Faculty Publications
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Item Surface Soil Moisture Retrieval Using C-Band Synthetic Aperture Radar (SAR) over Yanco Study Site, Australia—A Preliminary Study(Springer, 2020) Gururaj, G.; Umesh, U.; Shetty, A.The motto of this work is to evaluate retrieval of surface soil moisture (5 cm) using Sentinel-1a C-band data Synthetic Aperture Radar (SAR). Data for this study is collected from Yanco Study site, Australia. Yanco study site consists of 37 soil moisture measuring stations at every 20 min interval for various soil depths and it also provides other Hydro-Meteorological information. SAR backscattered energy is a function of soil roughness and soil moisture. Surface roughness is eliminated using change detection approach. The R2 performance statistics revealed that between Backscattered energy and NDVI there is no relation. Volumetric soil moisture and backscattered energy showed a positive correlation with R2 = 0.57 and 0.43 for VH and VV polarization. Dielectric constant also showed a positive correlation with backscattered energy having R2 = 0.62 and 0.38 for VH and VV polarization respectively. By taking into account of all these affecting parameters, a regional Semi-empirical model is developed to retrieve surface Soil moisture over the study area. © Springer Nature Singapore Pte Ltd. 2020.Item A Resolution Independent 2-Bits-per-Cycle SAR ADC(Institute of Electrical and Electronics Engineers Inc., 2014) Morakhia, A.; Gunnam, S.; Prakash, P.; Kudli, S.; Laxminidhi, T.This paper proposes a resolution independent architecture for SAR ADCs. The proposed architecture uses 2 bits per cycle conversion and is made independent of number of ADC bits. A 2-bit flash ADC is used to compute 2-bits in each iteration. The reference voltage across the resistor divider of the 2-bit flash ADC is changed in each iteration based on the 2-bits resolved in the previous iteration. The reference voltages for each iteration are generated using a pair of modified switched capacitor-based DACs. The new DAC architecture used in the proposed ADC can use the thermometric code output of the 2-bit flash ADC directly, avoiding the need for complex control circuitry. The dependency of DAC architecture on the ADC resolution, observed in conventional SAR ADCs, has been absorbed into the digital logic which is easy to design. The proposed architecture is validated using an 8 bit ADC designed in 0.25 μm CMOS process. © 2014 IEEE.Item 3-D radar imaging using extended 2-D range migration technique(Institute of Electrical and Electronics Engineers Inc., 2017) Nagarad, S.R.; Sourabh, A.S.; Shripathi Acharya, U.S.; Srihari, P.; Prasad, S.; Rao, P.H.A three dimensional (3-D) imaging system is implemented by employing 2-D range migration algorithm (RMA) for frequency modulated continuous wave synthetic aperture radar (FMCW-SAR). The backscattered data of a 1-D synthetic aperture at specific altitudes are coherently integrated to form 2-D images. These 2-D images at different altitudes are stitched vertically to form a 3-D image. Numerical simulation for near-field scenario are also presented to validate the proposed algorithm. © 2017 IEEE.Item Assessment of spatial variation of soil moisture during maize growth cycle using SAR observations(SPIE spie@spie.org, 2019) Gururaj, P.; Umesh, U.; Shetty, A.Spatial Information about Soil moisture over agricultural crops are required for efficient irrigation which in turn helps in saving water and increases crop yield. Soil moisture also useful in prediction of flooded and drought regions. However field measurement of soil moisture is not a practical approach. The main objective of the study is to track soil moisture variation all along the maize growth period in a Semi-Arid region. There are only few studies carried out on soil moisture variation considering whole maize growing period. During the crop growing period soil moisture field investigation are conducted in synchronization with Satellite pass. Sentinel-1a Synthetic Aperture RADAR (SAR) satellite, Interferometric wide swath dual polarized data with 5.405 GHz frequency and central incidence angle of 23ï?° with repeat period of 12 days was used in this study. All in all during growth period 6 satellite pass scenes are acquired and processed by standard procedure using Sentinel Application Platform (SNAP) software. An attempt was made to redeem surface soil moisture for the whole maize growing crop cycle using water cloud model. The whole period of maize crop was divided into 3 parts like seedling, growing and harvesting period and soil moisture is retrieved for each period. The estimated soil moisture was validated with 30 field measured soil moisture samplings. The correlation coefficient of retrieved and actual soil moisture of seedling, growing and harvesting periods are 0.77, 0.72 and 0.6 respectively. The output of this study will be helpful in formulating strategies for irrigation water management. © 2019 SPIE.Item Mapping of Flood-Inundated Urban Regions Using Sentinel-1 SAR Imagery for the 2018 and 2019 Kerala Floods(Springer Science and Business Media Deutschland GmbH, 2023) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.Floods are a