Manoj K. Arora

PEC University of Technology, Chandigarh, Chandigarh, India

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Publications (99)102.58 Total impact

  • N. Prabhu · Manoj K. Arora · R. Balasubramanian
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    ABSTRACT: Hyperspectral data have many applications and are being promoted over multi-spectral data to derive useful information about the earth surface. But this hyperspectral data suffers from dimensionality problem. It is one of the challenging tasks to extract the useful information with no or less loss of information. One such technique to extract the useful information is by using wavelet transformations. In this paper, a series of experiments have been presented to investigate the effectiveness of some wavelet based feature extraction of hyperspectral data. Three types of wavelets have been used which are Haar, Daubechies and Coiflets wavelets and the quality of reduced hyperspectral data has been assessed by determining the accuracy of classification of reduced data using Support Vector Machines classifier. The hyperspectral data has been reduced upto four decomposition levels. Among the wavelets used for feature extraction Daubechies wavelet gives consistently better accuracy than that produced from Coiflets wavelet. Also, 2-level decomposition is capable of preserving more useful information from the hyperspectral data. Furthermore, 2-level decomposition takes less time to extract features from the hyperspectral data than 1-level decomposition.
    No preview · Article · Jan 2016 · Journal of the Indian Society of Remote Sensing
  • M S Ganesh Prasad · Manoj K Arora
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    ABSTRACT: Fuzzy approaches are being adopted for supervised digital classification of remote sensing images. However, the use of fuzzy classification methods is restricted when compared to hard classification methods in producing land use/land cover maps from remote sensing images. The major barrier for the wider adoption of fuzzy classifications is the difficulty in evaluating the classification accuracy, as the conventional measures of accuracy are not appropriate for such classifications. To overcome this barrier, many measures of soft classification accuracy have been developed. In this paper, two measures viz., fuzzy similarity measure and fuzzy certainty measure have been used for assessing the quality of a fuzzy classification . Fuzzy c- means classification was applied to a synthetic data set to derive fuzzy membership values. The derived fuzzy membership values and the corresponding fuzzy reference data were used to compute the values of fuzzy similarity measure and fuzzy certainty measure for each of the classes considered in the classification. The results indicated that the two measures estimate the values differently and fuzzy certainty measure resembles measure of goodness of fit used in statistical models.
    No preview · Article · Oct 2015 · Journal of Geomatics
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    Varinder Saini · Ravi P. Gupta · M. K. Arora
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    ABSTRACT: Jharia coal-field holds unequivocal importance in the Indian context as it is the only source of prime coking coal in the country. Haphazard mining over nearly a century has led to environmental changes to a large extent such as degradation in quality of air, water, soil, changes in landform, land use/land cover, vegetation distribution. Jharia is also infamous for widespread development of surface and subsurface fires due to unsustainable mining practices. These fires are burning over nearly a century and are a major cause of air pollution, loss of vegetation and subsidence. The paper outlines the environmental issues related to coal mining in Jharia coalfield, Jharkhand. For studying changes in vegetation pattern over the years, Landsat TM data has been used. Analysis of vegetation index (NDVI) indicates that during the years 2004-2011, dense vegetation has decreased and sparse vegetation has increased. The utility of field based data along with remote sensing data in such studies has been emphasized.
    Full-text · Conference Paper · Oct 2015
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    Dataset: Poster- HKT

    Full-text · Dataset · Oct 2015
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    ABSTRACT: Glaciers are large persistent bodies of ice which form where accumulation of snow exceeds its ablation over many years and often centuries. Information about the size and spatial extents of glacier is of utmost importance for many research applications such as mass balance studies, melt runoff modeling, glacier hazard prediction modelling and snow-line dynamics. So, it is desirable to clearly distinguish the various components of glaciers such as snow, ice, ice-mixed debris, valley rocks, glacial lakes, moraines, crevasses and debris. Keeping in mind the difficult, vast, and inaccessible nature of mountain glaciers, remote sensing provides the most pragmatic tool for their extensive, cost-effective, and repetitive study. However, their mapping using satellite data is challenged by their spectral similarity. Although numerous techniques for mapping of glacier facies using low to moderate resolution remote sensing data (Landsat, ASTER, AWiFS etc.) have been devised, mapping of glacier facies using high spatial resolution remote sensing data still remains a bottleneck. In this study, we have made an attempt to demonstrate the performance of Object Based Image Analysis (OBIA) for mapping glacier facies of Gangotri glacier from high spatial MS remote sensing data of Worldview-2 satellite. Basic processing units in OBIA are image objects (group of pixels) and not single pixels. Therefore, the strength of OBIA is that the object characteristics such as shape, texture, spatial relationships (connectivity, distances and location etc.) can be used for classification alongwith spectral response.
