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 Application of remote sensing and GIS for identification of potential ground water recharge sites in Semi-arid regions of Hard-rock terrain, in north Karnataka, South India(Springer Science and Business Media Deutschland GmbH, 2018) Bhagwat, T.N.; Hegde, V.S.; Shetty, A.Hydro-geomorphological characteristics, together with soil, slope, lineament density and Land use Land cover are signatures of potential ground water recharge areas, and are vital for water harvesting. In the present paper, Fifth order sub-basins in Semi-arid regions of the Varada River basin in South India is studied for selection of suitable area for recharge and prioritize the sub-basins using Indian Remote Sensing satellite (IRS) P6; Linear Imaging Self Scanning Sensor (LISS III) and ArcGIS 9.2. The Fifth order sub-basins of the Varada River spread in Hard-rock terrain and of different agro-climatic zones. The study shows that there are significant spatial variations in the fifth order basins with respect to their morphometric characteristics such as the basin area, drainage density, bifurcation ratio, and circularity ratio, constant of channel maintenance and slope of the basin. These variations reflect the differences in the hydrological process in the different Sub-basins. Based on the variations in the linear, aerial, relief as well as the slope, lineament density, and precipitation pattern rankings are assigned for each parameter with respect to ground water recharge within the Subbasins. Weighted sum overlay for precipitation, Land use, soil and Water table fluctuation are used to select the suitable areas of recharge within the sub-basins. Buffers created for lineaments and drainage networks were intersected with the suitable area of recharge for the probable tank's locations for recharge. The tank locations identified after intersection and having higher stream orders are further filtered for the identification of potential sites for ground water recharge. In the prioritized sub-basins SB-8, SB-10, SB-11 locations have been selected for recharge. © 2018, Springer International Publishing AG, part of Springer Nature.Item Assessment of consumption and availability of water in the upper Omo-Gibe basin, Ethiopia(Springer, 2020) Nesru, M.; Nagaraj, M.K.; Shetty, A.Understanding water balance components is imperative for proper policy and decision making, specifically in the upper part of the Omo-Gibe basin (UOGB) Ethiopia. The objective of this study is to explore the possibility of assessing consumption and availability of water using freely available satellite data and secondary data. Using twenty-three rain gauge stations data, a spatial average of rainfall was computed using the Thiessen polygon approach. Actual evapotranspiration (ETa) was estimated through the Surface Energy Balance System (SEBS). Input data used are, 16 clouds free Moderate Resolution Imaging Spectroradiometer (MODIS) images covering the study area for estimation of the spatial distribution of actual evapotranspiration covering the whole cropping year from the months of November 2003 to October 2004. Additionally, Priestly and Taylor’s approach was used to estimate reference evapotranspiration (ET0). For the study period, the result of estimated precipitation and ETa showed that the UOGB received 41,080 mm3 of precipitation, while 24,135 mm3 become evapotranspired. The assessed outflow from the basin is 17.6% of the precipitation and demonstrated that water is a scares resource in the UOGB. © 2019, Saudi Society for Geosciences.Item Modeling of surface soil moisture using C-band SAR data over bare fields in the tropical semi-arid region of India(Springer Science and Business Media Deutschland GmbH, 2021) Gururaj, P.; Umesh, P.; Shetty, A.Spatial variability of surface soil moisture is a prime factor in modeling many environmental and meteorological processes. This study aims to model surface soil moisture in bare fields using Sentinel-1A SAR data at a regional scale. The site/plot selected for the study falls in the tropical semi-arid region of Malavalli, Karnataka, India. The study site is divided into 43 grids to collect soil moisture samples from bare field plots synchronized with Sentinel-1A pass. Sentinel-1A, dual-polarized (VV and VH) data with 5.405-GHz frequency and central incidence angle of 33° are used. Six SAR imageries were procured from ESA, out of which five were used to model field soil moisture and one for validation. Processing of the SAR imageries is carried out using SNAP 7.0 software’s standard tools, and the backscattered energy of each sample grid is extracted using R software. The relation between SAR backscatter energy with soil parameters like moisture, dielectric constant, and roughness was used to model soil moisture. Results revealed that Sentinel-1A has a high potential to record the soil moisture spatial variation at the plot scale. Volumetric soil moisture and backscattered energy showed a positive correlation with R2 of 0.59 and 0.51 for VV and VH polarization. Dielectric constant also showed a positive correlation with backscattered energy having R2 of 0.54 and 0.48 for VV and VH polarization. With this knowledge, surface soil moisture is modeled over bare fields and mapped. Soil moisture modeled is validated using field data, which has R2 of 0.88 and RMSE of 1.93. The developed model and surface soil moisture map are helpful in regional