Urban area classification is one of the important applications of Polarimetric Synthetic Aperture Radar (POLSAR). This paper suggests an effective technique for classifying the terrain using different polarimetric decompositions and circular correlation coefficient (CCC) along with the total power. It is observed that after applying decomposition, the oriented urban area is getting classified as forest. The result of proposed technique shows that the classification accuracy increases significantly for settlement class i.e., most of the oriented settlement gets classified as settlement not as forest.

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. This work utilizes the scattering-type parameters: alpha$(\overline{\alpha})$and theta$(\theta_{\text{FP}})$to estimate soil permittivity using the X-Bragg as the dominant surface scattering model. A theoretical study ascertains that the recently proposed$\theta_{\text{FP}}$is fairly robust towards the depolarizing component in the X-Bragg model. Hence, it is expected to produce better inversion accuracy. This study analyzes major phenology stages of Canola using the UAVSAR full-pol SAR data and the ground measurements acquired during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method achieved an RMSE of 5.9 for soil permittivity with a Pearson coefficient,$r=0.83$. Further, the temporal trend of the soil permittivity estimates also agrees with in-situ measurements for the entire timeframe.

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 soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θFPand θCP, respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θFPand θCP, unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient,r≥ 0.62.

Accurate and high-resolution spatio-temporal information on wheat growth is an essential factor for agronomic management and grain yield estimation. In this study, we propose a new vegetation descriptor from the Sentinel-1 Synthetic Aperture Radar (SAR) GRD data for monitoring the growth stages of wheat. We also assess the performance of the proposed vegetation descriptor for estimating wheat biophysical parameters: Plant Area Index (PAI), Dry Biomass (DB), and Vegetation Water Content (VWC) over the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) test site in Manitoba, Canada. The proposed vegetation descriptor produced good correlation$(R^{2})$with the biophysical parameters of wheat: 0.63 (PAI), 0.64 (DB), and 0.57 (VWC) compared to$\sigma_{\text{VH}}^{\mathrm{o}}/\sigma_{\text{VV}}^{\mathrm{o}}$and the dual-pol Radar Vegetation Index (RVI).

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 estimating soil moisture. The realization of vegetation component in the original form of WCM utilizes various plant descriptors (e.g., vegetation water content (VWC) and plant area index (PAI)). These descriptors were eventually replaced with vegetation metric obtained from ancillary sources (e.g., the Normalized Difference Vegetation Index -NDVI derived from the optical sensor). To overcome this dependency on ancillary data, we utilize radar derived vegetation descriptors to estimate soil moisture over canola fields. We investigated the performance of WCM for soil moisture retrieval utilizing the PAI and radar derived vegetation descriptors, i.e., the ratio of backscatter intensities (HH/VV and VH/VV) and indices (viz., Radar Vegetation Index (RVI), and Generalized Radar Vegetation Index (GRVI)) in WCM. The radar data derived vegetation descriptors provides encouraging retrieval accuracy with RMSE ranging from 0.04 (for GRVI) to 0.08 m3m-3(for HH/VV). This comparative analysis using different polarizations indicates that the HH polarization outperforms VV, while VH has marginal deviations for all descriptors.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

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) modes obtained through a simulation from L-band full-pol UAVSAR data. Time-series simulated NISAR data are obtained from the UAVSAR data acquired during the SMAPVEX12 campaign over the CAL/VAL test site in Winnipeg (Canada), to assess the proposed vegetation index. The temporal trend of DpRVI follows the growth stages of canola with a promising correlation of DpRVI with several biophysical variables. Correlation analysis indicates that DpRVI derived for VV-VH mode correlates better with the canola biophysical parameters than the HH-HV mode.