    Full-text · Conference Paper · Oct 2015
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    M S Ganesh Prasad · Manoj K Arora
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    ABSTRACT: In recent years, uncertainty has become an important subject in assessing the quality of remote sensing image classifications. Fuzzy classification approaches aim to estimate the proportions of specific classes that occur within each pixel. Partial class membership values derived from fuzzy classifications serve as baseline information to assess classification uncertainties and allow the depiction of spatial variation of uncertainty. Many metrics have been developed to estimate pixel-wise classification uncertainties. The main objective of this paper was to discuss and examine the applicability of uncertainty metrics, such as exaggeration uncertainty, a measure of classification uncertainty, and two forms of confusion index, and to make a comparative assessment. A comparison of these measures was made based on hypothetical examples and the relationships between them were discussed. A multispectral image from Landsat-7 ETM+ sensor was subjected to fuzzy c-means classification. The derived class membership values for each pixel were used in assessing uncertainty in the classification. A comparative analysis of classification uncertainty provided by the metrics was carried out. The results indicated that measure of classification uncertainty and the exaggeration uncertainty discussed in this paper were similar in conveying uncertain information, but on different scales. The scale factor in this case was always greater than 1 and was given by n/(n-1), where n was the number of classes. This is the reason measure of classification uncertainty estimates values higher than exaggeration uncertainty. Another observation was that if the ratio form of confusion index and exaggeration uncertainty were combined using fuzzy algebraic sum operator, the result was the difference form of confusion index. The results from this study demonstrate the usefulness of class membership values derived from fuzzy c-means classifiers in estimating uncertainties in final thematic maps. The outcome of this research and the observations made in this paper would help the remote sensing community avoid the use of similar metrics of uncertainty under different nomenclature. Keywords:Remote Sensing; Fuzzy Classification; Quality; Classification Uncertainty; Confusion Index; Exaggeration Uncertainty
    Preview · Article · Sep 2015
  • Varinder Saini · R. P. Gupta · M. K. Arora
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    ABSTRACT: Jharia coal-field holds unequivocal importance in the Indian context as it is the only source of prime coking coal in the country. The coalfield is also known for its infamous coal mine fires which have been burning since last more than a century. Haphazard mining over a century has led to eco-environmental changes to a large extent such as changes in vegetation distribution and widespread development of surface and subsurface fires. This article includes the spatiotemporal study of remote sensing derived eco-environmental parameters like vegetation index (NDVI), tasseled cap transformation (TCT) and temperature distribution in fire areas. In order to have an estimate of the temporal variations of NDVI over the years, a study has been carried out on two subsets of the Jharia coalfield using Landsat images of 1972 (MSS), 1992 (TM), 1999 (ETM+) and 2013 (OLI). To assess the changes in brightness and greenness over the years, difference images have been calculated using the 1992 (TM) and 2013 (OLI) images. Radiance images derived from thermal bands have been used to calculate at-sensor brightness temperature over a 23 year period from 1991 to 2013. It has been observed that during the years 1972 to 2013, moderate to dense vegetation has decreased drastically due to the intense mining going on in the area. TCT images show the areas that have undergone changes in both brightness and greenness from 1992 to 2013. Surface temperature data obtained shows a constant increase from 1991 to 2013 apparently due to coal fires. The utility of remote sensing data in such EIA studies has been emphasized.
