Faculty Publications
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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 Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data(Springer, 2022) Keerthana, N.; Salma, S.; Dodamani, B.M.In India, the majority of the population relies heavily on rice as it is their primary source of nutrition. Rice crop yield productivity depends on seasonal variations and mainly depends on hydrological conditions. Long-term water clogging in rice fields for an extended period causes crop flooding and reduces production in terms of quality and quantity. This study deals with the identification of rice crop fields and their flooding due to surface irrigation using Sentinel-1 SAR data. The identification of rice fields was attempted by classifying the image data using a random forest algorithm. For crop flooding analysis, the temporal backscatter of the corresponding fields has been extracted from SAR data and local thresholding is used. The temporal analysis of the SAR backscattering showed a similar tendency in terms of crop growth. The overall accuracy of rice crop classification for VH and VV is 97.30% and 92.24% with RMSE errors of 0.0143 and 0.0145, respectively, obtained at the peak stage of the crop. From the crop flooding analysis, it is observed that crop fields have been flooded at the growth stage due to surface irrigation and rainfall. We identified crop flooding even at the crop mature stage. In the analysis, it has been observed that the flooding is not due to irrigation water but is due to the precipitation water. © 2022, Indian Society of Remote Sensing.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 Analysis of RVI for rice crops in small-scale agricultural fields using Sentinel-1 SAR data: case study on LAI retrieval using regression algorithms(Springer Science and Business Media Deutschland GmbH, 2025) Salma, S.; Ket, S.K.; Dodamani, B.M.Leaf Area Index (LAI) is a crucial indicator for assessing plant growth, canopy structure, photosynthetic capacity, and overall productivity. The Radar Vegetation Index (RVI), a well-established microwave metric, serves as an effective tool for retrieving the LAI due to its sensitivity to vegetation characteristics. The primary objective of utilizing RVI in LAI studies is to improve the accuracy and reliability of LAI estimation, where optical methods may be hindered by atmospheric conditions. Over the past decade, numerous studies have explored the relationship between RVI and LAI, highlighting the potential of RVI for accurate LAI estimation in crops. In particular, for rice crop analysis in this study, the RVI is derived by incorporating the Degree of Polarization (DOP) from a 2 × 2 covariance matrix as the coefficient, along with the polarization backscatter of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data. The study also explores RVI derivation from M-chi (m-?) and M-delta (m-?) decomposition (assuming circularity in dual-polarized data) and linear backscattering intensities. Using the RVI’s, machine learning regression models are applied to retrieve LAI. The DOP over crop period, the temporal analysis of RVI, and in-situ LAI has been employed to examine trends during crop growth. Notably, among all derived RVIs, the one obtained using the DOP technique, particularly when combined with random forest regression, consistently exhibits superior performance for rice crop LAI estimation (R = 0.91; RMSE = 0.25 m2/m2), whereas, the R value for other models ranges a lower value of 0.63 to a higher value of 0.83 with RMSE of higher value 0.64 m2/m2 to a lower value of 0.32 m2/m2. The findings in the study highlights the sensitivity of SAR data to the DOP and the vegetation structure of rice crops in small-scale agricultural fields. © The Author(s), under exclusive licence to the International Society of Paddy and Water Environment Engineering 2024.