Faculty Publications
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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 Surface soil moisture modeling using C-band SAR observations at different stages of agricultural crops(Springer Science and Business Media Deutschland GmbH, 2023) Gururaj, P.; Shetty, A.; Umesh, P.Surface soil moisture (SSM) can be helpful in irrigation monitoring, water conservation, and a variety of other hydrological modeling applications. The majority of previous researches concentrated on the applicability or development of soil moisture models at only one stage of agricultural crop. The goal of this research is to model SSM of agricultural crops at different crop stages using C-band SAR data. The SSM of agricultural crops modeled include Paddy, Tomato, Sugarcane, and Maize fields. The whole crop cycle of these crops are divided into vegetative, maturity and yield stage. Field data like soil moisture, roughness, and Vegetation Water Content (VWC) were gathered in synchronization with the satellite pass over the study area. SEM’s for each crop stage is developed and compared to existing models like Oh 2004 and WCM. From the study, it is observed temporal variation of SSM is almost uniform for the whole crop cycle of sugarcane (~ 5%). But in case of other crops, SSM is high during the seedling/vegetative stage and comparatively less during the yield stage. Developed SSM models using SAR data is performing well in vegetative and maturity stage of all crops whereas in yield formation stage of maize and paddy error is comparatively high. On the hand, both developed and existing models did not perform well in case of sugarcane crop at maturity and yield stage. To the best of our knowledge, this is the only study that deals with surface soil moisture modeling of different crops and their stages at the plot scale in the semi-arid tropics. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Flood hazard map of the Becho floodplain, Ethiopia, using nonstationary frequency model(Springer Science and Business Media Deutschland GmbH, 2024) Tola, S.Y.; Shetty, A.Flood estimates based on stationary flood frequency models are commonly used as inputs to flood hazard mapping. However, changing flood characteristics caused by climate change necessitate more accurate assessments of the probabilities of rare flood events. This study aims to develop a flood hazard map based on the nonstationary flood frequency using a generalized extreme value distribution model for the Becho floodplain in the upper Awash River basin. The distributional location parameter was modeled as a function of rainfall amount of different durations, annual total precipitation from wet days, yearly mean maximum temperature and time as covariates. The one-dimensional Hydrological Engineering Center River Analysis System (HEC-RAS) hydraulic model with steady flow analysis was used to generate flood hazard map input, depth and velocity, and inundation extent for different return periods. The result indicated that the model as a function of rainfall, such as monthly rainfall (August) and annual wet day precipitation, provided the best fit to the observed hydrological data. Rainfall as a covariate can explain the variation in the peak flood series. The developed hazard map based on depth alone and the combination of depth and velocity thresholds resulted in more than 70% of the floodplain area being classified as a high hazard zone under 2, 25, 50, and 100-years return periods. The current study assists water resource managers in considering changing environmental factors and an alternative flood frequency model for developing flood hazard management and mitigation strategies. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2023.Item MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface(Elsevier B.V., 2024) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective. © 2024