The Kerala state of India experienced a devastating flood during Aug 2018, which incurred huge socio-economic losses and human fatalities. ALOS-2 L-band SAR image acquired during the peak flood was used in this study. A split-window approach combined with thresholding algorithm was used for analyzing the 2018 flood event of Kerala, India. The SAR image splitting and tile selection was carried out based on two parameters, namely the Coefficient of Variation (CV) and ratio to the scene. Kittler and Illingworth's thresholding algorithm was implemented on the selected split images. Euclidean distance was used to shortlist the split images with large variation in the data representing both thematic classes (flood/non-flood). An independent split based analysis (ISBA) was implemented in which respective threshold values obtained from the split images are averaged to get an optimum threshold value. From the results, we observe that an underestimation of flood area in urban land use due to double bounce, volume scattering and shadow effects. Validation is carried out on a small subset area for which the field data was available, and an accuracy of 73 % was obtained.

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR data [compact-pol radar vegetation index (CpRVI)]. The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an ideal depolarizer (a realization of vegetation canopy). The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. In this article, we utilize time-series-simulated RADARSAT Constellation Mission (RCM) compact-pol SAR data (RH-RV) obtained from the full-pol RADARSAT-2 observations during the soil moisture active passive (SMAP) validation experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, to assess the proposed vegetation index. Among the various crops grown in this region, in particular, we analyze the growth stages of wheat and soybean due to their different canopy structures. A temporal analysis of the proposed CpRVI with crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages confirms the trend of CpRVI with the plant growth. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. Also, the linear regression analysis confirms that the CpRVI values significantly correlate with PAI (r = 0.72 and 0.85) and VWC (r = 0.62 and 0.75) for both wheat and soybean. We observed good retrieval of PAI and VWC for both wheat and soybean.

In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI) using a geodesic distance between two Kennaugh matrices projected on a unit sphere, as given in Ratha et. al. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an isotropic depolarizer. The proposed vegetation index is compared with the Radar Vegetation Index (RVI) obtained from RADARSAT-2 full-polarimetric SAR data. We use a time series of simulated compact-pol SAR data (RH-RV) obtained from the RADARSAT-2 data acquired during the SMAPVEX16-MB campaign over the Joint Experiment for Crop Assessment and Monitoring (JECAM) test site in Manitoba, Canada to assess the proposed vegetation index. Among the various crops grown in this region, only the growth stages of soybean are analyzed in this work. The temporal trend of CpRVI follows the growth stages of soybean. Regression analysis shows that CpRVI correlates better with the Plant Area Index (PAI) and Vegetation Water Content (VWC) than RVI.

In this paper, the height accuracy of various DEMs such as TanDEM-X Global DEM, newly released TanDEM-X 90 m DEM, TanDEM-X ascending or descending DEM, SRTM 30 m, 90 m DEMs, provisional version of the NASADEM, and the aerial LiDAR DEM are assessed over three different Indian terrains. The test sites deal with flat terrain, flat terrain with forest cover and hilly terrain conditions. The accuracy is assessed by computing statistics of the elevation difference between the reference data (ICESat or GPS) and other DEMs. The RMSE for TanDEM-X Global DEMs is less than the mission specification of 10 m. The RMSE over flat terrain (Bihar) is between 1.43 and 5.34 m. For KWLS (flat forested terrain) it ranges from 5.16 to 7.54 m, and for the rugged Himalayan test site Manali the RMSE ranges from 7.41 to 17.15 m. The provisional version of the NASADEM shows better vertical accuracy than the existing SRTM DEMs.

Chennai, the capital city of Tamil Nadu state, India experienced a major disastrous flood during Nov-Dec 2015. The city is characterized by mixed land use with high built-up density. The freely available C-band Sentinel-1 temporal GRDH SAR images are used to analyze this flood event. We used the co-polarized (VV) SAR images for mapping the flood area. A change detection technique (Normalized Change Index (NCI)) which uses a pre-flood image having the same image characteristics as the flood/post-flood images is implemented on the temporal SAR images. An automatic flood mapping approach, i.e. NCI combined with thresholding is used to identify the flood area. This approach has underestimated the flood area for this particular study area which is due to the use of moderate resolution SAR images for high-density urban flood mapping. Despite underestimation, Sentinel-1 SAR images are very useful to get the first insight of flood condition in the high density urban areas.

