Parul R Patel
@nirmauni.ac.in
Professor, Civil Engineering Department
Nirma University
RESEARCH INTERESTS
Construction Project MAnagement, Applications of Geomatics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jayachandra Ravi, Rahul Nigam, Bimal.K. Bhattacharya, Devansh Desai, and Parul Patel
Elsevier BV
Hemang Dalwadi and Parul Patel
Springer Nature Singapore
Angel Ratnani, Tithi Sathwara, and Parul Patel
Springer Nature Singapore
Sowkhya Badatala, Shweta Sharma, Saurabh Tripathi, Parul R. Patel, and Aloke K. Mathur
Cambridge University Press (CUP)
AbstractThe launch of the Sentinel-1B satellite in April 2016 completed the two-satellite synthetic aperture radar (SAR) constellation of the European Copernicus Sentinel-1 mission. The European Space Agency executed the calibration of this sensor during the commissioning phase and an independent calibration by the German Aerospace Center (DLR) in 2016. The calibration parameters must be monitored to assess the stability of the instrument. This study reports the temporal stability assessment of radiometric calibration and image quality parameters of Sentinel-1B SAR data using the corner reflector (CR) array, Surat Basin, Australia. Impulse response functions generated from the CRs in the satellite images were used to derive the image quality parameters. The average radar cross-section difference between estimated and theoretical values (38.40 dB m2) was 0.53 dB m2 for 1.5 m CRs, which is accordant with the absolute radiometric accuracy specified for the Sentinel-1 SAR system. Derived image quality parameters viz. the mean peak-to-side lobe ratio, mean integrated side lobe ratio, and spatial resolutions in the range and azimuth directions were found to be accordant with the specified value for the Sentinel-1 SAR system. The results indicate the excellent quality of the Sentinel-1B data.
Dhwanilnath Gharekhan, Rahul Nigam, Bimal K Bhattacharya, Devansh Desai, and Parul Patel
Springer Science and Business Media LLC
Dhwanilnath Gharekhan, Bimal K. Bhattacharya, Devansh Desai, and Parul R. Patel
Elsevier BV
Dhwanilnath Gharekhan, Bimal K. Bhattacharya, Devansh Desai, and Parul R. Patel
SPIE-Intl Soc Optical Eng
Abstract. Net surface radiation defines the availability of radiation energy on and near the surface to drive many physical and physiological processes such as latent heat, sensible heat fluxes, and evapotranspiration. One of the prime challenges of modeling radiation budget is estimation of net longwave radiation. Incoming or downwelling longwave radiation (LWin) flux is one of the two key components of net longwave radiation. Its estimation in cloudy conditions has always been a challenge due to lack of instrumentation and regular measurements at different spatial scales. In this study, two artificial neural network (ANN) multi-layer perceptron (MLP) models were developed for LWin flux estimation under cloudy-sky during daytime and nighttime using half-hourly flux measurements over different agro-climatic settings and several atmospheric parameters from measurements, satellite-based observations, and model outputs. A comparative evaluation was made between existing or newly developed multivariate linear regression (MVR) models and ANN-based models. The latter set of models were found to be superior to the best MVR model during both daytime and nighttime. The ANN models were found to have consistent performance across different sites and cloud types except less accuracy in sub-humid or humid climate and in deep convection cloud. The ANN models showed overall accuracies of 2.7% and 3.3% of measured mean and R2 of 0.86 and 0.85 for daytime and nighttime, respectively, when compared with independent data of in-situ measurements.
Ankit Chandelia, Parul R. Patel, and Dhwanilnath Gharekhan
IEEE
Hyperspectral Remote Sensing is a sub-branch of remote sensing, which captures information in contiguous spectral bands over a very narrow wavelength range. Hyperspectral data cube captures a large number of datasets in the form of multiple contiguous bands. Data Classification divides the datasets into multiple classes for feature extraction, material identification and temporal change analysis. Selection of proper classification technique is vital for higher accuracy. Single stage classifications sometimes cannot extract sub features within a single class with required accuracy. The current study deploys a multi staged classification approach for the extraction of built-up and road material over a study region. The study compares the performance between various classification techniques like Support Vector Machines (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance. Out of all classification techniques, Support Vector Machines provided the highest accuracy of nearly 90% in both the classification stages. First stage classification using SVM delivered 89.3% accuracy and further 2nd stage classification i.e., Material Identification delivered 90.0% accuracy.
Rahul Nigam, Devansh Desai, Jaychandra Ravi, Parul Patel, and Bimal K Bhattacharya
IEEE
For precision agriculture, estimation of evapotranspiration (ET) plays a key role in crop water management. ET at spatial scale is highly dependent on the land surface temperature (LST) observations. In this study, the field-scale ET is estimated using ground observed LST, NDVI and albedo measured from the ground based thermal and optical sensing. The morning and afternoon ET showed a difference of 5-31% in homogeneous crop at vegetative stage with the change in LST from 4 to 37% while physiologically matured crops showed change up to 6% with change in LST from 1-7%. The change in ET up to 22% was observed if the LST view zenith angle changes from 10° to 50° from nadir. This study showed that field-scale ET was very sensitive to time of measurement, stage of crop and view angle of LST measurements.
