Airborne Visible/Infrared Imaging Spectrometer-Next Generation Research Articles

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction (Rval2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions.

Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers

Abstract. Remote sensing emerges as an important tool for the detection of methane plumes emitted by so-called point sources, which are common in the energy sector (e.g., oil and gas extraction and coal mining activities). In particular, satellite imaging spectroscopy missions covering the shortwave infrared part of the solar spectrum are very effective for this application. These instruments sample the methane absorption features at the spectral regions around 1700 and 2300 nm, which enables the retrieval of methane concentration enhancements per pixel. Data-driven retrieval methods, in particular those based on the matched filter concept, are widely used to produce maps of methane concentration enhancements from imaging spectroscopy data. Using these maps enables the detection of plumes and the subsequent identification of active sources. However, retrieval artifacts caused by particular surface components may sometimes appear as false plumes or disturbing elements in the methane maps, which complicates the identification of real plumes. In this work, we use a matched filter that exploits a wide spectral window (1000–2500 nm) instead of the usual 2100–2450 nm window with the aim of reducing the occurrence of retrieval artifacts and background noise. This enables a greater ability to discriminate between surface elements and methane. The improvement in plume detection is evaluated through an analysis derived from both simulated data and real data from areas including active point sources, such as the oil and gas (O&G) industry from San Joaquin Valley (US) and the coal mines from the Shanxi region (China). We use datasets from the Precursore IperSpettrale della Missione Applicativa (PRISMA) and the Environmental Mapping and Analysis Program (EnMAP) satellite imaging spectrometer missions and from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) instrument. We find that the interference with atmospheric carbon dioxide and water vapor is generally almost negligible, while co-emission or overlapping of these trace gases with methane plumes leads to a reduction in the retrieved concentration values. Attenuation will also occur in the case of methane emissions situated above surface structures that are associated with retrieval artifacts. The results show that the new approach is an optimal trade-off between the reduction in background noise and retrieval artifacts. This is illustrated by a comprehensive analysis in a PRISMA dataset with 15 identified plumes, where the output mask from an automatic detection algorithm shows an important reduction in the number of clusters not related to CH4 emissions.

Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient.

Tropical forests exhibit significant diversity and heterogeneity in species distribution. Some tree species spread abundantly, impacting the functional aspects of communities. Understanding how these facets respond to climate change is crucial. Field data from four protected areas (PAs) were combined with high-resolution Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets to extract large-scale plot data of abundant species and their functional traits. A supervised component generalized linear regression (SCGLR) model was used to correlate climate components with the distribution of abundant species across PAs. The recorded rainfall gradient influenced the proportion of PA-specific species in the observed species assemblages. Community weighted means (CWMs) of biochemical traits showed better correlation values (0.85-0.87) between observed and predicted values compared to biophysical traits (0.52-0.79). The model-based projection revealed distinct distribution responses of each abundant species to the climate gradient. Functional diversity and functional traits maps highlighted the interplay between species heterogeneity and climate. The appearance dynamics of abundant species in dark diversity across PAs demonstrated their assortment strategy in response to the climate gradient. These observations can significantly aid in the ecological management of PAs exposed to climate dynamics.

CO2 concentration retrieval and emission rate estimation over Indian thermal power plants using radiative transfer approach and AVIRIS-NG data

