Punjab Remote Sensing Centre

Urbanization and climatic changes are concomitant to each other. Urbanization induces climate change through enhanced greenhouse gas emissions, industrial processes, and energy consumptions. In developing countries like India, with the increase in population and change in lifestyle, there has been substantial rise in demand and use of energy and fuel, which consequently leads to conversion of agricultural land to built up area. This leads to rise in land surface temperature (LST) and adversely impact the micro-climate of a region in close vicinity of urban entities. The present study aims to analyze the association between built-up, green cover and land surface temperature for selected cities and corresponding districts i.e. Amritsar, Jalandhar, and Ludhiana of Punjab State, India, the main contributors to green revolution. Adopting remote sensing based approach, district-level analysis of the normalized differential built-up index (NDBI), normalized differential vegetation index (NDVI) and land surface temperature (LST) was carried out using Landsat 5 for the years 1990, 1999 and 2009, and Landsat 8 (OLI/TIRS) for 2019. To analyze the relationship between built-up and green cover with LST, correlation analysis was carried out indicating (a) alarming rising trend of built-up is 1.72 % in Amritsar, 2.99 % in Jalandhar and 3.3 % in Ludhiana (b) relatively higher temperature in core and the peri-urban built-up areas generate warmer surface temperatures than the surroundings (c) reduced area under agricultural land and (d) rising stress on water bodies.

The deterioration of water quality in downstream dams/reservoirs may result from agricultural chemicals, industrial waste, sediment inflow due to soil erosion, and urban runoff. Therefore, conducting thorough quality analyses of dam/reservoir water is crucial to guarantee the provision of high-quality irrigation water. Keeping this in view, a study was carried out at Punjab Agricultural University, Ludhiana, to evaluate the water quality of the Dholbaha reservoir, which is a multi-purpose dam situated in the Hoshiarpur district of Punjab, India. Machine Learning techniques viz. Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), coupled with Remote sensing-derived indices and parameters, namely Chlorophyll-a (Chl-a) concentration, Normalized Difference Chlorophyll Index (NDCI), and Normalized Difference Turbidity Index (NDTI), were used for analyzing water quality of the reservoir for each month of the year 2022. Moreover, on-site investigations were carried out for water quality analysis of the reservoir during pre- and post-monsoon periods to develop a robust water quality assessment technique. Rigorous water sampling was done in the months of May and October and the collected samples were examined for electrical conductivity (EC), pH, chloride, calcium, magnesium, and total dissolved solids (TDS). Results showed a pH decrease from 8.3 (pre-monsoon) to 7.6 (post-monsoon), within the normal range. EC increased from 0.042 to 0.047 dS/m, while TDS decreased from 169.5 ppm to 139.3 ppm post-monsoon, all within suitable ranges. Chloride content decreased from 1.8 to 1.0 meq/l, and calcium and magnesium levels reduced substantially in post-monsoon, indicating improved water quality. Machine learning models effectively integrated remote sensing and field data to predict water quality, particularly turbidity. PLSR showed the highest predictive accuracy (R² = 0.99), capturing strong linear correlations between inputs and predicted values, while SVR (R² = 0.96) handled non-linear relationships, and RF (R² = 0.90) captured complex interactions with slightly higher variability. Furthermore, these models were applied to predict the water quality of the reservoir, confirming PLSR as the most efficient method, while SVR and RF also provided reliable predictions for more complex patterns. Both remote sensing and conventional analyses of water quality proved to be efficient inputs for the machine learning model, indicating effectively dynamic spatial and temporal fluctuations. Together, these methods provide comprehensive and complementary insights for effective water quality assessment and management.