    No preview · Conference Paper · Sep 2015
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    ABSTRACT: Landslides constitute one of the major natural hazards that could cause significant loss of life and various human settlements. Mansa Devi hill near Haridwar city has encountered with such potential hazard for several years due to the instability of the slopes. Therefore, preparedness both on regional and site-specific basis at spatial level in the form of surface movements is extremely important to diminish the damage of human life and settlements. Though the surface movement measurement through field-based technique is always very accurate, this technique is time-consuming and unfeasible over a widely affected region. Therefore, areal and satellite remote sensing is gaining importance in landslide investigation due to its wide coverage. In recent years, synthetic aperture radar has already proven its potential for mapping ground deformation due to earthquake, landslide, volcano, etc. Therefore, in this study, an attempt has been made to identify the potential landslide-affected region in Mansa Devi area using one multi-temporal SAR technique and intensity tracking technique. Intensity tracking technique has identified significant mass movement in the landslide-affected region where the other conventional multi-temporal technique, SBAS, fails. An error analysis has been carried out in order to demonstrate the applicability of intensity tracking technique. This study demonstrated that intensity tracking can be considered as an alternative to conventional interferometry for the estimation of land surface displacement when latter is limited by loss of coherence due to rapid and incoherent surface movement and/or large acquisition time intervals between the two SAR images.
    Full-text · Article · Sep 2015 · Natural Hazards
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    ABSTRACT: Correlation of sub-pixels between two images acquired at different time using optical remote sensing has been used widely for estimation of glacier velocity recently. The technique has been proven for large glaciers with fairly reasonable estimate of glacier velocities as published by many researchers. However, the technique has not been applied to estimate velocities of small glaciers with glacier length of less than 10 km. In this paper we are analysing the results of this technique on a small glacier of Great Himalaya named ‘Zing-Zing-Bar’. The co-ordinates of the middle of the glacier are 32˚46ʹ02ʺ N latitude and 77˚20ʹ26ʺ E longitude. Glacier is having N-E Aspect and lies in the elevation range 4607 m – 5531 m from mean sea level. This glacier is rapidly retreating glacier due to climate change as reported earlier. To measure ground displacement using remote sensing images, we have selected Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) of 30m spatial resolution. Satellite images of the period September 2004 and August 2009 were downloaded from USGS (United States Geological Survey) website (http://glovis.usgs.gov/). In order to obtain good correlation between images, it was ensure that images are of same path and row, and are free of cloud cover. The ASTER digital elevation model (DEM) has been used for topographic analysis. For Co-registration of images band 4 of Landsat TM as well as Landsat ETM+ was used. This band represents near infrared region (NIR) in both the images. We have used COSI-corr (freely available add on tool of ENVI) downloadable from www.tectonics.caltech.edu tool for finding correlation between sub-pixels of the images. Using correlation image we obtained three output images: a North/South displacement image, an East/West displacement image and Signal to Noise ratio image (SNR). SNR image defines the quality of the correlation. Pixels of correlation having value < 0.9 were chunk out. Finally Eulerian norms were used to calculate the resultant velocity. Resultant velocity in the accumulation zone ranges from 1-58 〖ma〗^(-1) and in the middle region of the glacier, velocities of the order of 2-20 〖ma〗^(-1)are observed. Velocities around 1-5〖 ma〗^(-1) are observed near snout of the glacier. An attempt has been made to estimate the thickness of the glacier using surface velocities. Assuming basal velocity to be 25 percent of the surface velocity, thickness has been estimated by the relation given by Cuffey and Paterson(2010). Ice thickness of the glacier has been estimated in the range of 27-42 m near terminus and thickness increases to 150 m in the middle part and accumulation zone of the glacier.
