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The first phase of an airborne science campaign has been carried out with the Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) imaging spectrometer over 22,840 sq. km across 57 sites in India during 84 days from 16 December 2015 to 6 March 2016. This campaign was organized under the Indian Space Research Organisation (ISRO) and National Aeronautics and Space Administration (NASA) joint initiative for HYperSpectral Imaging (HYSI) programme. To support the campaign, synchronous field campaigns and ground measurements were also carried out over these sites spanning themes related to crop, soil, forest, geology, coastal, ocean, river water, snow, urban, etc. AVIRIS-NG measures the spectral range from 380 to 2510 nm at 5 nm sampling with a ground sampling distance ranging from 4 to 8 m and flight altitude of 4–8 km. On-board and ground-based calibration and processing were carried out to generate level 0 (L0) and level 1 (L1) products respectively. An atmospheric correction scheme has been developed to convert the measured radiances to surface reflectance (level 2). These spectroscopic signatures are intended to discriminate surface types and retrieve physical and compositional parameters for the study of terrestrial, aquatic and atmospheric properties. The results from this campaign will support a range of objectives, including demonstration of advanced applications for societal benefits, validation of models/techniques, development of state-of-the-art spectral libraries, testing and refinement of automated tools for users, and definition of requirements for future space-based missions that can provide this class of measurements routinely for a range of important applications.

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.

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.

Remote sensing is a vital tool to assess water quality parameters in water bodies like rivers, lakes, estuaries and lagoons. All these fall under the category of optically complex waters (case 2), where water-leaving radiance is affected by optically active water constituents and bottom substrate. The present study estimates water quality parameters, viz. turbidity, suspended sediment concentration and chlorophyll in River Ganga in Buxar (Bihar), and Howrah (West Bengal) and Chilika lagoon (Odisha) using hyperspectral reflectance data of AVIRIS-NG. Concurrent ground-truth data of water samples were collected and simultaneous spectro-radiometer measurements were made in synchronous with the AVIRIS-NG flight over the study area. Semi-analytical simulation modelling followed by inversion and contextual image analysis-based methods were used for estimating the water quality parameters. Water turbidity maps were generated for both the study sites. Over Ganga river, water was relatively clear in Buxar (6.87–20 NTU, TSS 42–154 mg/l), while it was extremely turbid in Howrah (50–175 NTU, TSS 75–450 mg/l). In Chilika lagoon, water was more turbid in the northern sector, which may be due to the river input and resuspension from shallow bathymetry. The results suggest that the small-scale changes in turbidity due to point sources like river tributaries or sewerage discharges can be identified using hyperspectral data. The imaging spectroscopy data over water are a key source to find out potential locations of water contamination.

Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%–94% for AVIRI-NG and 42%–55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (Cab ) using AVIRIS-NG narrow bands. The retrieved LAI and Cab showed 19%–27% and 23%–29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662–702 and 2155–2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.

Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time. Our research compares linescan and snapshot hyperspectral cameras for brain tissues and chromophore identification. We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information. The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at , two for at and , and one for water at . The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.

In this article, we discuss the potential of airborne hyperspectral data in mapping host rocks of mineral deposits and surface signatures of mineralization using AVIRIS-NG data of a few important geological provinces in India. We present the initial results from the study sites covering parts of northwest India, as well as the Sittampundi Layered Complex (SLC) of Tamil Nadu and the Wajrakarur Kimberlite Field (WKF) of Andhra Pradesh from southern India. Modified spectral summary parameters, originally designed for MRO-CRISM data analysis, have been implemented on AVIRIS-NG mosaic of Jahazpur, Rajasthan for the automatic detection of phyllosilicates, carbonates and Fe–Mg-silicates. Spectral analysis over Ambaji and the surrounding areas indicates the presence of calcite across much of the study area with kaolinite occurring as well in the north and east of the study area. The deepest absorption features at around 2.20 and 2.32 μm and integrated band depth were used to identify and map the spatial distribution of phyllosilicates and carbonates. Suitable thresholds of band depths were applied to map prospective zones for marble exploration. The data over SLC showed potential of AVIRIS-NG hyperspectral data in detecting mafic cumulates and chromitites. We also have demonstrated the potential of AVIRIS-NG data in detecting kimberlite pipe exposures in parts of WKF.

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach. We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes and and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO). The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in , Hb, oxCCO, and the oxidized state of cytochrome and (oxCytb and oxCytc). The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in , Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900 nm. The optimization procedure does not help to resolve changes in cytochrome and in a significant way but helps to better resolve oxCCO changes. We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

Developing stable, robust, and affordable tissue-mimicking phantoms is a prerequisite for any new clinical application within biomedical optics. To this end, a thorough understanding of the phantom structure and optical properties is paramount. We characterized the structural and optical properties of PlatSil SiliGlass phantoms using experimental and numerical approaches to examine the effects of phantom microstructure on their overall optical properties. We employed scanning electron microscope (SEM), hyperspectral imaging (HSI), and spectroscopy in combination with Mie theory modeling and inverse Monte Carlo to investigate the relationship between phantom constituent and overall phantom optical properties. SEM revealed that microspheres had a broad range of sizes with average and were also aggregated, which may affect overall optical properties and warrants careful preparation to minimize these effects. Spectroscopy was used to measure pigment and SiliGlass absorption coefficient in the VIS-NIR range. Size distribution was used to calculate scattering coefficients and observe the impact of phantom microstructure on scattering properties. The results were surmised in an inverse problem solution that enabled absolute determination of component volume fractions that agree with values obtained during preparation and explained experimentally observed spectral features. HSI microscopy revealed pronounced single-scattering effects that agree with single-scattering events. We show that knowledge of phantom microstructure enables absolute measurements of phantom constitution without prior calibration. Further, we show a connection across different length scales where knowledge of precise phantom component constitution can help understand macroscopically observable optical properties.