Faculty Publications
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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 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 Reusable floating polymer nanocomposite photocatalyst for the efficient treatment of dye wastewaters under scaled-up conditions in batch and recirculation modes(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Das, S.; Mahalingam, H.BACKGROUND: In the last decade, research on floating photocatalysts has increased rapidly with polymer substrates being a popular choice. However, most of the published work is on very small volumes and there is very little work on scale-up of such systems. RESULTS: Polystyrene–titanium dioxide nanocomposite floating films were prepared using a facile solvent casting method and tested for the photocatalytic degradation of four different dyes under UV irradiation. The prepared film was characterised by Fourier-transform infrared (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma optical emission spectrometry (ICP-OES) and profilometry. Scale-up studies were done in batch mode under optimised conditions, and for the larger reactor volume, the effect of recirculation was studied. Complete decolourisation of the model dye (Remazol Turquoise Blue) was observed within 80 min in the scaled-up batch process. In the recirculation mode, for a much larger volume of the dye solution, around 75% decolourisation in 6 h was observed. The reusability of the photocatalytic film was tested, and the results promise a minimum decolourisation efficiency of around 70%. Finally, total organic carbon (TOC) and liquid chromatography mass spectrometry (LC-MS) analysis were used to assess the degradation of the dye. The maximum TOC reduction observed was around 25% possibly due to the complex nature of the dye used in this study. The intermediate products of degradation were identified, and a tentative mechanism is suggested. CONCLUSION: This work demonstrates the recirculation aspects of the photocatalytic reactor under the scaled-up conditions for a complex dye. The prepared film showed excellent stability with satisfactory wastewater decontamination under UV irradiation even after repeated use. © 2019 Society of Chemical Industry. © 2019 Society of Chemical IndustryItem Solar assisted photocatalytic degradation of organic pollutants in the presence of biogenic fluorescent ZnS nanocolloids(Elsevier Ltd, 2019) Uddandarao, P.; Hingnekar, T.A.; Mohan Balakrishnan, R.M.; Rene, E.R.The main aim of this study was to ascertain the photocatalytic degradation of organic pollutants present in aqueous phase using fluorescent biogenic ZnS nanocolloids produced from an endophytic fungus Aspergillus flavus. The degradation studies were carried out using different organic pollutants such as methyl violet (MV), 2,4-dichlorophenoxyacetic acid (2,4-D) and paracetamol (PARA) for 120 min, 270 min and 240 min, respectively, at pH varying from 3.0 to 11.0. The results from this study indicate that the degradation efficiency of ZnS nanocolloids for MV, 2,4-D and PARA were 87%, 33% and 51%, respectively, at the optimum concentration of 100 mg/L of the tested organic pollutants. At different time intervals, the samples were analyzed for their chemical oxygen demand (COD) and total organic carbon (TOC) contents. The reduction of COD and TOC were 78% and 74% for MV at 120 min; 55.5% and 57.2% for 2,4-D at 270 min and 47.6% and 44.5% for PARA at 240 min, respectively. The degradation pathway was determined based on the mass spectrum and the intermediates formed; in addition, the interaction between organic pollutants and nanocolloids was also elucidated based on atomic force microscopy (AFM) and fluorescence spectrum. © 2019 Elsevier LtdItem Novel immobilized ternary photocatalytic polymer film based airlift reactor for efficient degradation of complex phthalocyanine dye wastewater(Elsevier B.V., 2020) Das, S.; Mahalingam, H.Reduced graphene oxide (rGO) as well as graphitic carbon nitride (g-C3N4) catalysts were synthesized and a physical admixture of rGO and g-C3N4 along with TiO2 in the ratio of 1:1:1 by weight was immobilized in a polystyrene film using the facile solvent casting method. An internal loop airlift reactor with a working volume of 1.2 litres incorporating the prepared polymer-based photocatalytic film was designed and tested for the photocatalytic degradation of remazol turquoise blue dye synthetic wastewater. The reactor parameters affecting the photocatalytic activity such as airflow rate and Di/Do (ratio of draft tube diameter to outer tube diameter) were evaluated. The successful operation of the reactor obtained using the ternary immobilized catalyst mixture film gave 92.25% total organic carbon reduction and 94% decolourization within 140 min, compared to 91% decolourization by the slurry form within 40 min. Complete and quicker decolourization of the dye was also demonstrated under the influence of O3 or H2O2. The immobilized catalyst was successfully reused four times. The ternary catalyst admixture employed in this work and the unique design of the photocatalytic reactor helps to increase the degradation rate of toxic textile effluents thus making it suitable for larger scales of treatment. © 2019 Elsevier B.V.Item Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data(MDPI, 2022) Mruthyunjaya, P.; Shetty, A.; Umesh, P.; Gomez, C.Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model’s performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R2cv and RMSEcv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R2cv ranging from 0.72 to 0.79 and RMSEcv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR. © 2022 by the authors.Item Canopy centre-based fuzzy-C-means clustering for enhancement of soil fertility prediction(Inderscience Publishers, 2024) Sujatha, M.; Jaidhar, C.D.For plants to develop, fertile soil is necessary. Estimating soil parameters based on time change is crucial for enhancing soil fertility. Sentinel-2’s remote sensing technology produces images that can be used to gauge soil parameters. In this study, values for soil parameters such as electrical conductivity, pH, organic carbon, and nitrogen are derived using Sentinel-2 data. In order to increase the clustering accuracy, this study suggests using Canopy centre-based fuzzy-C-means clustering and comparing it to manual labelling and other clustering techniques such as Canopy, density-based, expectation-maximisation, farthest-first, k-means, and fuzzy-C-means clustering, its usefulness is demonstrated. The proposed clustering achieved the highest clustering accuracy of 78.42%. Machine learning-based classifiers were applied to classify soil fertility, including Naive Bayes, support vector machine, decision trees, and random forest (RF). Dataset labelled with the proposed RF clustering classifier achieves a high classification accuracy of 99.69% with ten-fold cross-validation. © 2024 Inderscience Enterprises Ltd.. All rights reserved.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.