    Full-text · Conference Paper · Jul 2015
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    ABSTRACT: Siachen glacier is one of the largest glaciers outside Polar region having length of more than 70 km. It lies in Karakoram Himalayas between 35.2°N-35.6°N latitude and 76.8°-77.3°E longitude with an elevation range of 4000 m to above 7000m mean sea level. This valley glacier having width of 1-8 km is fed by many tributary glaciers including two large tributary glaciers Teram-Sher and Lolofond. Nubra river which is a part of Indus drainage system originates from Siachen glacier. Study of glacier dynamics and geometry is very important in assessment of the glacier sustainability and catchment hydrology. However keeping in view the inaccessibility of the Siachen glacier, remote sensing seems to be a promising tool to study this glacier. Optical remote sensing data of Landsat Thematic Mapper (TM) and Landsat 8 OLI (Optical Land Imager) with spatial resolution of 30 m have been used to estimate the velocity of the glacier. Data of 23-August-2010 and 3-November 2013 was acquired to perform sub-pixel correlation between images. To achieve good correlation it was insured that the images are free of cloud cover and having same path and row. Near Infrared (NIR) Band of both the images was used for co-registration. COSI-corr a freely available add on tool of ENVI downloadable from www.tectonics.caltech.edu was used for finding correlation. Correlation patches of size 32 x 32 pixels corresponding to .96 x .96 km on the ground are chosen. Correlation mask parameter is set m=0.9 for initialisation and four robustness iterations were performed and a sliding step of 4 pixels (120x120m) was used. After performing above steps we obtained three output images i.e. North/South displacement image, East/ West displacement image and a signal to Noise ratio Image (SNR). SNR image defines the quality of correlation and all pixels having SNR values< 0.9 were discarded. Finally eulerian norms were used to obtain the resultant velocity. Resultant velocity ranges from nearly 1.4 - 62 〖ma〗^(-1) in the accumulation zone and around 18 - 38 〖ma〗^(-1) near snout. However maximum velocities of the order of 150 - 230 〖ma〗^(-1) were observed in the middle region of the glacier. The glacier ice thickness estimates were made using glacier velocities as given by Cuffey and Paterson (2010) and used by many researchers e.g. Gantayat et al. (2014), Linsbauer et al. (2011) etc. Glacier thickness was observed to be varying between 400-700m in accumulation zone and middle part, 100-300m and in the lower part in ablation zone and 50-100 meters near snout. Unfortunately we don’t have the ground data of surface velocity and depth measurements of the glacier to validate our results. However results published in the literature using other techniques were found to be in good agreement with this study.
    Full-text · Conference Paper · Jul 2015
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    ABSTRACT: The aim of the here presented research was to investigate the potential of two-pass differential InSAR, and advanced DInSAR techniques, such as Small Baseline Subset (SBAS) and Persistent Scatterers (PS) interferometry, in order to detect and monitor the temporal behaviour of surface deformations in selected areas of the Garhwal and Kumaon Himalaya. We present results from the surroundings of the town of Nainital, from the Mansa Devi Hills area, and the areas around the cities of Chamoli Gopeshwar and Joshimath.
    Full-text · Article · Apr 2015
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    ABSTRACT: The aim of the here presented research was to investigate the potential of two-pass differential InSAR, and advanced DInSAR techniques, such as Small Baseline Subset (SBAS) and Persistent Scatterers (PS) interferometry, in order to detect and monitor the temporal behaviour of surface deformations in selected areas of the Garhwal and Kumaon Himalaya. We present results from the surroundings of the town of Nainital, from the Mansa Devi Hills area, and the areas around the cities of Chamoli Gopeshwar and Joshimath.
    Full-text · Conference Paper · Apr 2015
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    Reet Kamal Tiwari · Manoj K. Arora · Ravi P. Gupta
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    ABSTRACT: Mapping of debris-covered glacier boundaries using remote sensing technique is restricted by the presence of supraglacial debris (debris over the glacier) since it has similar spectral properties than that of periglacial debris (debris outside glacial boundary). However, earlier studies have suggested that the temperature differences between the supraglacial and periglacial debris and/or geo-morphometric parameters can be used to separate these two classes. Several automated and semi-automated approaches have been developed for the mapping of debris-covered glacial boundaries utilizing thermal information and/or geo-morphometric parameters. Most of the techniques utilizing multisource datasets use semi-automated time consuming method of classification. In this article, a novel hybrid classification scheme utilizing both the maximum likelihood classification and knowledge based classification has been used which integrates inputs from ASTER optical, thermal and DEM remote sensing data for mapping debris-covered glacier boundary in a test area in the Chenab basin, Himalayas, India. The results of this new proposed classification scheme were compared with the classification results of maximum likelihood classification which has been used earlier by several researchers for a similar type of mapping. Further, cloud is also considered as one of the major hindrance in mapping of the glaciers due to its similar reflectance as that of snow. Additionally, the low radiometric resolution of most of the optical remote sensing data may sometimes cause serious problem in mapping glacial terrain classes due to saturation towards higher DN values due to higher reflectance of snow. A contrast enhancement using band transformation has been proposed in this remote sensing based study to resolve such problems.
