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The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth's processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image's spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald's reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset's successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise's findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions' datasets. [ABSTRACT FROM AUTHOR]

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimation of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) are evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide, and (2) aircraft in situ HCHO measurements from campaigns conducted over the U.S. in 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI = 0.651 Ωairc + 2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data givesΩOMI = 0.659 ΩFTIR + 2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (~445 Tg yr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over Southeastern U.S. remains imperfectly elucidated. [ABSTRACT FROM AUTHOR]

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework. [ABSTRACT FROM AUTHOR]

This study aims to create a 21-year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° x 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° x 0.5°) and IMD daily gridded rainfall (0.25° x 0.25°) were utilized to generate long-term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000--2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement final-run. The results suggest GSMaP_IMD has a smaller root-mean-square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3-hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India. [ABSTRACT FROM AUTHOR]

The study aimed to determine the above-ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sentinel 1A C-band synthetic aperture radar (SAR). Field sampling based on 73 forested grids exhibited a diverse forest composition in the Satpura Tiger Reserve (STR) with a total of 54 tree species of which 31.48% species were IUCN Red-Listed. The forests were highly diverse (H′=3.22) with a low concentration of dominance (CD = 0.06) and a quite high species evenness (E = 0.81). The sensitivity and correlation between field-based AGB and SAR backscatter in STR were highest in cross-polarization due to its higher sensitivity to the vegetation structure in both L- and C-band SAR, where L-VH based AGB estimates were found more accurate (RMSE:5.17%). The study provided vital information for setting up strategies for biodiversity conservation, enhancing forest productivity, and restoring wildlife habitats. [ABSTRACT FROM AUTHOR]

Glaciers are found largely in remote and inhospitable terrains around the world, and it is generally known that the ocean has transformed into glaciers, according to a long-standing study. As a result, remote sensing technology is critical for remotely monitoring the region to detect any potential threats or mass changes in the near future. We used C-band Synthetic Aperture Radar data from the Sentinel-1 satellite to estimate the surface velocity of the Gangotri glacier in the Himalayan region in this paper. Using SAR offset tracking methodology, the maximum intensity correlation provides the two-dimensional movement of the Gangotri glacier and its tributaries in the Gharwal Himalayas in India. The repeat-pass SAR image subpixel offset tracking approach was also used to create velocity vectors from Sentinel-1 seasonal datasets. Based on 40 years of daily reports, we also used the Artificial Neural Network (ANN) Algorithm to forecast future trends in precipitation, temperature, and insolation index for the region. [ABSTRACT FROM AUTHOR]

Tropical coastal wetlands have been well recognized for their role in mitigating climate change apart from possessing high biodiversity values as well as providing important habitats for fisheries and other ecosystem services. Although these wetlands play key roles in sequestering carbon (C) in the lithosphere and hydrosphere, there is a notable dearth of comprehensive yet spatially explicit accounts of their total blue carbon (TBC) stocks. The anthropogenic factors including development stressors have fundamentally altered the global C dynamics. In this respect, holistic accounting of the total C stocks of the coastal wetlands becomes the foremost task in emphasizing their recognition as integral components of climate change mitigation strategies. This study modeled TBC stocks of an intertidal mudflat of eastern India across different land use/ land covers (LULCs) by quantifying the above ground biomass (AGB), below ground biomass (BGB), and soil organic C (SOC). The distribution of SOC was modeled through spatial interpolation of point-specific C densities (R2 = 0.77; RMSE = 22.91 Mg ha−1). AGB was estimated by establishing allometric relationships between geospatial data, both of active (PALSAR-2) and passive (Pleiades-1B) nature and in-situ dendrometric variables using the artificial neural network (R2 = 0.85; RMSE = 48.91 Mg ha−1). BGB was estimated from the modeled AGB. Spatial integration of these datasets estimated the TBC stock of mudflat as 27,469.30 Mg. Overall, open mangrove represented highest mean TBC density (163.29 Mg ha−1) while herbaceous vegetation recorded largest TBC stock (9818.94 Mg). Mean TBC densities across LULCs were significantly different (p < 0.05) from each other except for the herbaceous vegetation and built-up categories, indicating the inertial effects of paleo-land covers. Accordingly, this study could be inferred as a pioneering one in developing a comprehensive geospatial model of TBC accounting in India towards strengthening the regional and national C inventories. [Display omitted] • Total blue C stocks of an intertidal mudflat including AGB, BGB and SOC estimated • Applying destructive/non-destructive sampling, elemental analysis and RS datasets • Coupling of fine resolution multispectral and SAR datasets through ANN • Effects of land use/land cover patterns on blue C dynamics assessed [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