Compact polarimetry (CP) offers a tradeoff with fully polarimetric modes in terms of swath width, power budget, and polarimetric information content. In this letter, a classification comparison is made among real CP, simulated CP (SCP), and quad polarimetric (QP) data acquired from the L-band SAR system onboard the ALOS-2 satellite. The Wishart supervised classification scheme is used to compare data modes over two regions of a mixed test site in India. The quantitative classification assessment indicates that the QP data have higher classification accuracy than any other polarimetric combinations for both regions. The comparative classification accuracy of real versus SCP data is different for the two regions. The overall accuracy of the real CP data is slightly higher ~1% than SCP for region 1, which is dominated by urban and rice classes, whereas it is lower by ~9% for the agricultural crop dominated region 2.

Soil moisture measurement using L-band radiometry is now widely accepted as the state-of-art remote sensing approach, and has been adopted by both the SMOS and SMAP soil moisture dedicated satellite missions. However, it suffers from the shallow depth of its soil moisture measurement, and the confounding effects of vegetation and soil roughness on soil moisture retrieval. P-band, which is a longer wavelength measurement, provides the potential to retrieve deeper soil moisture information, and to do so more accurately due to reduced soil roughness and vegetation effects. This paper presents some pioneering work on the use of P-band for soil moisture retrieval. The Polarimetric P-band Multibeam Radiometer (PPMR) used in this research operates at 740 MHz / wavelength of 40 cm. It is used together with the Polarimetric L-band Multibeam Radiometer (PLMR) which operates at 1.4 GHz / wavelength of 21 cm. The PPMR and PLMR are mounted onto a 10m high tower in an agricultural farm located at Cora Lynn, Victoria. This paper outlines the initial set up for the study and the experimental plan for understanding PPMR's performance, along with some initial data.

In this paper, a multi-target inversion scheme is adopted for joint estimation of crop biophysical parameters from dual-pol SAR data. The single-output support vector regression (SVR) method is extended to a multi-output support vector regression (MSVR) method to estimate biophysical parameters. The MSVR is implemented for simultaneous retrieval of plant area index (PAI) and crop biomass from the Sentinel-l C-band dual-pol (VV + VH) data. In this particular study, the inversion algorithm is trained and validated for the canola crop using in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign. The validation results indicate a good correlation coefficient (r) of 0.72 and 0.85, with a RMSE of 0.35 m2m-2and 0.48 kgm-2for PAI and wet biomass respectively. In addition, the mapped PAI and wet biomass values at flowering stage of canola capture the variability in crop growth from Sentinel-l data.

Long-term ground surface displacements of seismically active Bhuj area in India between two time spans are derived from L-band ALOS-1 and ALOS-2 images using Persistent Scatterer Interferometry approach. The work also focuses on studying the influence of high resolution TanDEM-X Global DEM on topographic phase removal and it's effect on deformation estimates. The results over the 2001 Bhuj earthquake epicentral region indicate minor deformation rates in the range of ±20 mm year-1. Interpretation of results revealed new subsidence areas between 2014 and 2017.

Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sentinel-1 images is developed with the Google Earth Engine's cloud computing platform. A dense time-series analysis of backscatter response of rice with different management practices is analyzed. Subsequently, the early and late transplanted rice is classified using a clustering algorithm within this platform. The proposed approach is used to monitor different cultivars of rice in three districts in the state of West Bengal, which is one of the major rice growing regions in India. The classification accuracy is assessed across 150 validation points spanning multiple blocks for the 2017 monsoon season. The Sentinel-1 SAR images acquired up to the early vegetative stage for rice have provided satisfactory classification accuracy with an overall accuracy >85% with κ ~ 0.86 across different management practices throughout the region.

This paper compares the classification capability of data acquired in hybrid and dual linear polarization mode over the Chelmsford area, United Kingdom, from RISAT-1 C-band satellite. Support vector machine based supervised classification is used in the study and accuracy is assessed over the validation pixels. The hybrid-pol RH/RV combination shows better classification accuracy over linear pol HH/HV combination by 2.5 %. Overall classification accuracy is increased to 90% over the given area when Stokes parameter are added to the polarization combination.

