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The ocean color monitor (OCM) sensor onboard OceanSat‐2 is providing data in visible and near Infrared (NIR) bands. Due to the limited spectral coverage of OCM, the widely used dark‐target and deep‐blue aerosol algorithms cannot be adapted. Here, a new aerosol optical depth (AOD) retrieval algorithm for OCM (or similar sensors) over land, termed Space Applications Center AErosol Retrieval (SAER) is described. It utilizes two blue bands for the AOD inversion and, Red and NIR band to characterize the surface in visible bands without assuming red and NIR bands transparent to aerosols. Unlike the dark‐target algorithm, the SAER algorithm can retrieve AOD over bright arid and urban areas too. The uncertainty analysis of SAER suggests a theoretically expected error (EE) envelope of ±(0.06 + 0.26 × AOD) for typical retrieval conditions. OCM AOD over land is retrieved operationally for the first time over Indian and neighboring countries' landmass at the finest spatial resolution of 0.007°. The SAER algorithm is validated against in‐situ AOD measurements during the years 2016–2018 at 21 Aerosol Robotic Network stations located in south‐Asia. Overall validation using 1900 match‐up points shows correlations exceeding 0.8 with 74% of retrievals within EE. The retrievals over cropland, grassland, and mixed land cover types show high (low) correlation (bias), while over bright urban areas somewhat low (high) correlation (bias) is observed. Excluding monsoon season, OCM AOD retrievals show good performance over the year. The performance of MODerate Resolution Imaging Spectroradiometer dark‐target and OCM AOD, against common in‐situ, is close to each other. The study shows that OCM AOD can be used for air quality monitoring/modeling at high spatial resolution. Key Points An algorithm termed Space Applications Center AErosol Retrieval is developed for aerosol optical depth (AOD) retrieval at subkilometer resolution for ocean color monitor (OCM) (a visible to near Infrared sensor) use over land The theoretical uncertainty study of the algorithm suggests an absolute and relative error of 0.06 and 0.26, respectively, in AOD retrievals Operational OCM AOD retrievals show a very good correlation with Aerosol Robotic Network measurements and MODerate Resolution Imaging Spectroradiometer dark target retrievals

This study assessed the potential and demonstrated the applicability of hyperspectral images from AVIRIS-NG data in mapping the health condition of the Lothian Island estuarine mangrove forest with rich floral diversity for the years 2016 and 2018. Nine vegetation indices covering every aspect of the health status were considered and a weighted overlay analysis was performed to generate health maps from AVIRIS-NG data. Discriminant Normalized Vegetation Index (DNVI) was produced from Sentinel-2 data for both the years 2016 and 2018 to validate the results from AVIRIS-NG data. The analysis showed considerable negative trends in plant pigment contents indicating poor vegetation health and increased stress in the entire mangrove forest of the Lothian Island. Furthermore, red-edge analysis at four specific points helped in validating our health maps generated from both AVIRIS-NG and Sentinel-2 data. Nearly 56% of the total area exhibits evidence of increasing stress within the span of the study years. 35% of the area has shown no change in the existing stress conditions in 2018. Both new plantation and species-specific resilience to stress appear to be the key factors for the sustenance of the existing mangrove population in the Lothian Island of the Indian Sundarbans.

Optical imaging satellites, such as SPOT and Cartosat‐2S, provide visible/near infrared (VNIR) multispectral data at very high spatial resolution. The applications of these data sets are associated with precise mapping, monitoring, and change detection of Earth's surface, given that the measurements can be compensated for atmospheric effects. Existing atmospheric correction (AC) algorithms use visible and shortwave infrared channels and therefore cannot be used for AC of data from VNIR sensors. This article describes an algorithm for aerosol optical depth (AOD) retrieval and AC of VNIR imaging data. The AOD algorithm relies on the fact that for vegetated surfaces there exists a visible/NIR surface reflectance relationship due to the absorption of solar radiation by photosynthetic pigments in visible bands, while high reflectance in NIR bands governed by structural discontinuities in the leaves of healthy vegetation. We then describe how retrieved AOD is used to derive surface reflectance. To test the algorithm, the aerosol and surface reflectance products generated from 106 Cartosat‐2S data sets are compared with MODIS‐terra products. The algorithm significantly removes the haze from the images making surface feature visible. The comparison of Cartosat‐2S and MODIS‐terra AOD involving >1,500 data points shows good correlation of 0.95 with a relative difference of ≤25%. Similarly, the comparison of surface reflectance involving >4,500 data points shows good correlation ranging from 0.75 to 0.86 with a relative difference ranging from 24% to 37%. The normalized difference vegetation index shows a correlation of 0.89, with a relative difference of ≤18%. Results show that the given algorithm may be useful for AC of data from VNIR sensors. Key Points An algorithm for aerosol retrieval and atmospheric correction of visible and near‐infrared sensor is presented The algorithm significantly removes the haze from the high‐resolution images making surface feature visible Comparison of derived aerosol and surface reflectance with MODIS‐terra products shows good agreement

PurposeExtracting suitable features to represent an image based on its content is a very tedious task. Especially in remote sensing we have high-resolution images with a variety of objects on the Earth's surface. Mahalanobis distance metric is used to measure the similarity between query and database images. The low distance obtained image is indexed at the top as high relevant information to the query.Design/methodology/approachThis paper aims to develop an automatic feature extraction system for remote sensing image data. Haralick texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree (QT) decomposition are developed as feature set to represent the input data. The extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user interface.FindingsThe developed retrieval system performance has been analyzed using precision and recall and F1 score. The proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction methods.Originality/valueThe main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT decomposition. The features required to represent the image is 207 which is very less dimension compare to other texture methods. The performance shows superior than the other state of art methods.

