RISAT-1 SAR, the first indigenous active SAR sensor launched by India, has shown its potential in several natural resource monitoring and disaster-related applications. Two-dimensional signature and linear decision rule classification approach, implemented in in-house developed Microwave Data Analysis Software (MIDAS), was used to process and generate snow/ice facies information using RISAT-1 MRS data over the Himalayan-Karakoram (H-K) region. A criterion of glacier area more than 8 km2was adopted to minimize topographic effects due to oblique sensor viewing geometry. A total of 1352 RISAT-1 MRS images between 2012 and 2016 were picked up covering almost 1300 glaciers in H-K region. Sigma-nought (σ0) images, DEM and outline of glaciers were used for generating snow/ice facies products. Processes in formation of snow and ice facies based on SAR interactions has been discussed during accumulation and ablation months of a year for respective state of snow/ice. ERA-5 reanalysis temperature data synchronous to SAR derived facies products have shown a high correlation between Wet Snow Zone (WSZ) and Seasonal Frozen Percolation Zone (SFPZ) with atmospheric temperature at 2 m respectively covering glaciers in H-K region. Detailed analysis was also carried out in view of Longitudinal, Latitudinal, mean elevation, and mean air temperature respectively for individual glacier, showing variation in SFPZ and WSZ class as one move from Western to Central and further in Eastern Himalayas. This work explicates retrieval and analysis of long-term database as a continuing activity for forthcoming RISAT-1 series and NISAR mission to understand its linkages with ever-changing climate.

Nonlinear Unmixing using the Band-wise Generalized Bilinear Mixing (NU-BGBM) model specifies an acceptable mixing scenario up to the second-order interaction of light rays and also suppresses various types of mixed noise while performing un-mixing. However, NU-BGBM requires high computational time and multiple parameter tuning, which could practically limit its application to large HyperSpectral Images (HSIs). In this context, we propose a computationally efficient BGBM as a fast and robust variant of the NU-BGBM method, In this model, the objective function for the non-linear optimization scheme is designed without the sparsity constraint, and an iterative scheme based on the Alternating Direction Method of Multipliers (ADMM) is formulated for solving the proposed model. Extensive analyses have been carried out on synthetic (with simulated mixed noise) and real HSIs. The performance of the proposed method was compared with the NU-BGBM model using signal-to-reconstruction error (SRE), abundance Root-Mean-Square Error (aRMSE), source Root-Mean-Square Error (sRMSE), and Root-Sum-Squared (RSS) error. Results from extensive numerical analysis reveal that the proposed method reduces computation time (on an average six times faster) while being comparable (and often better) than NU-BGBM in terms of accuracy on large data sets.

We propose an unsupervised methodology for multi-class change detection (CD) in multimodal remote sensing data fused using the Kronecker product formalism. The method utilizes the compressed change vector analysis (C2VA) on the fully vectorized change matrices. The multimodal case is demonstrated using dual-frequency full-polarimetric Syn-thetic Aperture Radar (SAR) data obtained by EMISAR over the Foulum agricultural area. The change types are inves-tigated using ground truth data for the growth of various crops. The work showcases the capability of the Kronecker product-based CD formalism beyond conventional scalar change indices.

Spectral signatures of the pure constituent materials vary across the hyperspectral image (HSI) due to variable illumination, atmospheric conditions, and intrinsic variability. Using a single endmember to represent the target material (or endmember) with high spectral variability will lead to errors in estimating abundance. Therefore, we propose a probabilistic generative network (PGM-Net) architecture to learn the spectral variability from the HSI (hereinafter referred to as endmember-guided-probabilistic-model-network, EGPGM-Net). The PGM-Net is guided by endmember-network (E-Net) using the parameter sharing strategy. Experimental analysis was carried out on benchmark datasets to compare the performance of the proposed method with the state-of-the-art methods. Moreover, we have also demonstrated the application of EGPGM-Net for estimating the abundance of red and black soil over sparsely vegetated areas using airborne-visible-and -infrared -imaging-spectrometer-next-generation (AVIRISNG) sensor. The quantitative analysis reveals that the proposed method consistently achieves a better unmixing performance than other linear-mixing and deep learning based models in terms of spectral-angle-distance (SAD) and abunance-root-mean-square error (aRMSE). The proposed semi-supervised approach accurately delineated the abundances of red soil, black soil, crop residue, built-up areas and bituminous roads.

