Uncrewed aerial systems (UASs) are becoming very popular in the domain of water resource mapping and management (WRMM). Being a cheaper and quicker option capable of providing high temporal and spatial resolution data, UAS has become a much sought-after platform for remote sensing. Still, their application in the field is in its early stage. This paper encompasses basic concepts of UAS, different payloads and sensor technologies available, various methodologies for its application in WRMM, different software available, and challenges associated with them, thus presenting a comprehensive review of multiple applications of UAS in different sub-domains of water resources. From cryosphere, rivers and lakes, and coastal areas to sub-surface water, as well as from water quality to wastewater management, the authors have discussed various applications of uncrewed aerial vehicles. At the end of the paper, the authors have identified the issues posing problems in the wider implementation of UAS in WRMM. Also, the future scope of the UAS in WRMM has been discussed.

High-resolution multispectral remote sensing images offer valuable information about various land features, providing essential details and spatially accurate representations. In the complex urban environment, classification accuracy is not often adequate using the complete original multispectral bands for practical applications. To improve the classification accuracy of multispectral images, band reduction techniques are used, which can be categorized into feature extraction and feature selection techniques. The present study examined the use of multispectral satellite bands, spectral indices (including Normalized Difference Built-up Index, Normalized Difference Vegetation Index, and Normalized Difference Water Index) for feature extraction, and the principal component analysis technique for feature selection. These methods were analyzed both independently and in combination for the classification of multiple land use and land cover features. The classification was performed for Landsat 9 and Sentinel-2 satellite images in Delhi, India, using six machine learning techniques: Classification and Regression Tree, Minimum Distance, Naive Bayes, Random Forest, Gradient Tree Boosting, and Support Vector Machine on Google Earth Engine platform. The performance of the classifiers was evaluated quantitatively and qualitatively to analyze the classification results with whole image (comprehensive feature) and small subset (targeted feature). The RF and GTB classifiers were found to outperform all others in the quantitative analysis of all input combinations for both Landsat 9 and Sentinel-2 datasets. RF achieved a classification total accuracy of 96.19% for Landsat and 96.95% for Sentinel-2, whereas GTB achieved 91.62% for Landsat and 92.89% for Sentinel-2 in all band combinations. Furthermore, the RF classifier achieved the highest F1 score of 0.97 in both the Landsat and Sentinel datasets. The qualitative analysis revealed that the PCA bands were particularly useful to classifiers in distinguishing even the slightest differences among the feature class. The findings contribute to the understanding of feature extraction and selection techniques for land use and land cover classification, offering insights into their effectiveness in different scenarios.

Urbanization-related growth and development have been recognized as substantial factors in increasing surface temperatures. India's rapid urbanization has exacerbated climate change challenges, particularly the rising Land Surface Temperature (LST) and Urban Heat Island (UHI) phenomena. The study used LST data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to examine decadal variances in LST and diurnal LST variations over the summer months in a study region. We compared the LST values in 2010, 2020, and 2022 with the reference year 2000 by analyzing LST data collected from 2000 to 2022 using geospatial analysis. The study found a significant increase in the LST over a two-decade period, particularly in May and June, with temperature jumps of up to 10 (°C) both during the day and at night. The study also revealed differences in LST patterns between rural and urban regions, with rural areas having a higher daytime LST than urban areas and urban areas witnessing an elevated nighttime LST owing to the UHI impact. Analysis revealed significant differences in LST patterns, with 2020 showing lower surface temperatures due to limited heat released from anthropogenic activities such as industrial operation and vehicular traffic due to the COVID-19 lockdown. However, in the years 2010 and 2022, the study observed a significant increase in LST during the summer months both during the day and at night. These findings provided useful insights for urban development and planning and contribute to the implementation of measures aimed at reducing heat-related risks and improving the well-being of urban residents.