    Full-text · Article · Jan 2015 · Remote Sensing of Environment
  • M S Ganesh Prasad · Manoj K. Arora
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    ABSTRACT: Representing the quality of thematic maps derived from remote-sensing image classification is important in assessing its fitness for use. Conventional approaches to represent the quality in terms of accuracy need information from the reference data at the same scale. Error-prone or dubious reference data may have an impact on the assessment of quality. Therefore, measures that complement the conventional accuracy measures are required to represent the quality. Uncertainty and confidence are such measures that do not require reference data. Few studies have been attempted to derive pixel-level confidence. However, these measures are not widely adopted by the remote-sensing community due to their limitations. In this article, a simple measure of confidence is derived to represent the quality of fuzzy classification. To derive the confidence value for a pixel, two values, viz. first highest class membership value as evidence and an associated degree of certainty, are required. When the difference between first and second highest membership values is used as degree of certainty in the proposed approach, the confidence measure derived is equal to the complement of existing measure of uncertainty, viz. confusion index in difference form.
    No preview · Article · Dec 2014 · International Journal of Remote Sensing
  • M S Ganesh Prasad · Manoj K Arora
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    ABSTRACT: In recent years, uncertainty has become an important subject in assessing the quality of remote sensing image classification. Classification uncertainty is due to poor class definition, transition zones and the presence of mixed pixels in remote sensing data. Fuzzy classification approaches aim to estimate the proportions of specific classes that occur within each pixel. Partial class membership values derived from fuzzy classification serve as baseline information to assess classification uncertainties and allow the depiction of spatial variation of uncertainty. Providing uncertainty information at pixel level may assist in increasing the confidence in using thematic maps produced from remote sensing image classification. Many metrics have been developed to quantify pixel-wise classification uncertainty. In the present study, two formulations of confusion index are used. Literature state that, the two forms of confusion index provide similar information. The present study aims at examining whether these two formulations provide similar information or not. Multispectral image from Landsat- 7 ETM+ sensor was subjected to fuzzy c-means classification. The derived class membership values for each pixel were used in quantifying classification uncertainty. A comparative analysis of classification uncertainty provided by two forms of confusion index was carried out. The results from the study show that the two forms of confusion index provide dissimilar information on classification uncertainty.
    No preview · Article · Dec 2014
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    ABSTRACT: The surface ice velocity has a major impact on the health and fate of the glacier. Measurement of ice velocity can help in modeling the glacier dynamics. This article presents sub-pixel image correlation technique (COSI-Corr) for calculating glacier ice velocity. It is difficult to obtain sufficient ice velocity data with conventional glaciological techniques (field measurements) due to the frequent loss of stakes and difficulty in the handling of measuring instruments at the site. A number of researchers have also used SAR interferometry/speckle tracking to map glacier ice velocities. However, it has been reported that SAR based works have limitations in highly rugged terrains like the Himalayas and especially for fast-moving glaciers. The literature suggests that optical image based correlation techniques appear to be more successful and robust matching method than SAR interferometry for the measurement of glacier ice velocity in Himalayan terrain, and therefore, the former is the focus in this study. The principle involved in this technique is that two images acquired at different times are correlated to find out the shift in position of any moving object, which is then treated as displacement in this time interval. Though it has been used to measure ground deformation but it has been suggested that the proposed technique would also allow for the measurement of surface displacements due to ice-flow or geomorphic processes, or for any other change detection application. The algorithm works in four fundamental steps: In the first step each pixel from the satellite focal plane is projected onto a ground reference system. This operation utilizes knowledge from both the imaging systems and the ground topography. The second step involves optimizing the satellite viewing parameters with respect to some reference frame. The third step involves resampling of the acquired images with the previously calculated parameters. This yields ground-projected images, called orthorectified images. Then in the the fourth step, image correlation is run to calculate surface ice velocities. This algorithm has now been implemented in a software package, Co-registration of Optically Sensed Images and Correlation (COSI-Corr), developed with Interactive Data Language (IDL) and integrated under ENVI. The described approach allows for the correction of offsets due to attitude effects and sensor distortions, as well as elevation errors. This methodology is thus well suited to generate accurate, low-cost glacier-ice velocity data of remote regions like Himalayan glaciers where ground instrumentation is difficult to implement and terrain conditions are inhospitable.