common natural calamity causing an immense impact on the natural and human ecosystems around the world. A combination of unfavorable meteorological, hydrological, and physical conditions causes it. The study area is the Vembanad Lake System in Kerala, India comprising six watersheds: Periyar, Muvattupuzha, Meenachil, Manimala, Pamba, and Achenkovil that drains into the lake. The state faced severe flooding in 2018 and 2019 due to torrential rainfall. Thus, this study focuses on assessing flood inundation mapping utilizing Sentinel-1 SAR imagery in Google Earth Engine (GEE) for 2018 and 2019 since it simplifies and streamlines the complicated and time-consuming pre-processing of Sentinel-1 SAR images. These images are pre-processed, and the flooded areas are delineated. Change detection by image ratio method is utilized to identify the flood inundated and the most frequently flooded areas. The results show that 4% and 3.21% of the entire region were flooded in 2018 and 2019, respectively. In addition, 14.7 Km2 of the urban area flooded in 2018, whereas 7.26 Km2 of urban land flooded in the 2019 floods. Hence, these inundation maps can be utilized for risk assessment and primary preventive measures. It also serves as a tool to warn the residents in that region about the hazards and the possibility of inundations at the time of heavy downpours in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Target decomposition using dual-polarization sentinel-1 SAR data: Study on crop growth analysis(Elsevier B.V., 2022) Salma, S.; Keerthana, N.; Dodamani, B.M.Decomposition of synthetic aperture radar data has been carried out using fully polarised microwave bands. Considering the economical point of view, fully polarimetric SAR data is expensive to use for many applications like soil and agriculture, where, it is important to monitor frequently. With the advancement of human civilization, new agricultural techniques and crops are being developed to meet global food demand. With the development of crop growth, crop texture and dielectric properties varies, which is depicted in the backscattering values along with the crop growth. In this study, Sentinel-1 SAR data is used, which is freely available in dual polarimetric mode with a temporal revisit of 12 days with respect to the transmitter polarizations. In this work, we attempt to decompose the targets from dual polarised SAR data using entropy and alpha bands of H-A-α decomposition. The entropy and alpha band clusters are obtained by using K-means unsupervised classification is utilized for target decomposition. The clustering process is repeated 30 times for 100 iterations to obtain the optimum grouping of pixels. The clusters are plotted on the H-α plane to get an H-α plot of dual-polarization SAR data for target decomposition. The obtained H-α plot is used to identify the crop stages and its scattering mechanism at different growth stages. Crops grown in selected crop fields during the considered period include ginger, tobacco, rice, cabbage, and pumpkin. An attempt is made by plotting the time-series trends of early and late-planted crops with peak mature stages in terms of backscattering analysis, and the results were compared to the H-α plot to gain a better understanding of crop growth scattering mechanisms at various growth stages. Although the backscattering values for the VH and VV polarizations of crops are different, the temporal backscattering study showed the same trend for both polarizations with good similarity of VH polarization than VV for analyzing crop growth stages. The crop growth scattering mechanism on the H-α plane produced similar results to the temporal analysis. © 2022 Elsevier B.V.Item Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India(Elsevier B.V., 2023) Subbarayan, S.; Devanantham, D.; Reddy, N.M.; Kulithalai Shiyam Sundar, P.; Janardhanam, N.; Sathiyamurthi, S.; Vivek, V.Kerala experiences a high rate of annual rainfall and flooding resulting in a frequent natural disaster. The objective of this study is to develop flood susceptibility maps for the Idukki district making use of Remote Sensing (RS) data, Geographic Information System (GIS), and Machine Learning (ML). In this study, five different ML models (Adaboost, Gradient boosting, Extreme Gradient Boosting (XGB), CatBoost, Stochastic Gradient Boosting (SGB)) are evaluated to determine flood susceptibility in Idukki district Kerala. There were a total of sixteen hydrometeorological parameters taken into account. Area under the curve (AUC) was used to evaluate the accuracy of various techniques in terms of both prediction and success rates. The validation results proved the efficiency of the individual techniques. The highest AUC was obtained by the SGB and GBC (92%), followed by that of the Adaboost with AUC 87%, and the lowest AUC was obtained by CatBoost, with AUC of 79%. The absence of data overfitting in all models demonstrates the efficacy of boosting techniques. The boosting algorithms penalize models that overfit the training set, which helps to decrease overfitting. Researchers and local governments could benefit from the proposed boosting techniques in the flood susceptibility mapping and mitigation strategies. © 2023