The target decomposition techniques give more information about scattering mechanism than obtained through covariance or coherency matrix. In this paper, the effect of various parameters of Touzi decomposition on classification accuracy is studied. The work shows that out of many Touzi parameters, the first components of α, Φ, λ, τ along with span can effectively classify various land features. The influence of helicity is more prevalent at L-band compared to C-band. The effect of different non-parametric classifiers on classification accuracy is also studied.

In this paper, the accuracy of TanDEM-X DEMs is evaluated for different terrains of India and is also compared with that of Cartosat-1 optical stereo DEMs. Using accurate GPS points as reference, TanDEM-X DEMs of Mumbai area with flat as well as low hilly terrain, Koyna area with high hilly terrain, rugged Himalayan terrain of Manali and Katerniaghat area with forest cover over flat terrain were evaluated. The results show an RMSE of 3.3 m, 4.9 m, 12.8 m and 8.7 m for the four test areas respectively. Cartosat-1 DEM over these areas give an RMSE of 4.8 m, 7.9 m, 11.4 m and 8.7 m respectively. The height error also shows its dependence on slope. The difference between the DEMs obtained from the two techniques was also calculated for all the test sites in order to compare their accuracies. For all the test sites except Manali, an RMSE <; 4 m with 90% confidence level was observed between the two DEMs. The rugged terrain of Manali area is highly affected due to layover and shadow effect in TanDEM-X DEM and hence showed higher RMSE when compared with Cartosat-1 DEM. As both DEMs have spatial resolution and high accuracy, gaps in the TanDEM-X DEM may be filled with Cartosat-1 DEM.

The objective of this paper study is to understand the land use land cover type information using C-band RADARSAT-2 data in quad pol and simulated hybrid pol mode. A quad-pol RADARSAT-2 image was used to simulate right circular hybrid polarimetric synthetic aperture radar SAR data. The area under rice cultivation shows dominance of double-bounce scattering, while cotton and banana crops shows dominance of surface scattering in the quad as well as hybrid pol mode. The correlation coefficient is also assessed among different full and compact polarimetric descriptors. The overall investigation shows the comparative strength of hybrid polarimetry for target discrimination and scattering mechanism analysis.

The applicability of a recently developed compact polarimetric decomposition and inversion algorithm for C-band to estimate soil moisture under growing agricultural vegetation cover is investigated using simulated L-band compact Polarimetric Synthetic Aperture Radar (PolSAR) data. The surface scattering component is separated from the volume scattering component through a model-based compact polarimetric decomposition under the assumption of a randomly oriented vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physically-based, low frequency surface scattering models such as Extended Bragg (X-Bragg) and Polarimetric Two Scale Model (PTSM). The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR 2006 campaign over the Görmin test site in Germany. The compact PolSAR derived soil moisture is validated against in situ measurements. Including the entire growing season and three different crop types, the estimated soil moisture values indicate an overall RMSE of 9-12 vol.% and 9-15 vol.% using the X-Bragg and PTSM, respectively.

Synchronous with RISAT-1 and RADARSAT-2 passes, corner reflectors (CR) were mounted for the evaluation of radiometric calibration of RISAT-1 compact polarimetric SAR data. Based on the CRs response on SLC images, RISAT-1 calibration constant, PSLR/ISLR, resolution, channel imbalance and cross-talk were analyzed and compared with that of product specifications. A difference of 0.5 to 1.5 dB is observed between product and estimated calibration constant K. The resolution and PSLR/ISLR are better than the specification of the product. Backscattering coefficient and compact polarimetric parameters of RISAT-1 were compared with RADARSAT-2 simulated compact polarization. The large difference in sigma-0 is observed for water and sand features due to high value of noise-equivalent-sigma-zero of RISAT-1. Similarly, some differences are observed between RISAT-1 estimated compact polarimetric parameters (m, mc, δ, χ, axial ratio, CPR) and that of RADARSAT-2 simulated compact data.