hydrological studies and crop water requirement assessment. © 2021, Società Italiana di Fotogrammetria e Topografia (SIFET).Item An exploratory analysis of urbanization effects on climatic variables: a study using Google Earth Engine(Springer Science and Business Media Deutschland GmbH, 2022) Shetty, A.; Umesh, P.; Shetty, A.Rapid global economic expansion has resulted in a drastic increase of urbanization while impacting the Earth’s entire ecology. This study evaluates the impact of historical land-use/land-cover (LU/LC) change signatures on seasonal variation of climatic variables using a cloud platform-Google Earth Engine. Due to rapid urbanization and the noticeable spatio-temporal difference in the climate, administrative units of Dakshina Kannada district are taken for demonstration. The LU/LC of the district extracted from high-resolution images of Landsat using random forest classification, land surface temperature (LST) extracted from the thermal band of Landsat images using the mono window algorithm, evapotranspiration (ET) data extracted from MOD16A2.006 and precipitation data from CHIPRS was used. The data was extracted for the pre-monsoon and post-monsoon period 2001–2019. The district has seen a 13.67% reduction in the forest area with 18.81% increase in the built-up areas. The LST and ET has seen a progressive drift in the past two decades, with an increase of 4.07 °C in median temperature in forest areas and a decline of 2.19 mm in median ET value, which necessitates monitoring forest encroachment. The higher variation in maximum LST in built-up land (0.36∘C/year/sq.km) (near the industrial area) indicates that LU/LC change signature is the predominant driving factor and is associated with the physical characteristics of the built-up area. The ET exhibited a decreasing rate of 0.62 mm/year/sq.km of the built-up land. This study highlights the power of Google Earth Engine and free availability of satellite data in environmental protection, land-use management and sustainable development in the region. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Estimation of Vertisols Soil Nutrients by Hyperion Satellite Data: Case Study in Deccan Plateau of India(Springer, 2022) Vinod, N.T.; Shetty, A.; Shrihari, S.Soil nutrients are essential for agricultural purpose. There are efforts for estimation of topsoil properties using visible and near infrared reflectance (VIS–NIR) of Hyperion satellite. Notwithstanding this, there should be more research on variety of soils and fields of practicable size especially in the Indian context, necessary for using this on a practical basis. To bridge this gap, estimation of selected soil nutrients (nitrogen (N), potassium (K), copper (Cu) and iron (Fe)) from small sized and randomly scattered vertisols fields taken up in Deccan plateau of India using Hyperion satellite data. The nutrient index (NI) for Fe was estimated to be higher (NI = 2.76) than other nutrients in the study area, which influences the spectral behavior. The pretreatment of Hyperion reflectance data by Savitzky-Golay filter (window size 15, second-order derivative) and partial least square regression (PLSR) analysis resulted in low to moderate estimations of soil nutrients. The variable importance projection (VIP) for each soil nutrient has been estimated. The important wavelengths were identified in the mid infrared region for nitrogen. For Potassium, the wavelengths were identified in the visible, near infrared and the mid infrared regions. The near infrared and midinfrared region for iron. Lastly for Cu, in the green region and mid infrared region were identified. The prediction accuracy for N, K, Fe, and Cu were estimated to be medium, with coefficients of determination values as 54%, 45%, 40%, and 41%, respectively. The vertisols in the study region demonstrated low reflectance that is deficient in humus, due to low permeability and moisture stress throughout the drought. Hence the presence of soluble nutrients concentration is low compared to other soils. In this study considering the results of R2, the iron has good prediction, then other soil properties. Thus, the present research implied that, Hyperion satellite data provides moderate potential to estimate the Indian vertisols soil nutrients. © 2022, Indian Society of Remote Sensing.Item Soil Moisture Retrieval Over Crop Fields from Multi-polarization SAR Data(Springer, 2023) Shilpa, K.; Suresh Raju, C.; Mandal, D.; Rao, Y.S.; Shetty, A.Soil moisture estimation from agriculture fields using SAR measurements is a challenging process owing to the presence of vegetation canopy. In this study, the soil moisture (SM) is retrieved from multi-polarization airborne L- and C-band E-SAR data of different agriculture fields by using the radar parameter, Radar Vegetation Index (RVI). The retrieval methodology employs the semi-empirical Water Cloud Model (WCM) for vegetation-soil system modeling, followed by an inversion algorithm based on a Look Up Table approach. The impact of using different vegetation descriptors, both from in situ measured (Leaf Area Index, Wet Biomass and Vegetation Water Content) and radar derived (L-band RVI and C-band RVI), on the WCM inversion for SM retrieval is examined. The use of the RVI as the vegetation descriptor, which is obtained from C-band data, improves soil moisture retrieval with an RMSE of 7–8% volumetric soil moisture at L-band. © 2023, Indian Society of Remote Sensing.