Faculty Publications
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Item Assessment of surface soil moisture from ALOS PALSAR-2 in small-scale maize fields using polarimetric decomposition technique(Springer Science and Business Media Deutschland GmbH, 2021) Gururaj, P.; Umesh, P.; Shetty, A.Surface soil moisture knowledge is important, especially in agriculture and irrigation management. Properties of microwave remote sensing like penetration power and longer wavelength facilitate retrieval of surface soil moisture. ALOS PALSAR-2, quad polarized data are used to retrieve surface soil moisture using polarization decomposition techniques in a marginal farmer small-scale maize field. The focus of the study is to explore the utility of ALOS PALSAR-2 in retrieving surface soil moisture using the polarization decomposition technique. The demonstration of the study is carried out in Malavalli village, southern India, an agricultural predominant area. The study involves field soil moisture sampling in synchronous with satellite pass, measuring soil properties, preprocessing of SAR data, polarization decomposition, proportional analysis, regression analysis, model calibration and validation. Van Zyl decomposition gave the highest surface scattering component (43%) and reduced volumetric scattering component compared to Yamaguchi and Freeman–Durden decomposition. Surface scattering component of Yamaguchi decomposition gave a good coefficient of determination (R2 = 0.8029) with field-measured surface soil moisture. The semi-empirical model (SEM) was developed using surface scattering component and depolarization ratio with adjusted R2 = 0.75 at 95% confidence interval. On its comparison with existing soil moisture models, it is observed that the developed model is performing well with RMSE and AEmax of 1.81 and 2.88, respectively. Implying the applicability of ALOS PALSAR-2 in soil moisture retrieval in marginal farmer small-scale maize fields gave satisfactory results of accuracy. © 2021, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.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 Evaluation of surface soil moisture models over heterogeneous agricultural plots using L-band SAR observations(Taylor and Francis Ltd., 2022) Gururaj, P.; Umesh, P.; Shetty, A.The goal of this study is to evaluate the efficiency of surface soil moisture models based on L-band SAR data at two different crop stages in typical Indian agricultural plots. Agricultural fields examined include paddy, tomato, sugarcane, at two distinct crop stages, and a reference fallow field. Among the evaluated models, X-Bragg model underestimates soil moisture in all agricultural fields, whereas the Oh 2004 model fits into three agricultural plots for two crop stages without any necessity of auxiliary field information. All models underperformed in the case of sugarcane at the grand growth stage. Although WCM gave best result, it came at the cost of field data utilized to calibrate model parameters. Overall, the Oh 2004 model outperforms other models across crop types and growth stages. To the best of our knowledge, this is the only study that deals with soil moisture estimations at the plot scale across different crops. © 2022 Informa UK Limited, trading as Taylor & Francis Group.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 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.