Soil moisture is one of the important variables in hydrological systems. Spatial and temporal dynamics of soil moisture are important for determining complex environmental process. The aim of the study is to assess the trend of soil moisture in India and to monitor agricultural drought in Medak district of Telangana state during the period 2002 to 2014. Spatio-temporal analysis has been performed on yearly basis using AMSR-E (2002-2010) and SMOS (2010-2014) soil moisture data. Soil Moisture Anomaly Percentage Index (SMAPI) is used for performing spatio-temporal analysis. It has been observed that soil moisture trend shows a dynamic pattern across the country and positive correlation with the precipitation data. Soil moisture anomalies are more in North-Western and North-Eastern part India when compared to other parts. Departure of soil moisture from its mean is used for monitoring drought. Agricultural drought monitoring in Medak district shows that the abnormal pattern of dry spell areas during a particular year matches with that of the drought years.

In this paper, the capabilities of hybrid polarimetric synthetic aperture radar are investigated to estimate soil moisture on bare and vegetated agricultural soils. A new methodology based on a compact polarimetric decomposition, together with a surface component inversion, is developed to retrieve surface soil moisture. A model-based compact decomposition technique is applied to obtain the surface scattering component under the assumption of a randomly oriented vegetation volume. After vegetation removal, the surface scattering component is inverted for soil moisture (under vegetation) by comparison with a surface component modeled by two physics-based scattering models: The integral equation method (IEM) and the extended Bragg model (X-Bragg). The developed algorithm, based on a two-layer (random volume over ground) scattering model, is applied on a time series of hybrid polarimetric C-band RISAT-1 right circular transmit linear receive data acquired from April to October 2014 over the Wallerfing test site in Lower Bavaria, Germany. The retrieved soil moisture is validated against in situ frequency-domain reflectometry measurements. Including the entire growing season (all acquired dates) and all crop types, the estimated soil moisture values indicate an overall rmse of 7 vol.% using the X-Bragg model and 10 vol.% using the IEM model. The proposed hybrid polarimetric soil-moisture inversion algorithm works well for bare soils (rmse = 3.1-8.9 vol.%) with inversion rates of around 30-70%. The inversion rate for vegetation-covered soils ranges from 5% to 40%, including all phenological stages of the crops and different soil moisture conditions.

The objective of this study is to understand the temporal characteristic of cotton, sugarcane, banana and rice crop using C-band quad pol RADARSAT-2 data. Temporal response of these crops was analyzed using H/A/α and model based Freeman-Durden decomposition. Results of this preliminary study indicate that the (Entropy, anisotropy α and α1) parameters are sensitive to crop growth stages and can be used for crop mapping and monitoring purpose. The three basic scattering mechanisms (surface, volume and double-bounce) and H/A/α parameters obtained from these decompositions found suitable to distinguish between crop types and their temporal sensitivity.

The capability of hybrid Polarimetric Synthetic Aperture Radar (PolSAR) to estimate soil moisture (under vegetation) was assessed within the agricultural region of Wallerfing in Lower Bavaria, Germany. A novel methodology based on a hybrid polarimetric decomposition together with a surface component inversion is developed to retrieve surface soil moisture. The model-based, hybrid decomposition technique is used assuming a randomly oriented vegetation volume to obtain the surface scattering component. After vegetation removal, the surface scattering component is inverted for soil moisture by comparison with the surface scattering component, modeled by the Extended Bragg (X-Bragg) model including a depolarization term. The developed algorithm is applied on multi-temporal hybrid polarimetric C-band RISAT-1 data acquired from April to October 2014. The estimated soil moisture values indicate an overall Root Mean Square (RMS) error of 6.2 to 8.5 vol.% accounting for the entire growing season and four different plant types.

This paper deals with the fusion of TanDEM-X raw DEMs in ascending and descending pass over Mumbai test area and enhance its quality. Before applying fusion method, a robust layover and shadow map has been calculated in ITP using TanDEM-X DEM and the corresponding slant range image. The selection of optimum weights for fusion has been based on height error map calculated from interferometric coherence. Results show a substantial reduction in number of invalid pixels after fusion. In the fused DEM, invalid is only 1.2%, while ascending and descending pass DEMs have 6.7% and 5.7% respectively. The improvement in accuracy of the DEM is very slight in this case which is due to the coarse resolution of the SRTM DEM used as reference.