This letter aims to develop an inversion model to estimate soil moisture using Navigation with Indian Constellation (NavIC) L-band signal. Several research works suggest that microwave signal property gets affected after reflecting from the soil surface. The nature of the reflected microwave signal depends on the signal’s penetration depth, which is the factor of water content present in the soil surface. NavIC multipath signal can be used for the estimation of soil moisture using this property. The carrier to noise ratio$(C/N_{\mathrm {o}})$of NavIC signal is used for this purpose. The model proposed in the letter is based on the relationship of estimated multipath phase value with the volumetric moisture content present in the soil surface. The output of the developed model is highly encouraging. A linear relation relationship with a high correlation coefficient value of 0.902 and root mean square error (RMSE) of 4.03% is obtained between ground truth soil moisture and retrieved soil moisture from developed algorithm.

In recent time, Hyperspectral(HS) and multispectral(MS) data fusion based on spectral unmixing methods has become an active area of research. Coupled non-negative matrix factorization (CNMF), one among many unmixing-based data fusion approaches, performs alternating unmixing of the HS and MS data while connecting the results by point spread function and spectral response function of the sensors. However, CNMF operates exclusively on the spectral information of the HS and MS data and also disregards the spatial distribution of the data. In this paper, we propose an extended linear mixing model approach to enhance the spatial resolution of HS data. The proposed method extends the commonly used linear mixing model for data fusion by introducing an additional term that accounts for the non-linearity effects as well. The results of the simulation obtained from the analysis on various synthesized datasets suggest that the proposed method can significantly improve the Peak signal-to-noise ratio (PSNR), minimize the relative dimensionless global error (ERGAS) of fusion, and also competes with state-of-the-art approaches.

Progression towards the different scattering models has fulfilled some of the awaited requirements in the physical model-based scattering power decomposition techniques. Among these, the complete utilization of terrain information has recently been achieved by the development of helix and compounded scattering components. However, the achievement comes up with the expense of some adverse effect on the decomposition result. As occasionally, the power associated with any particular component gets overestimated. Apart from that, an enhancement in the percentage of negative scattering pixels has also been observed as the advancement propagates. Paper briefly concentrated on the analysis of decomposed scattering power and negative scattering pixels on the ALOS PALSAR 2 datasets over the San Francisco Bay Area, CA, USA, when being implemented under the different decomposition strategies.

The exploitation of GNSS signals for soil moisture as one of the land applications is the current interest of researchers due to its multiple advantages over existing soil moisture retrieval techniques based on traditional radiometer and other datasets. Multipath phase and amplitude of GNSS C/Nodata have been mostly utilized to determine the sensitivity of soil moisture. However, in this work, we have analyzed the multipath peak frequency to determine its sensitivity for field soil moisture. The multipath frequency is used to consider constant when studies are carried out with multipath phase or amplitude. Here, we have demonstrated that the multipath peak frequency is a function of field soil moisture which can be evaluated efficiently with Lomb Scargle Periodogram (LSP). Navigation with Indian Constellation (NavIC) data has been used to determine the correlation between multipath peak frequency and surface soil moisture. The obtained sensitivity results are very optimistic (correlation coefficient = 0.69), which can be further utilized for developing soil moisture estimation model using NavIC data to cater different land applications.

The possibility of using high resolution SCATSAT-1 data for studying ice calving events in Antarctica has been explored in this study. Two recent calving events in the ice shelves of Amery (2019) and Larsen D (2020) have been observed. These gave birth to icebergs D-28 and A-69 respectively. Enhanced resolution level-4 horizontally polarized daily gamma-0 measurements are used. Canny edge detection technique has been employed to observe these events. The results obtained from the edge detection have been compared with Sentinel-1A Level-1 Ground Range Detected datasets and are found to be in good agreement. A mean difference of 0.2 km (± 3.7 km) is obtained between the two ice front edges (derived and actual).

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m3/m3during pre-monsoon and 0.65 and 0.053 m3/m3in post monsoon periods, respectively.