Reflectivity measurements from X‐band ground‐based radar (GR) are assessed using observations from the Ku‐band space‐borne radars (SRs) onboard the Tropical Rainfall Measuring Mission and Global Precipitation Measurement satellites as a reference. The GR is at Mandhardev (18.04°N, 73.86°E) in the Western Ghats region, and the evaluation period is from June to September of 2014 and 2018. At X‐band, the rainfall‐induced attenuation leads to significant signal loss. The rain attenuation correction of GR is performed based on the relationship between specific attenuation and reflectivity. Using T‐matrix scattering simulations, the specific attenuation and reflectivity are derived from the disdrometer data. Further, the systematic differences between reflectivity measured by GR and SR are minimized by performing the frequency scaling using scattering simulation. The comparison between corrected reflectivities of GR and SR is achieved by matching the resolution volumes and viewing geometry of both radars. The evaluation between corrected reflectivities of GR and SR shows reasonable agreement between them, indicating that the attenuation correction performs reasonably for GR. In rain regions, the GR bias lies between −2.6 and −1.8 dB for stratiform cases, while for convective samples, the GR bias varies from −2.4 to −0.7 dB relative to SR observations. The GR shows smaller reflectivity compared to SR at all heights. This study aims to implement the attenuation correction to long‐term X‐band radar data available in the Western Ghats region to study the orographic precipitation and convective system during monsoon. Key Points: For the first time, assessment of reflectivities between IITM's X‐band radar and TRMM‐GPM Ku‐band radars is carried outThe bias in GR attenuated corrected reflectivity is within −2.7 dB relative to SR measurementsThis study provides a framework to have attenuated corrected reflectivity for better quantitative application of reflectivity data [ABSTRACT FROM AUTHOR]

Monsoonal convective systems are examined using four years (2014-2017) of radar reflectivity data from the Precipitation Features (PFs) database of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual Precipitation Radar (DPR). The study classifies the cumulonimbus tower (PFCbT) at 12 km, and intense convective clouds at both 3 km (PF-ICC3) and 8 km (PF-ICC8) based on PFs' reflectivity threshold at a reference height over the Indo-Gangetic Plains (IGP) and Indian Western coastal (WG) region, including the Arabian Sea and the Western Ghats. Results show that the regional variations are more enhanced for the PF-ICC3 clouds with high occurrence over the IGP region. In the mixed-phase regime, the median maximum reflectivity is greater for all cloud types over the IGP region. The occurrences of 20 and 40 dBZ echo the top height > 5 km is higher in the IGP region, indicating the deep and intense convection. The aerosol-cloud interaction is examined for warm and mixed-phase clouds. The vertical structure of aerosols shows the suppression of warm rainfall over the IGP region. However, rainfall intensity increases in mixed-phase clouds because of the dominancy of ice processes. The significant positive (negative) correlation is observed between the echo top height and aerosol concentrations over the IGP (WG) region. The value of the novel findings clearly states the region-specific demand for a closer examination of the radar reflectivity-aerosol interaction on regime-dependent clouds over the IGP region as well as contrasts against other regions for similar and contrasting cloud-aerosol-radiation interactions. [ABSTRACT FROM AUTHOR]

In this study, one of the latest space-based techniques for measuring sub-centimetric ground displacement, Differential Synthetic Aperture Radar Interferometry (D-InSAR) has been used to assess the potential land subsidence phenomenon of Kolkata city, India. The occurrence of a thick surface clay layer with an average thickness of ∼40 m and above, raises a question on the land subsidence phenomenon in Kolkata city. At this juncture, the D-InSAR-based study will help to ascertain the actual land subsidence scenario of Kolkata city. We have identified an area in Kolkata city in and around Machhua Bazar, Calcutta University and Raja Bazar Science College, which had been undergoing subsidence during the period of observation, i.e. 1992-98 with an estimated rate of 5 to 6.5 mm/yr. [ABSTRACT FROM AUTHOR]