In flooded elevated regions, mapping and differentiating the shadow-layover pixels of input SAR image from the overall inundated region is a major challenge which is not addressed in earlier studies. This paper will brings out the details about a DEM based SAR image analyzing techniques that can help in identifying the pixels of shadow/layover regions of input SAR image. The outcome of the proposed technique have been verified with eight different RADARSAT-2 SAR scenes which have been acquired during various flood events of India and the results of one of the image has been discussed in this paper.

Recently launched TanDEM-X SAR mission (June, 2010) aims to generate a consistent global DEM equaling HRTI-3 specification. In view of global DEM generation using TanDEM-X InSAR data, it is very important to evaluate their accuracy over various test areas. This paper presents the evaluation of DEM generated using interferometric technique from TanDEM-X data in and around Mumbai city in India. The DEM was also compared with the DEM generated using Cartosat-1 stereo optical data having equivalent resolution. On comparison of the results obtained from the two techniques with the accurate DGPS values, it is observed that TanDEM-X DEM has a RMSE value of 8 m and is superior to Cartosat DEM which gives 15 m RMSE value for this test site. The standard deviation of the uncertainty of the elevation value given by TanDEM-X DEM and Cartosat DEM is 7.9 m and 9.7m respectively. More data sets with varying elevation are needed for the evaluation of TanDEM-X data by combining ascending and descending passes.

Speckle has a nature of multiplicative noise which is difficult to deal as compared to additive noise. It complicates the problem of interpretation of the image segmentation and classification. The primary goal of existing speckle filtering algorithms, which are subjective in nature is to reduce the speckle without loss of information. Various techniques have been proposed to suppress the speckle. In this paper we propose Self-Organizing Feature Map (SOFM) based polarimetric SAR speckle filter. The filter is evaluated using fully polarimetric ALOSPALSAR and Radarsat-2 data imaged over Mumbai, India. Quantitative and qualitative results revels that SOFM based approach is effective in terms of bias and speckle reduction.

The launch of RISAT-1 Indian remote sensing satellite on 26thApril 2012, made it possible to collect hybrid polarimetric data from a space-borne sensor. The RISAT-1 C-band compact polarimetry data acquired over Mumbai is analyzed and assessed for classification of various land features and also compared with other fully polarimetric spaceborene SAR data sets. For better comparison, RISAT-1 C-band and RADARSAT-2 C-band simulated compact polarimetric data is classified and compared.

This paper is an attempt to help the beginners and the students to learn the basics of the SAR signal processing in a simple and easy way using MATLAB. The signal processing of SAR data is very important for generating various data products for different applications and analysis of land features. The SAR raw data for signal processing is a two dimensional array which contain sampled echoes in the complex form. Pulse compression techniques are used to compress energy of echoes and thereby increasing the resolution of SAR. To obtain the final image the range compression, Doppler centroid frequency estimation, range cell migration correction, azimuth compression etc. are implemented in such a way that any user can understand very easily. Advantage of the developed code in MATLAB is that it does not need a vast programming knowledge to be able to customize it. The developed code will be available to all users for educational and training purposes.

ENVISAT ASAR images acquired during 2003-2007 were processed for landslide movement using PSInSAR and SBAS methods over Himalayan region. The mean velocity of the landslide movement obtained with ascending and descending pass data using PSInSAR is around ± 16 mm/yr and SBAS method gives -25 to 15 mm/yr with descending pass and -39 to 38 mm/yr with ascending pass data sets. The number of PS points found with SBAS method is much less than that of obtained using PSInSAR. The effectiveness of the result shows highest movement in steeply sloped region.

Polarimetric Synthetic Aperture Radar (PolSAR) data is available at different frequencies and polarizations from various sensors like ALOS-PALSAR, Envisat ASAR, TerraSAR-X. This study compares the classification accuracies obtained with fully polarimetric and dual-polarization L-band ALOS-PALSAR data over Mumbai and Sundarban area. We have also compared dual polarized ALOS-PALSAR L-band TerraSAR-X X-band and Envisat C-band SAR data acquired over Mumbai area. IRS-P6 optical data over the same area has been used to compare the classification accuracy between optical and SAR data. The change in accuracy due to the phase information of SAR data is also assessed by comparing the classified results of intensity and complex images for all the possible polarization combinations such as (HH, HV), (HH, W) and (HV, VV) at L-band. The results show that the fully polarimetric mode gives maximum classification accuracy (above 95%). It is observed that among dual polarized data, complex (HH, VV) combination gives the maximum (above 92%) accuracy.