    Full-text · Conference Paper · Oct 2014
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    R K Tiwari · M K Arora
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    ABSTRACT: Debris cover over glaciers affects the rate of ablation and is considered as an indicator of glacier health. It also affects the ability to map glacier bodies and thereby has a bearing on the accuracy of modelling used for climate prediction, runoff estimation, etc. Mapping of debris-covered glacier boundaries using remote sensing technique is restricted by the presence of supraglacial debris since it has similar spectral properties than that of periglacial debris. However, earlier studies have suggested that the temperature differences between the supraglacial and periglacial debris and/or geo-morphometric parameters can be used to map the extent of these two classes. Several automated and semi-automated approaches have been developed for the mapping of debris-covered glacial boundaries utilizing thermal information and/or geo-morphometric parameters. Most of the techniques utilizing multisource datasets uses semi-automated method of classification which are time consuming. A novel hybrid classification scheme utilizing both the maximum likelihood classification and knowledge based classification has been used here which integrates inputs from ASTER optical, thermal and DEM remote sensing data for mapping debris-covered glacier boundary. Further, cloud is also considered as one of the major hindrance in mapping of the glaciers due to its similar reflectance as of snow. Additionally, the low radiometric resolution of most of the optical remote sensing data may sometimes cause serious problem in mapping glacial terrain classes due to saturation towards higher DN values due to higher reflectance of snow. A contrast enhancement using band transformation has been proposed in this remote sensing based study to resolve such problems.
    Full-text · Conference Paper · Oct 2014
  • Kamal Kumar · K. S. Hariprasad · M. K. Arora
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    ABSTRACT: Water Cloud Model (WCM) relates the backscatter coefficient (σo) with soil moisture. The backscatter coefficient includes the backscatter coefficient due to vegetation (σoveg), and the backscatter coefficient due to soil (σosoil). The σoveg of WCM depends upon vegetation characteristics. The present study is aimed to investigate the effect of different vegetation descriptors in estimating soil moisture from WCM. The study is carried out in Solani river catchment of India. ENVISAT ASAR images of three dates were acquired for the study. The field data, volumetric soil moisture from the upper 0-10 cm soil layer, soil texture, soil surface roughness, Leaf Area Index (LAI), Leaf Water Area Index (LWAI), Normalized Plant Water Content (NPWC) and average Plant Height (PH) corresponding to satellite pass dates were collected. Genetic Algorithm optimization technique is used to estimate the WCM vegetation parameters. The use of LAI as vegetation descriptor results in minimum root mean square error (RMSE) of 1.77 dB between WCM computed backscatter and ENVISAT ASAR observed backscatter. Also, use of LAI in WCM as vegetation descriptor results in the least RMSE of 4.19%, between estimated and observed soil moisture for the first field campaign while it was 5.64% for the last field campaign which was undertaken after 35 days of first campaign. It is concluded that LAI can be treated as the best vegetation descriptor in studies retrieving soil moisture and backscatter from microwave remote sensing data. This article is protected by copyright. All rights reserved.
    No preview · Article · Oct 2014 · Hydrological Processes
  • Kamal Kumar · M. K. Arora · K. S. Hariprasad
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    ABSTRACT: The aim of this paper is to estimate soil moisture at spatial level by applying geostatistical techniques on the point observations of soil moisture in parts of Solani River catchment in Haridwar district of India. Undisturbed soil samples were collected at 69 locations with soil core sampler at a depth of 0–10 cm from the soil surface. Out of these, discrete soil moisture observations at 49 locations were used to generate a spatial soil moisture distribution map of the region. Two geostatistical techniques, namely, moving average and kriging, were adopted. Root mean square error (RMSE) between observed and estimated soil moisture at remaining 20 locations was determined to assess the accuracy of the estimated soil moisture. Both techniques resulted in low RMSE at small limiting distance, which increased with the increase in the limiting distance. The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.
    No preview · Article · Jun 2014
  • M. K. Arora · S. Chauhan · M. Sharma

    No preview · Conference Paper · May 2014

Publication Stats

2k Citations
102.58 Total Impact Points

Institutions

  • 2014-2015
    • PEC University of Technology
      Chandigarh, Chandigarh, India
  • 2003-2015
    • Indian Institute of Technology Roorkee
      • Department of Civil Engineering
      Roorkee, Uttarakhand, India
  • 2008-2014
    • Uttar Pradesh Textile Technology Institute
      Cawnpore, Uttar Pradesh, India
  • 2002-2004
    • Syracuse University
      • Department of Electrical Engineering and Computer Science
      Syracuse, New York, United States