In the present era, climate change coupled with population explosion, industrial advancement and upsurge in agricultural activities has put a tremendous pressure on freshwater resources leading to water crisis. To manage this ongoing issue, rainwater harvesting (RWH) at potential sites is emerging as an ecofriendly strategy for sufficing the agricultural and other domestic requirements. In this perspective, it is imperative to identify the potential sites for harvesting the excess rainwater which otherwise flows as a surface runoff. In the present study, suitable sites for rainwater harvesting have been identified in block Balachaur of District SBS Nagar Punjab (India) using GIS and multi-criteria decision-making techniques. The analytical hierarchy process (AHP) and Fuzzy-AHP techniques have been used in GIS environment to identify the most suitable sites for RWH. This study advances current GIS-based rainwater harvesting (RWH) assessment approaches by integrating both AHP and Fuzzy-AHP within a multi-criteria geospatial decision framework. Eight criterion layers including runoff depth, soil type, slope, stream order, drainage density, geology, LULC and distance to roads have been used. AHP and Fuzzy-AHP have been used to assign the weight to each layer and then weighted overlay analysis (for AHP) and fuzzy overlay (for Fuzzy-AHP) was done in ArcGIS to generate the RWH site suitability maps. The AHP-based RWH site suitability map revealed that about 7.34

Fluoride contamination in groundwater is a serious public health concern, especially in semi-arid regions like Bathinda in Punjab, where people rely heavily on groundwater for drinking and daily use. Despite several studies on fluoride contamination, research integrating uniform spatial sampling, hydrogeochemical assessment, and advanced predictive modeling remains limited. This study addresses that gap by automating groundwater fluoride prediction using deep learning techniques and evaluating seasonal hydrochemical variations in the Bathinda district. The study collected 226 groundwater samples across the pre-monsoon and monsoon seasons using GIS-based sampling at approximately 5-km intervals. Hydrochemical parameters were analyzed following APHA standards, and the Water Quality Index (WQI) was calculated. Fluoride concentrations were spatially mapped using GIS and modeled using both machine learning and deep learning approaches, specifically the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and hybrid CNN-LSTM models. To enhance model robustness, data augmentation was applied using the nearest-neighbor interpolation, creating 30,000 synthetic points. Among all models, the DNN outperformed the others, with an R2 of 0.92 (pre-monsoon) and 0.91 (monsoon), followed by the hybrid CNN-LSTM. Spatial analysis revealed fluoride hotspots exceeding WHO limits (> 1.5 ppm), strongly associated with specific lithological units, land use land cover (LULC), and geomorphological features. This integrated approach enables accurate fluoride prediction in unsampled areas, supporting early risk identification and informed decision-making. These findings are highly relevant to strategies for groundwater management, environmental monitoring, and public health planning in regions affected by fluoride.

Soil moisture retention (SMR) is the most important soil physical property that directly capture the soil’s capacity to store plant available water in hydrological analysis and for determining irrigation water requirements for agricultural crops, Characterisation of SMR at Field Capacity (FC) and Permanent Wilting Point (PWP) is important for agricultural water management, but due to the lack of technical expertise and high cost of pressure plate apparatus, this information is not readily available. So, in this study we adopted alternate method “modelling approach”. Literature based nine pedotransfer functions (PTFs) were identified/selected for predicting FC and PWP with minimum available dataset of biophysical variables. About 200 soil samples were collected from different agroclimatic zones of Punjab to calibrate (n = 78) and validate (n = 40) the selected PTFs. The models were evaluated using statistical indicators like Root Mean Square Error (RMSE), Index of Agreement (d) and mean absolute error (MAE). The PTF model developed by Pidgeon [45] and by Gupta and Larson [27] for PWP performed best, prediction capability of resp. models on validation showed RMSE-0.05, d-0.72. MAE-0.049 at FC and RMSE-0.048, d-0.770. MAE-0.041 at PWP. With the best PTFs, database of biophysical variables such as of sand, silt, clay, and SOC extracted from 520 pedons of Punjab soils was used to predict FC and PWP. The predictions were then mapped using the QGIS software. Maps revealed that in Punjab soils FC, PWP, and AW ranged from 0.131 to 0.387, 0.009–0.228, 0.113–0.183 (cm3 cm− 3), respectively. The findings highlight that validated PTF regression models can be used to predict soil moisture characteristics. Mapping the predicted information revealed the spatial variability of soil moisture content across the region that would help to configure the efficient utilisation of available water resources.