Glacier retreat and advance is general phenomenon in Himalayan region due to change in climatic conditions. For quantifying the global warming effects on local scale glacier system monitoring and estimating its dynamic geophysical parameters viz. movement and volume change are important. In this study glacier movement estimation is attempted in north western Himalayan region using spaceborne InSAR technique, which is based on preserving the coherence between two acquisitions of the same scene. It is observed that ERS-1/2 tandem data give high correlation over Gangotri glacier and Siachen glacier and movement in LOS direction is deciphered. However, with the use of SAR Speckle tracking method two dimensional (Azimuth and range directions) velocities of glaciers can be obtained. In this study attempt has been made to measure 2-D velocity components of Gangotri glacier. New generation TerraSAR-X (TS-X) high resolution spotlight mode (HS), single look slant range complex (SSCs) data of 28thAugust, 2008, 08thSeptember, 2008, 19th September, 2008 and 30thSeptember, 2008 are exploited for this study. Interferogram, coherence image and intensity image are generated. It is observed that Interferometric SSC data of 28thAugust, 2008 and 08th September, 2008 give some fringes outside glacier area and complete decorrelation is shown on the Gangotri glacier due to high movement of glacier.

Aqua AMSR-E L3-daily soil moisture data of 7 years (2002-2008) were analyzed to know the soil moisture pattern over entire India and also some specific areas with limited ground-truth. The data were also used for observing change in daily soil moisture over some parts of Gujarat and Rajasthan. By comparing estimated soil moisture and rainfall data we found that the estimated soil moisture varies well with rainfall for uniform bare fields. The results indicate that the soil moisture can be used for drought monitoring and flood area estimation with less accuracy.

Polarimetric SAR data from ALOS PALSAR, SIR-C and ENVISAT ASAR over wet and arid regions were processed for classification and soil moisture estimation. HH and HV dual polarized ALOS PALSAR could classify wetlands of Mumbai coastal area with an accuracy of 96%, whereas fully polarized SIR-C data over Kolkata gave 92% accuracy. The accuracies are based on selected training areas and not based on test areas. ALOS PALSAR could clearly discriminate water, mangrove forest and ocean water. With Dual polarized data, discrimination between Ocean water and wetlands is not possible. Several features in arid data can also be classified using PALSAR data in addition to the estimation of soil moisture.

An endeavor is made for the study of movement of Himalayan glaciers using Spaceborne InSAR technique, which is based on preserving the coherence between two acquisitions of the same scene. Gangotri, Siachen, Bara Shigri and Patsio are major glaciers in the Himalayan region, which are showing retreat, and their respective tributary glaciers completely disconnected from main body of glaciers. ERS-1/2 observations show high correlation on glacier area and hence movement of Siachen and Gangotri glacier are measured. Information about dynamism of glaciated terrain can be retrieved by counting differential interferograms. Displacement of Gangotri glacier in the radar look direction has been observed as 8.4 cm represented by 3 fringes. Siachen glacier exhibits a displacement of 22 cm represented by 8 fringes. ERS-1/2 tandem data over all these glaciers show highest correlation over glacier areas but ENVISAT ASAR data shows coherence loss over glacier area due to decorrelation. Coherence loss is usual phenomena in glaciated terrain as repeativity of sensor is high (35 days for ENVISAT). Among all these, Siachen glacier shows highest coherence and then Gangotri, respectively. A tandem pair of ERS-1&2 acquired on April 1 and 2, 1996 in descending pass over Siachen shows high coherence than the ascending pair acquired on May 2 and 3, 1996. It is due to change in climate between two acquisitions at glacier locations. A systematic monitoring of dynamism of Himalayan glaciers can be done using Permanent Scatterer Interferometric SAR (PSInSAR) if we place a corner reflector on the glaciers.