Dhwanilnath Gharekhan, Bimal K. Bhattacharya, Rahul Nigam, Devansh Desai, and Parul R. Patel
IEEE
Net surface radiation defines the availability of radiative energy on and near the surface to drive many physical, physiological and eco-hydrological processes such as latent heat, sensible heat fluxes and evapotranspiration. Incoming longwave radiation (LWin) is one of the key components of net longwave radiation. One of the prime challenges of modelling radiation budget is estimation of surface incoming longwave radiation. Estimation of incoming longwave radiation in cloudy and foggy conditions has always been a challenge due to the lack of instrumentation and regular measurements at different spatial and temporal scales. In this study, two neural network models (daytime and night-time) were developed for estimation of incoming longwave radiation under foggy sky using half-hourly LWin, and other meteorological parameters such Ta, RH etc. The model provided high correlation of 0.85 (daytime) and 0.86 (night-time) with Root Mean Square Error (RMSE) of 4.9% for both daytime and night-time.
Gautam Dadhich, Shweta Sharma, Mihir Rambhia, Aloke K. Mathur, P. R. Patel, and Alpana Shukla
Informa UK Limited
Abstract This study presents the results obtained from image quality assessment of Radar Imaging SATellite (RISAT-1). Image quality parameters such as spatial resolution, peak to sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are calculated by the analysis of impulse response function (IRF) of the point target. The study is carried out to assess temporal stability and consistency of image quality parameters obtained from analysis of IRF of 44 point targets. The results obtained from this study show that the mean values of the range and azimuth resolution are 2.048 ± 0.081 m and 3.383 ± 0.097 m for RH and 1.981 ± 0.081 m and 3.348 ± 0.076 m for RV, respectively. PSLR/ISLR values for RH channel are obtained as −26.492 dB/−26.823 dB for azimuth and −19.209 dB/−19.921 dB for the range. For RV channel, PSLR/ISLR values are −26.300 dB/−27.572 dB for azimuth and −19.146 dB/−19.827 dB for range.
Shweta Sharma, Gautam Dadhich, Mihir Rambhia, Aloke K. Mathur, R.P Prajapati, P.R Patel, and Alpana Shukla
Informa UK Limited
ABSTRACT Synthetic aperture radar (SAR) data used for quantitative temporal and/or spatial analysis requires calibration to ensure that observed pixel values of amplitude and phase can be related to the geophysical parameters of interest. The process of radiometric calibration of SAR images involves comparison of the backscattered radar reflectivity signal from a ground resolution element containing a calibration target of known signal response, such as a corner reflector. In this study, absolute radiometric calibration of RISAT-1 intensity data of fine resolution stripmap-1 (FRS-1) and medium resolution ScanSAR (MRS) mode was carried out by utilizing array of standard point targets of various types (triangular trihedral, square trihedral, and dihedral) with known radar cross-section deployed prior to satellite overpass with precise azimuth and elevation angles in Desalpar, Rann of Kutch in western India. The derived calibration constants using the integral method were then compared with the values provided in the header file. Deviations in the results are reported in this article. The results obtained show that the difference between the estimated average calibration constants for FRS-1 and MRS mode data with the provided value was found to be within the absolute radiometric accuracy specification of Radar Imaging SATellite (RISAT-1). Near-range to far-range difference of 0.1–0.2 dB for HH (Horizontal transmit, Horizontal receive) polarization and 0.1–0.3 dB for HV (Horizontal transmit, Vertical receive) polarization was estimated for the same scene using distributed target analysis indicating the stability of calibration for the same scene. This study also concluded that Desalpar site in Rann of Kutch has the potential of being an operational SAR calibration site.
Gautam Dadhich, Parul R. Patel, and Manik H. Kalubarme
Inderscience Publishers
Crop land suitability for wheat crop is carried out in the Patan District of North Gujarat, India. There are many factors which affect the land suitability for wheat cultivation like land use, land cover, soil texture, slope, soil pH, soil salinity, soil sodicity, soil depth, soil drainage, groundwater quality and soil nutrients [nitrogen (N), phosphorous (P), potassium (K)]. The land evaluation criteria were adopted from soil suitability manual of National Bureau of Soil Survey and Land Use Planning (NBSS % LUP), India and literature survey. All these factors have been assigned weightage obtained from Satty's method. The crop land evaluation results of the present study area were classified into four categories of wheat suitability (highly suitable, moderately suitable, marginally suitable, and unsuitable) as per Food and Agriculture Organization (FAO). These categories were arrived at by integrating the various layers with corresponding weights in Geographical Information System (GIS) environment. This study proposes a methodology for evaluation and mapping of land suitability for wheat crop using the spatial MCDM techniques. The results indicate that distribution of wheat acreage under various suitability classes was highly suitable: 34.09%, moderately suitable: 47.37%, marginally suitable: 11.76% and unsuitable: 6.76%.
Parul R. Patel and Madhav N. Kulkarni
Informa UK Limited
Abstract Anthropogenic land subsidence is associated with the removal of subsurface material during activities such as underground mining operations, underground construction, and withdrawal of natural resources like water, oil, and gas. In south Gujarat province of India, gas has been extracted from shallow depth for the last two and half years. To measure land subsidence over the study area, a precise GPS monitoring network was established in February 2004 with four reference stations and 27 deformation stations. GPS repeat observations have been carried out over this network to study land subsidence, during February 2004 to May 2006 with geodetic dual-frequency GPS receivers by GPS team, Indian Institute of Technology Bombay. The results of nine campaigns, to estimate the subsidence are discussed in this paper. One of the major causes of land subsidence over this area is gas extraction; hence the observed subsidence is compared with the production of hydrocarbon and pressure depletion. A high correlation is found between land subsidence and hydrocarbon production.