Faculty Publications

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    The role of atmospheric correction algorithms in the prediction of soil organic carbon from hyperion data
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Minu, S.; Shetty, A.; Minasny, B.; Gomez, C.
    In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
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    Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Minu, S.; Shetty, A.; Gomez, C.
    Visible near-infrared and shortwave infrared data acquired by spaceborne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (ELm) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an ELm technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an ELm technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and ELm) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R2), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R2 ? 0.75, RPD ? 2, and RPIQ ? 2.58 than single algorithms. HAC algorithms, developed using ELm technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
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    Integrated assessment of bias correction techniques, CMIP6 model rankings, and multi-model ensemble optimisation across diverse temporal scales for regional climate projection in Kerala, Southwestern India
    (Springer Science and Business Media Deutschland GmbH, 2025) Athithottam, S.M.; Ramesh, H.
    In the context of climate change, CMIP6 (Coupled model intercomparison project phase 6) General Circulation Models (GCMs) are indispensable for projecting global and regional climate impacts, including temperature rise, precipitation variability, and extreme weather events. These models serve as the basis for Intergovernmental Panel on Climate Change (IPCC) assessments and are crucial for informing mitigation and adaptation strategies. However, their coarse resolution and systematic biases limit their direct application in local-scale climate impact studies. This motivates the present study, which aims to enhance the reliability of CMIP6 precipitation projections over Kerala, a monsoon-dominated, topographically complex region susceptible to rainfall variability. This study employs the CRITIC–TOPSIS (Criteria Importance through intercriteria correlation and technique for order of preference by similarity to ideal solution) framework to comprehensively evaluate bias correction methods, GCM performance, and multi-model ensembling (MME) techniques across multiple temporal scales. Observed daily rainfall data from the India Meteorological Department (IMD) serve as the reference for model evaluation. This integrated, data-driven approach enables robust ranking and selection of optimal models and techniques for regional application. The findings reveal considerable variability in model performance across time scales. ACCESS-ESM1-5 performs consistently well, while MRI-ESM2-0 and HadGEM3-GC31-LL are more suited to long-term projections. IITM-ESM and CMCC-CM2-SR5 show strength in short- to medium-term applications. Advanced ensemble methods, such as Support Vector Machines, Gradient Boosting Machines, Random Forests, and LightGBM, outperform simpler methods in capturing rainfall variability. The study’s results provide practical guidance for selecting climate models and designing ensemble strategies, particularly for hydrological forecasting, infrastructure planning, and climate risk assessment in Kerala and similar monsoon-prone regions. Overall, this research contributes to advancing regional climate modelling practices and supports informed, climate-resilient decision-making at policy and planning levels. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.