This study discusses the capability of full polarimetric L-band ALOS PALSAR data for snow classification. In this study, Polarimetric decomposition and the complex Wishart classifier are applied on ALOS-PALSAR data. Optical (ALOS-AVNIR) data within six days difference was used for visual interpretation of snow and non-snow classes. Application of Entropy-Anistropy-Alpha-Wishart classifier for training samples gives better classification results. To reduce speckle effects and to improve classification results, the refined Lee filter was applied on the covariance matrix several times, each time increasing the size of the moving window. Over all classification accuracy were observed 75.61%, 91.46%, 94.91%, 96.19%, 97.16% and 98.37% using different window sizes 1times1 (without filtered image), 3times3, 5times5, 7times7, 9times9 and 11times11 respectively for refined lee speckle filter. It is observed that classification accuracy increases as size of the filter window increases for speckle reduction. Polarization signatures of various features have also been generated using polarization synthesis techniques and signatures are represented in 3-D plot.

Snow cover area is a very important parameter for snowmelt runoff modeling and forecasting. Snow cover information is also useful for managing transportation and avalanche forecasting. Our study area includes Gangotri glacier region, Siachen glacier region and Beaskund glacier region in the North-West Himalayas of India. Our previous studies discuss the capability of several algorithms for optical sensor as well as SAR to map the snow cover area in Himalayan regions. In recent years, the SAR interferometry has provided number of attractive applications in landuse/landcover mapping. This study discusses the capability of both backscattering ratio techniques and InSAR coherence measurement techniques for snow cover mapping in Himalayan region with repeat passes data of ERSfrac12 and ENVISAT-ASAR. By analyzing the several pairs of ENVISAT repeat passes ASAR images for the study area, we find that the coherence measurement from bare soil, bare rock and vegetation are high and snow covered area and glacier area have very low coherence except in one day difference image.

We have made an endeavor to investigate the snow water equivalent (SWE) variations in Himalayan mountain region which is the most difficult terrain to access during winter seasons. The area also covers the large glaciers such as Siachen and Gangotri. A time series multi scale of SWE L3 product derived from Aqua Advanced Microwave Scanning Radiometer (AMSR-E) data have been analysed for three consecutive winters during 2002 -2005 for Himalayan Snow cover region. A major emphasis is on the study of SWE trend in the two glacier areas viz. Siachen and Gangotri. It has been observed through AVISR- E 5-day product that snow cover area (SCA) over whole Himalayan region is very dynamic. AVISR-E derived 5-day SWE is analysed at the two test sites viz. Patsio (Lat 32deg 45 17.89" N and Lon 77deg, 15', 43.13"E) and Dhundi (Lat 32deg, 22, 05"N and Lon 77deg, 15 E) concurrently with the available in-situ data .The result indicates that in the peak winter days only we found reasonable co-relation. At the Patsio, minimum and maximum SWE observed are 26 mm and 108 mm respectively using in situ data.

Two famous glaciersviz.Gangotri and Siachen were studied for DEM generation and movement using ERS-1&2 tandem data. While surrounding areas along the glaciers showed more decorrelation, glacier area showed a very good correlation between two image acquisitions. Contours obtained using ERS-1&2 SAR tandem data closely match with topographic maps of the area. Two-pass differential method was used along with SRTM DEM to study the movement of the glaciers. According to the differential interferogram over Gangotri, 3 fringes equivalent to 8.4 cm displacement in the radar look direction were observed, whereas for Siachen the fringes were about 8 which is equivalent to 22 cm. The estimated DEMs and movements are to be verified using GPS measurements

Optical remote sensing data and microwave remote sensing data are complementary to each other and hence the fusion of these data would help in improving the classification accuracy. In this paper IRS LISS-III data and Radarsat-1 SAR data are fused using Bayesian formulation of data fusion. For this purpose SAR image is modeled using multiplicative autoregressive random field model. The synthesized SAR image is fused with IRS LISS-III image using a model, which incorporates transition probability to give allowance for temporal ambiguity. Fusion technique is used to improve the classification accuracy of snow related features in Himalayan region, India.