Faculty Publications
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Item Natural and anthropogenic factors controlling the dissolved organic carbon concentrations and fluxes in a large tropical river, India(2006) Balakrishna, K.; Kumar, I.A.; Srinikethan, G.; Mugeraya, G.Carbon studies in tropical rivers have gained significance since it was realized that a significant chunk of anthropogenic CO2 emitted into the atmosphere returns to the biosphere, that is eventually transported by the river and locked up in coastal sediments for a few thousand years. Carbon studies are also significant because dissolved organic carbon (DOC) is known to complex the toxic trace metals in the river and carry them in the dissolved form. For the first time, this work has made an attempt to study the variations in DOC concentrations in space and time for a period of 19 months, and estimate their fluxes in the largest peninsular Indian river, the Godavari at Rajahmundry. Anthropogenic influence on DOC concentrations possibly from the number of bathing ghats along the banks and domestic sewage discharge into the river are evident during the pre-monsoon of 2004 and 2005. The rise in DOC concentrations at the onset of monsoon could be due to the contributions from flood plains and soils from the river catchment. Spatial variations highlighted that the DOC concentrations in the river are affected more by the anthropogenic discharges in the downstream than in the upstream. The discharge weighted DOC concentrations in the Godavari river is 3-12 times lower than Ganga-Brahmaputra, Indus and major Chinese rivers. The total carbon fluxes from the Godavari into the Bay of Bengal is insignificant (0.5%) compared to the total carbon discharges by major rivers of the world into oceans. © Springer Science+Business Media, Inc. 2006.Item 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.Item Prediction accuracy of soil organic carbon from ground based visible near-infrared reflectance spectroscopy(Springer, 2018) Minu, S.; Shetty, A.The present study was conducted to predict soil organic carbon (SOC) from ground visible near-infrared (Vis-NIR, 400- 2500 nm) spectroradiometer reflectance spectra. The objective was to study the effect of various pre-processing methods and prediction models on the accuracy of SOC estimated. Measured SOC content and reflectance spectra from pasture and cotton fields of Narrabri, Australia were used in the analysis. Reflectance spectra were pretreated with different smoothing methods such as: moving average, median filtering, gaussian smoothing and Savitzky Golay smoothing. A comparison between principal component regression, partial least square regression (PLSR) and artificial neural network models was carried out to get an optimum model for organic carbon prediction. The results indicate that PLSR model performs better with Savitzky Golay as the best pre-processing method for the study area, yielding R2cal = 0:84, RPDcal = 2.55 and RPIQcal = 4.02 in the calibration set and R2val = 0:77, RPDval = 2.17 and RPIQval = 3.19 in the validation set. The study recommends a suitable method in case of limited number of soil data. Based on the study, it can be said that properly pretreated reflectance spectra show tremendous potential in soil organic carbon prediction. © Indian Society of Remote Sensing 2017.Item 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.Item Use of cellulose acetate/polyphenylsulfone derivatives to fabricate ultrafiltration hollow fiber membranes for the removal of arsenic from drinking water(Elsevier B.V., 2019) Kumar, M.; Todeti, S.; Isloor, A.M.; Gnani Peer Mohamed, G.P.S.; Siddique, I.; Ismail, N.I.; A.F., A.F.; Asiri, A.M.Cellulose acetate (CA) and cellulose acetate phthalate (CAP) were used as additives (1 wt%, 3 wt%, and 5 wt%) to prepare polyphenylsulfone (PPSU) hollow fiber membranes. Prepared hollow fiber membranes were characterized by surface morphology using scanning electron microscopy (SEM), surface roughness by atomic force microscopy (AFM), the surface charge of the membrane was analyzed by zeta potential measurement, hydrophilicity by contact angle measurement and the functional groups by fourier transform infrared spectroscopy (FTIR). Fouling resistant nature of the prepared hollow fiber membranes was evaluated by bovine serum albumin (BSA) and molecular weight cutoff was investigated using polyethylene glycol (PEG). By total organic carbon (TOC), the percentage rejection of PEG was found to be 14,489 Da. It was found that the hollow fiber membrane prepared by the addition of 5 wt% of CAP in PPSU confirmed increased arsenic removal from water as compared to hollow fiber membrane prepared by 5 wt% of CA in PPSU. The removal percentages of arsenic with CA-5 and CAP-5 hollow fiber membrane was 34% and 41% with arsenic removal permeability was 44.42 L/m2h bar and 40.11 L/m2h bar respectively. The increased pure water permeability for CA-5 and CAP-5 hollow fiber membrane was 61.47 L/m2h bar and 69.60 L/m2 h bar, respectively. © 2019 Elsevier B.V.Item Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery(Elsevier B.V., 2024) Mruthyunjaya, P.; Shetty, A.; Umesh, P.This article introduces an improved method for estimating Soil Organic Carbon (SOC) using Sentinel 2 images, with a specific emphasis on the Dakshina Kannada area in India. By examining 364 soil samples, SOC estimation models were constructed using Random forests (RF) and Partial Least Squares Regression (PLSR), focusing on the impact of nearby vegetation pixels. The approach consisted of classifying soil samples by the presence of plant pixels at distances of 0, 10, and 20 m, and evaluating the influence of dry vegetation by the use of the Normalised Burn Ratio 2 (NBR2). The findings demonstrated a significant improvement in the precision of the model (by up to 20 %) when vegetation pixels within a 20-meter radius of the sample locations were omitted. The research also included a temporal analysis utilizing Sentinel-2 images from April 2017 to May 2023. This analysis showed strong relationships between the amount of exposed soil and the accuracy of predicting soil organic carbon (SOC) levels. These results emphasize the need to take into account both the spatial dynamics of vegetation and the temporal variations in bare soil covering to get an accurate estimate of soil organic carbon (SOC). This study improves the accuracy and dependability of SOC evaluations by including geographical and temporal aspects, providing useful insights for agricultural and ecological applications. © 2024 The Author(s)