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Item SPATIOTEMPORAL ANALYSIS OF SEA SURFACE SALINITY AND SEA SURFACE TEMPERATURE ALONG THE COASTAL REGION OF KARNATAKA(Institute of Electrical and Electronics Engineers Inc., 2024) Chandana, A.; Shwetha, H.R.This study aims to analyse the spatiotemporal patterns of sea surface salinity (SSS) and sea surface temperature (SST), key parameters affecting the coastal ecosystem of Karnataka, India. The SSS and SST data were obtained from the HYCOM model, a sophisticated oceanographic model that provides high-resolution global ocean data. The analysis of SSS during the monsoon season revealed distinct spatial variations across the studied coastal region, with the most significant increases occurring in Karnataka compared to the west. SST analysis also revealed consistent warming trends in SST across all seasons, particularly during the pre-monsoonal and winter seasons, reflecting reduced freshwater inputs. SSS analysis also reveals a predominance of warming patterns across the area, indicating broader climatic shifts. During the winter season, SSS ranged from 28.45°C to 30.02°C, with 2023 marking the highest mean SST. The spatial distribution and temporal changes in SSS over 30 years provide valuable insights into long-term oceanographic trends and patterns that could be crucial for climate and marine studies. © 2024 IEEE.Item Integrating Soil Spectral Library and PRISMA Data to Estimate Soil Organic Carbon in Crop Lands(Institute of Electrical and Electronics Engineers Inc., 2024) Reddy, B.S.; Shwetha, H.R.The increasing demand for precise soil organic carbon (SOC) monitoring in croplands is crucial for food security (SDG 2), and has led to the exploration of fusing soil spectral libraries (SSLs) with hyperspectral sensing data for SOC estimation. However, the widespread adoption of SSL for SOC estimation faces challenges, particularly in developing nations, due to inconsistent calibration libraries and reliable estimation models. Furthermore, SSL rely on regular soil sample collection and spectral data recording using spectroradiometers, which is impractical in agricultural-predominant countries, such as India, with limited time for sample collection between crop rotations. To address this challenge, we developed synthesized SSL in laboratory conditions and integrated it with hyperspectral data using machine learning (ML) algorithms to bridge the gap between synthesized SSL and hyperspectral data for local-scale SOC mapping. This approach was tested by mapping SOC in Mysore, India, using spectroradiometer hyperspectral measurements and PRISMA sensor data. The proposed approach and synthesized SSL exhibited better performance prediction accuracies, R2 of 0.92 and 0.79, and the RMSE values of 2.31 and 9.91 g/kg, respectively, for PRISMA and laboratory spectroscopy data. These results highlight the potential of synthesized SSL for SOC prediction in alluvial soils, leveraging local datasets, and hyperspectral data. Our future work will expand the synthesis approach to other study areas, particularly those with alluvial soil origins, further enhancing the applicability of this methodology for SOC estimation and aiding food security efforts. © 2004-2012 IEEE.