchy process (FAHP) in the cities in Béni-Mellal, and the neighbouring communities (Morocco) between 2002 and 2016. The usual LULCC dynamics were measured with remote sensing of the picture linked with the GIS. According to Douglas E. Alsdorf, Ernesto Rodrguez, and Dennis P. Lettenmaier, measurements of the inundated area have been used as proxies for discharge with varying degrees of accuracy, but they are successful only when in situ data is available for calibration; they fail to indicate the dynamic topography of water surfaces, which explains a future satellite concept, the Water and Terrestrial Elevation .
With the growth of artificial intelligence and comprehensive data, feature extraction has become a common research subject in recent years. In Landsat scenes with a mixed water pixel on small rivers or shallow water in China, Jiang et al.  suggested an automatic method for the retrieval of rivers and lakes by integrating water indices (NDWI, mNDWI, AWEIsh and AWE Insh) with digital imaging techniques. Jiang et al. More information on surface water identification, extraction, and tracking with optical remote sensing can be found in the latest study of Huang et al. . Anza Shakeel, Waqas Sultani, and Mohsen Ali took on the complex situation of counting built structures in satellite images. The built-up area segmentation is a more accurate measure of population density, urban area development, and its influence on the environment than building density. For counting built structures in satellite images, they presented a deep learning-based regression approach .
Mustafa et al.  compared four soft computing techniques, including multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neuro-fuzzy inference system (ANFIS), and dynamic evolving neural fuzzy inference system (DENFIS), to find the best model for predicting LST changes in Beijing. Topography change was taken into account in the study to correctly forecast LST, and Landsat 4/5 TM and Landsat 8OLI TIRS pictures were utilized to analyze LST variations in the research region for four years (1995, 2004, 2010, and 2015). The effects of numerous variables on delta flood were measured by Yihan Tang et al. The Pearl River Delta (PRD) is one of China's most developed coastal areas, having a dense river network and population. Variable income flood flow, sea-level rising, and excavated riverbeds have significantly impacted the high-water levels reported during the flood season. To measure the influence of these driving variables in the PRD, a technique was devised that included a numerical model and the index R. 
Zha  proposed an unprecedented method for the automation of the process of mapping built-up areas based on the Normalized Difference Built-Up Index. Conversion of satellite imagery into a land cover map using traditional methods was a lengthy process. The novel method takes advantage of the unique spectral response of built-up areas and other land covers. Built-up areas are effectively mapped through arithmetic manipulation of re-coded Normalized Difference Vegetation Index (NDVI) and NDBI images derived from TM imagery. The NDBI method was applied to map urban land in the city of Nanjing, eastern China.
Taylor et al.  devised a remote sensing method based on multi-satellite data that offers a global estimate of land-surface open water's monthly distribution and size. The results show significant seasonal and inter-annual variability in inundation extent, with a worldwide average maximum flooded area reduction of 6% throughout the fifteen-year period, particularly in tropical and subtropical South America and South Asia. Significant increases in population over the last two decades have resulted in the largest declines in open water, implying a global scale effect of human activities on continental surface freshwater: denser populations can impact local hydrology by reducing freshwater extent, draining marshes and wetlands, and increasing water withdrawals.
Prigent et al.  developed a novel remote sensing approach to estimate the effect of change in population density from 1973 to 2007 on the surface water levels of tropical and subtropical South America and South Asia and saw an inverse relationship between the two factors.
Wolfe et al.  enumerates the expansion of Satellite Remote Sensing for Earth Science in the Earth Science Remote Sensing monograph.It also discusses the NPOESS and NPP missions. It gives emphasis on MODIS on board both Terra and Aqua. This monograph has been designed to give scientists and research students with limited remote sensing backgrounds a thorough introduction to current and future NASA, NOAA and other Earth science remote sensing missions.
LIMITATIONS OF THE EXISTING LITERATURE
There are several limitations of the existing literature in the area of this study: First, in the existing studies, the data series are not sufficiently long, and the consequent modelling and forecasting exercises may not be fully reliable for this reason. Remote Sensing data is essential to monitor the global surface water levels, especially now when the in situ data is rapidly declining. The satellite data, along with the information on the time of capture, makes it possible to analyze the change in surface water levels over the years, but researchers are still limited by the availability of adequate long term data. Hydrologic processes are characterized by the frequency with which events of a given magnitude and duration occur. Infrequent but large-magnitude events (floods, droughts) have very large economic, social, and ecological impacts. Without an adequately long record of monitoring data, it is difficult, if not impossible, to understand, model, and predict such events and their effects. Second, another limitation is the inaccuracy of the mapped data performed using deep learning techniques. Surface water mapping using various methods like deep learning has taken place in various previous researches, but many of them show a lot of false positives. These false positives arise mainly from the ice and snow, which is mistaken to be water bodies due to terrain or cloud shadows.
OBJECTIVES AND CONTRIBUTIONS OF THIS PAPER
The objective of the study is to analyze the changes in the surface water levels of India and to integrate the results with the temperature changes in that area along with the rainfall intensity change from the year 2000–2020. The changes will be mapped using ArcMap, and data analysis will be performed.
The structure of the paper is as follows: In Sect. 3, the Area of Interest is evaluated, and datasets are specified. Section 4 reports how the data obtained were processed, and raster maps were generated for further investigation of the relationships. Later on, pixel values of specific water bodies were averaged, and results are reported in Sect. 5. The paper is finally concluded in Sect. 6.