Narayana Rao Bhogapurapu

• PhD
• Visiting postdoc at California Institute of Technology

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matr...

Soil moisture retrieval over the vegetated soil surfaces using Synthetic Aperture Radar (SAR) data is a challenging issue. Presence of vegetation over soil surface makes the interaction of the radar signal with the soil more complex. Several studies used the Water Cloud Model (WCM) to separate vegetation effect on the soil backscatter while estimat...

The demand for processing tools increases with the increasing number of Synthetic Aperture Radar (SAR) satellite missions and datasets. However, to process SAR data, a minimal number of free tools are available (PolSARpro, SNAP) that consolidate all necessary pre-processing steps. Bearing this in mind, there is a need to develop specific tools for...

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we pro...

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric an...

Citation: Deb Roy, P.; Dey, S.; Bhogapurapu, N.; Chakraborty, S. Retrieval of Surface Soil Moisture at Field Scale Using Sentinel-1 SAR Data. Sensors 2025, 25, 3065. https:// Abstract: The presence of vegetation in agricultural fields affects the accuracy of soil moisture retrieval using synthetic aperture radar (SAR) data. As a result, the estimat...

Accurate crop classification with Synthetic Aperture Radar (SAR) data is a significant area of research and translating into practice from local to regional scale crop inventory mapping. With the growing accessibility to abundant data sources from both current and upcoming dual-polarimetric SAR missions, the capability to generate precise crop maps...

Continuous and operational monitoring of forest canopy structure plays an important role in assessing the global carbon budget, mapping forest disturbance, planning restoration activities, and informing decision-making. Several studies have taken advantage of synthetic aperture radar (SAR) for forest mapping and monitoring because of its regular re...

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. We have trained and validated the proposed model utilising backscatter information from E-SAR C- and L-band dual-polarimetric data acquired during the AGRISAR 2006 campaign. Further, we use the Product of Experts (PoE) fusion str...

This study proposes an extended temporal correlation model for targets with a noticeable periodic seasonal trend. Several studies have explored the nature of decorrelation in SAR interferograms. Specifically, providing a model the decay in interferometric correlation over time between two images remains a challenging task. Initially, it is necessar...

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. Backscatter information from E-SAR L- and C-band dual-polarimetric data acquired during the AGRISAR 2006 campaign were used to train and validate the proposed Gaussian Process Classifier (GPC) model. The model’s accuracy was asse...

In this paper, a Gaussian Process Regression (GPR) model is implemented to retrieve the Plant Area Index (PAI) of wheat and canola. Backscatter information from Sentinel-1 dual-pol GRD SAR data and in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign were used to calibrat...

Soil permittivity estimation using polarimetric synthetic aperture radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying s...

Polarimetric Synthetic Aperture Radar (SAR) data has been extensively used to estimate soil permittivity because of its high sensitivity to the dielectric properties of the target. However, the presence of vegetation cover induces bias in the permittivity estimates. % Previous researchers tackle this problem in various ways, including using polari...

p>Traditional survey methods for finding surface re- sistivity are time consuming and labor intensive. Very few studies have focused on finding the resistivity / conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by app...

p>Traditional survey methods for finding surface re- sistivity are time consuming and labor intensive. Very few studies have focused on finding the resistivity / conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by app...

Traditional survey methods for finding surface resistivity are time-consuming and labor intensive. Very few studies have focused on finding the resistivity/conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by applying...

Soil moisture is a critical land variable that controls the energy and mass balance in land-atmosphere interactions. Spaceborne Synthetic Aperture Radar (SAR) sensors offer an efficient way to map and monitor soil moisture because of their sensitivity towards the dielectric and geometric properties of the target. In addition, SAR acquisitions are w...

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop...

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, t...

India launched the Chandrayaan-2 satellite on 22 July 2019, with several sensors onboard to map the Lunar surface and subsurface. Among the several sensors, the Dual Frequency Synthetic Aperture Radar (DFSAR) operates in L- and S-band frequencies in full and compact polarimetric modes. This paper analyzes full- and compact polarimetric SAR data acq...

Classification of crop types using Earth Observation (EO) data is a challenging task. The challenge increases many folds when we have diverse crops within a resolution cell. In this regard, optical and Synthetic Aperture Radar (SAR) data provide complementary information about the characteristics of a target. Therefore, we propose to leverage the s...

We require spatio-temporal information about rice for executing and planning diverse management practices. In this regard, data obtained from Synthetic Aperture Radar (SAR) sensors are well suited for tracking morphological developments of rice across its phenology stages. This study proposes different target characterization parameters from polari...

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we pro...

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we pro...

ESAs' Sentinel-1 (S1) mission has reformed the field of agriculture monitoring from space with radar data. The high resolution, large swath and frequent coverage offer precise crop condition monitoring, growth rate and management practices at a global scale. The availability of dense temporal Sentinel-1 SAR data has opened new opportunities in time...

With high-resolution imaging capability and cloud independent acquisition ability, Synthetic Aperture Radar (SAR) has immense potential in estimating soil moisture. However, soil moisture estimation in the presence of vegetation is still a challenging task due to the complex interaction of SAR signal with vegetation and underlying soil. In particul...

The demand for processing tools increases with the increasing number of Synthetic Aperture Radar (SAR) satellite missions and datasets. However, to process SAR data, a minimal number of free tools are available (PolSARpro, SNAP), which consolidates all necessary pre-processing steps. Bearing this in mind, there is a need to develop specific tools f...

In this study, we propose a new vegetation index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed vegetation index is derived for two dual-pol (HH-HV and VV-VH...

In radar remote sensing applications, soil moisture retrieval over the vegetated surface is a challenging issue due to complex interaction of radar waves with vegetation layer and the underlying soil. Several studies utilized the Water Cloud Model (WCM) directly or by coupling it with surface inversion models, to compensate vegetation effects while...

Numerical modelling and simulation for Ground Penetrating Radar (GPR) was done to characterize and quantify the effect of subsurface roughness on GPR performance at 500 MHz operating frequency. An open source electromagnetic solver was utilized for modelling multiple scenarios with various subsurface roughness and its numerical simulations. Detaile...

Numerical modelling and simulation for multi-frequency Ground Penetrating Radar (GPR) have been attempted to characterize and quantify the effect of subsurface roughness on GPR performance at 500, 800 and 1000 MHz operating frequency. An open source electromagnetic solver has been utilized for modelling multiple scenario with various subsurface rou...

Remote sensing technology in combination with geographic information system (GIS) can render reliable information on vegetation cover. The analysis of spatio-temporal change in vegetation cover and its density has a greater significance in taxonomy and understanding the overall nature of biodiversity with response to recent climatic changes. We ana...