Enormous volumes of data made available by the high-resolution satellite imagery enable us to use a deep framework in the field of remote sensing for image classification. Recently, deep learning has been an area of interest for the researchers in the computer vision domain due to its high efficiency toward large-scale, high-dimensional data. In this letter, we propose an unsupervised learning algorithm to cluster hybrid polarimetric SAR images, and dual-polarized SAR images using the deep framework. We use feature extraction layers of the VGG16 model with batch normalization, which is trained with small patches derived from the hybrid polarimetric SAR images. It uses an entropy-based loss function and an adaptive learning rate optimization algorithm, Adam, for training. Broadly, the patches are segmented into three classes, namely, surface, volume, and double-bounce, which are defined with reference to the SAR scattering characteristics. Furthermore, we classify volume into dense forest region and agricultural crop fields. We also observe mixed classes between volume and double-bounce, mainly covering the settlements surrounded by areas covered by tall trees. Furthermore, we use transfer learning for generating the labels for dual-polarized images by using the learned weights of a hybrid polarized image model. Such a technique renders an average accuracy of 89.70% and 86.08% for hybrid polarized SAR images and dual-polarized SAR images, respectively. Hence, this method explores the spatial characteristics of remotely sensed images to distinguish urban settlements, water bodies, agricultural, and forest areas from the underlying scene in an unsupervised fashion.

Deep learning has proved to be highly efficient towards large scale, high dimensional data, rendering it to be an area of interest for researchers. Enormous volumes of satellite imagery enables us to utilise the benefits of a deep framework in the field of remote sensing. In this paper, we propose an unsupervised patch based learning method to cluster hybrid polarimetric SAR images. We extract small patches from the image data set, and train VGG16 model with batch normalization using an entropy based loss function. Initially, the patches are segmented into three classes, namely, surface, volume, and double-bounce, which are defined with reference to the SAR scattering characteristics. We further classify volume into dense vegetation, and agricultural areas. Mixed classes, mainly covering the areas which have settlements surrounded by tall trees, are also observed. This technique gives an average accuracy of 89.70%.

In this paper, we propose to distinguish forest-cover in an unsupervised fashion by a combination of passive multi-spectral imagery and active hybrid polarized SAR data. At first, multi-spectral imagery (MSI) is used to separate general vegetation region (e.g., forest, mature grassland, and pre-harvest agricultural fields) from the imaged scene using spectral slopes based rules and support vector machine technique. Then, hybrid polarimetric SAR image of the same region (acquired with a common time stamp) is clustered into three scatter classes, namely, surface, volume, and dihedral, using Stokes parameters based m - δ decomposition. Forest cover is extracted by bi-labeled pixels of the study site that correspond to vegetation (in MSI) and volume scatter (in SAR), which forms a community level classification of forest region. Further, using Wishart derived mean-shift clustering technique, we segregate possible categories of forest clusters within the mapped forest region to obtain a sub-community level classification. Discernible spectral and scattering characteristics of remotely sensed images are explored in our work for identifying forest regions and their possible categories. The proposed method is automated by freeing the manual supervision in selecting seed pixels for training any machine learning technique.

Differential Synthetic Aperture Radar Interferometry (DIn-SAR) can be used for observing and monitoring land surface change over a large area using multi-temporal SAR images. To remove signal decorrelation issues of DInSAR, Persistent Scatterer InSAR (PSInSAR) has come into practice over the last decade that can measure deformation at small scales and fine accuracy (mm-level). In this paper, we estimate ground deformation of Kolkata, India during 2007-2011 using a spatial correlation based PSInSAR method. Twenty L-band (ALOS PALSAR) SAR images along ascending orbit over Kolkata city were utilised to extract deformation time series, and a validation exercise was carried out using groundwater level data of two wells. PSInSAR extracted a large number of measurement points (363 per km2for a total 568014 points), and we found a mean annual deformation rate of -16 to +16 mm/year in Kolkata area along Line-Of-Sight (LOS) of ALOS PALSAR. Comparison between PSInSAR derived deformation and groundwater level fluctuation reveals a good correlation using time series analysis with some discrepancies.

Small Baseline Subset (SBAS) technique is one of most accurate methods in Differential SAR interferometry (DInSAR) to estimate the surface deformation. In this paper, this technique has been applied on 23 X-band COSMO-SKyMed (CSK) datasets during 2011 – 2016 to get the annual subsidence rate over Jharia Coal Field (JCF), India. Validation of the subsidence result with ground water level data strongly indicates the predominant underground coal mining induced surface deformation over Jharia area.