Speckle noise occurs in a wide range of medical images due to sampling and digital degradation. Removing speckle noise from medical images is the key for further automated processing techniques like segmentation, and can help the clinicians with better diagnosis and therapy. We consider partial differential equation (PDE)-based denoising model which is a modified Euler-Lagrange equation derived from the total variation minimization functional with additional speckle noise constraints. The new PDE model is designed and optimized to rectify speckle noise and enhance edges present in medical imagery. Wealso develop the efficicient and stable discretization techniques for the corresponding speckle denoising model. The method is tested for several types of images including ultrasound images, and it is compared favorably to the conventional denoising model.

Mangroves play a major role in supporting biodiversity, providing economic and ecological security to the coastal communities, mitigating the effects of climate change and global warming. Species level classification of mangrove forest, understanding physical as well as chemical properties of mangrove vegetation, mangrove health, pigments, and levels of stress are some of the key issues for making scientific and management decisions. Hyperspectral remote sensing owing to its narrow bands, yield information on structural details and canopy parameters. Hyperspectral data over Sundarban and Bhitarkanika mangrove forests are analyzed for species discrimination and forest health assessment. In all, 15 mangrove species in Sundarban and 7 mangrove species in Bhitarkanika have been identified and classified using Spectral Angle Mapper technique. In-situ spectro-radiometer data has been used along with AVIRIS-NG hyperspectral data. Based on response of vegetation in blue, red and near-infrared regions, combination of vegetation indices are used to assess mangrove forest’s health. Reduction in NIR reflectance with shift towards lower wavelength has been observed in less healthy groups.

Applications of high-spatial resolution imaging spectrometer data acquired from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) under India campaign 2015–16, require a thorough compensation for atmospheric absorption and scattering. The data-processing algorithms used for retrieving critically important atmospheric parameters, namely ‘water vapour and aerosol optical depth (AOD)’ over land and water surfaces are presented. Over land surfaces, the dark dense vegetation method and radiative transfer modelling are used for deriving spectral AOD for boxes of 20 × 20 pixels. For AOD retrieval over water surfaces, dark-target approximation is used with near-infrared and short-wave infrared measurements. Estimation of precipitable water vapour is carried out using short-wave hyperspectral measurements for each pixel. A differential absorption technique (continuum interpolated band ratio) has been used for this purpose. The retrieved AOD and water vapour values were compared with in situ sun-photometer and radiosonde data respectively, indicating good matches. Further, these parameters were used to derive ‘atmospherically corrected surface reflectance and remote sensing reflectance’, for land and water surface respectively, assuming horizontal surfaces having Lambertian reflectance.

We have monitored the changes that have occurred over nine Antarctic ice shelves between 1997 and 2019 using Sentinel-1 and RADARSAT-1 images of Antarctica using change detection technique. The net loss of Antarctic ice shelves during the period was about 14,723 sq. km in surface area, corresponding to 1.21% area of ice shelves. The Ross and Filchner–Ronne ice shelves retreated significantly in terms of total area, while shelves in that Antarctic Peninsula, namely Wilkins and Larsen C retreated drastically in terms of percentage change.

Species diversity and vegetation health are two critical components to be monitored for sustainable forest management and conservation of biodiversity. The present study characterizes species dominance and α-diversity of a forest for the selected region in Mudumalai Wildlife Sanctuary (MWS), Western Ghats, which represents one of the most economically important forest types in India – the tropical dry deciduous forest. NASA’s Next-Generation Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-NG) data at spectral resolution of 5 nm and spatial resolution of 5 m were used to analyse the forest matrix. Biodiversity (α-diversity) map thus generated from airborne platform over 14.5 sq. km area mostly represents the forest tree species diversity. Dominant tree species in the study area were also mapped using AVIRIS data for 21.7 sq. km. Canopy emergent dominant species, viz. Anogeissus latifolia, Tectona grandis, Terminalia alata, Grewia tiliifolia, Syzygium cumini and Shorea roxburghii were classified using spectral angle mapper technique and image-based spectra in the MWS study site. The study shows that nearly 40% area is dominated by A. latifolia and 27.5% by T. grandis in the study site. This study concludes that AVIRIS data can be used in the delineation of species and α-diversity mapping at community level; however, the accuracy achieved for species classification is moderate (60%) due to intermixing of species in the study area. For the Shimoga study site in Karnataka, the field spectra were collected using a spectroradiometer and used for the classification for the three dominant tree species using absorption peak decomposition technique. Field-collected pure spectra were analysed and species-wise absorption peaks (Gaussian) with central wavelength, peak amplitude and dispersion were used as the endmembers for classification. AVIRIS-NG data over Shoolpaneshwar Wildlife Sanctuary (SWS) study site used for fuel load estimation with narrow band indices calculated from AVIRIS-NG datasets. AVIRIS-NG data for MWS and Shimoga study site were collected during 2 and 5 January 2016, while for SWS site data were collected on 8 February 2016.