Differential Synthetic Aperture Radar interferometry (DIn-SAR) is an efficient technique for observing and monitoring ground deformation over a large area at a millimetric level using multi-temporal SAR images. However, the phase decorrelation phenomena along with other bottlenecks, such as atmospheric nuisance, orbital, and DEM inaccuracy limit the accuracy of the traditional DInSAR technique. Persistent Scatterer InSAR (PSInSAR), based on DInSAR, circumvents these limitations using temporally stable reflectors or permanent scatterers (PS) of earth surface using a long temporal stack of SAR images. In this paper, a spatial correlation based PSInSAR technique is applied to detect the ground deformation of Las Vegas, Nevada, US between 2002 to 2010. This aim of this study is focused on the precise estimation and validation of ground deformation using the PSInSAR analysis of descending pass Envisat ASAR data, and further cross-validation of displacement time-series is carried out with GPS time-series observations. Archived C-band Envisat ASAR SAR data stack was used to estimate ground deformation map of the concerned area. A good correlation is observed between PSInSAR and GPS time-series. We have observed a mean annual deformation rates of -5 to 5.1 mm/ year during 2002-2010 along satellite line of sight (LOS).

The Oceansat-II Scatterometer has completed two years in orbit. The instrument has been declared operational, and the normalized radar cross section (σ0) and wind products are being made routinely available to the global operational Numerical Weather Prediction community. The σ0data from the sensor have been rigorously analyzed for the past two years. Efforts have been put to systematically correlate the biases observed in the data to the onboard functionality of the instrument and to precisely quantify these biases. These analyses have helped not only in the refinement of the ground-processing algorithm but also in the evaluation of sensor performance. This paper presents some of the analyses that have been carried out related to instrument noise calibration with reference to deep-space observations, estimation of biases in the signal bandwidth, and estimation of fixed remnant attitude biases. This paper also addresses the means for rectifying these instrument-related biases.

This paper presents a new assortment of temperature-sounding channels for a proposed low-Earth-orbit polar Sun-synchronous satelliteborne millimeter-wave atmospheric sounder of the Indian Space Research Organization (ISRO). The newness owes its origin to the exploration of the millimeter-wave O2absorption spectrum in quest of optimal off-resonance frequencies that impose fewer restrictions on channel bandwidth and temperature sensitivity and yet can sound up to 40 km in the atmosphere with a 4-km vertical resolution. This is ISRO's first leap toward millimeter-wave technology. The overall receiver-noise figure for the channels in the 5-mm band (50-60 GHz) has been pessimistically estimated at 5 dB which will severely degrade the system temperature sensitivity. Therefore, channel bandwidth is at premium. The purpose of this design is to limit the number of passbands and simplify the design of frequency-selective filters by choosing center frequencies that have sufficient interleaving and also provide a scope of allotting a reasonable bandwidth. The set of temperature-sounding channels in the current design have 15 channels in 17 passbands in contrast with 15 channels in 29 passbands of the operational Advanced Microwave Sounding Unit (AMSU)-A.

This paper departs from the popular usage of the Backus-Gilbert inversion (BGI) method as a tool for inversion of antenna temperature measurements in microwave radiometry. The BGI method is applied in this paper to enhance the information content of an existing set of oversampled brightness-temperature (TB) data. The purpose is to isolate the inversion process from its resolution enhancement counterpart. The advantage gained is that the resolution enhancement can be performed in a simplified way and in a different level of processing that starts with the scan-mode TBdata product and simply requires with it the knowledge of the antenna gain pattern and the sensor's scan geometry. The technique is demonstrated with the 19.35-GHz Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) channel, which provides oversampled TBdata. The radiometric resemblance of this channel with that of the 37 GHz and geocollocation of their TBfootprints facilitate validation of the enhancement of features. The significance of oversampling the low-frequency (LF) radiometer channels is underscored in the process, which gives the authors the confidence to propose oversampling of the LF data for the forthcoming sensor Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard the Megha_Tropiques mission, which is a joint ISRO-CNES collaboration (due for launch in 2009).