Carbon dioxide (CO2) emissions from thermal power plants (TPPs) are regarded as the primary source of CO2 at the boundary-level atmosphere. The first step toward mitigating these emissions is to improve emission estimation methods at the facility level and establish a reliable emission reporting system. This study aims to establish a transparent and reliable methodology for detecting point source CO2 emissions from TPP and estimating the emission rate released from the stakes of TPP. A remote sensing method continuum interpolated band ratio (CIBR) has been utilized to detect and estimate CO2 emissions over Indian TPPs using Airborne Visible/InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) data acquired by the ISRO-NASA joint campaign over India in 2015 and 2018. MODerate resolution atmospheric TRANsmission (MODTRAN) 5.3 radiative transfer (RT) model simulations were carried out for TPP sites to develop a CIBR algorithm for CO2 retrieval. Two AVIRIS-NG short-wave infrared (SWIR) bands were utilized for CO2 concentration retrieval, and the results from each band were compared based on their emission detection capabilities. Comparatively, SWIR-2 (1960–2120 nm) bands show higher sensitivity of CO2 than SWIR-1 (1560–1640 nm). Therefore, we utilize SWIR-2 results for the CO2 retrieval and emission rate estimation. Maximum CO2 emissions of 53.10, 46.36, 96.84, and 53.49 ppmv were detected at Maithon, Dhariwal, Udupi, and Parangipettai TPPs. The study includes one USA-based TPP to compare emission results for validation and test the method in diverse atmospheric conditions. In addition, we compared CO2 estimation results with AVIRIS-NG benchmark data. The simple band ratio method uses only three SWIR bands and operates with minimal computational facility, making it suitable for CO2 retrieval in hyperspectral data. Further, the integrated mass enhancement (IME) method has been used for emission rate estimation using CO2 concentration values from SWIR-2 retrieval results and wind speed at the sites. Yearly 5.96, 6.82, 2.29, and 3.52 Mt emissions were estimated for Maithon, Dhariwal, Udupi, and Parangipettai TPPs, respectively. The emission results have been validated by establishing a relationship between TPP's coal consumption and CO2 emissions. Further, estimated emission rates have been validated with former investigations and the CO2 baseline emission database of India's Central Electricity Authority (CEA) reports. Not having in-situ measurements and uncertainty in wind data may limit the study.

A hyperspectral view of the nearshore Mississippi River Delta: Characterizing suspended particles in coastal wetlands using imaging spectroscopy

Suspended particle concentration (Total Suspended Solids or TSS) determines sediment availability for deposition, whereas particle physico-chemical properties (e.g. composition and size) can influence sediment transport and accretion. These variables are critical to modeling and understanding nearshore sediment dynamics and coastal soil accretion and erosion. Upcoming satellite imaging spectrometers (e.g., GLIMR, SBG) have the potential to improve the detailed mapping and characterization of suspended particles in nearshore areas. Here, we conducted a detailed feasibility study assessing the capacity of imaging spectroscopy to accurately map suspended particle concentrations and physico-chemical properties in the Mississippi River Deltaic Plain (Louisiana, USA), a nearshore system with extreme coastline vulnerability. To that end, a large dataset of in situ TSS, Particulate Organic Carbon (POC) concentration, particle size, inherent optical properties (IOPs), and hyperspectral remote sensing reflectance (Rrs) was collected during the 2021 NASA Delta-X field campaigns in coastal Louisiana. Analyses of the data revealed that the enhanced spectral information provided by imaging spectroscopy (hyperspectral Rrs) improved the retrieval of TSS and POC/TSS (% organic carbon content), but highlighted the challenge of retrieving particle size in this system. Partial-least-squares-regression (PLSR) algorithms for TSS performed better than existing state-of-the-art algorithms and confirmed the importance of using local measurements and near-infrared Rrs. Implementation on Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery generated high-resolution (< 5 m), high-quality maps of TSS and POC/TSS in contrasted areas with a wide range of TSS concentrations from (0.1 to 100 mg L−1), and demonstrated the enhanced potential of upcoming missions such as SBG and GLIMR to inform studies of nearshore sediment dynamics and coastal vulnerability/resilience.

Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.

A novel method for detecting soil salinity using AVIRIS-NG imaging spectroscopy and ensemble machine learning

Soil salinization is one of the major land degradation processes spread over millions of hectares of global land. Hyperspectral Remote Sensing (HRS) coupled with modern data mining approaches help in real-time and cost-effective assessment or monitoring of salt-affected soils. This study aimed at predicting soil salinity across five sites in India using the Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) data in low to moderately salt-affected cropland soils. We have identified four unique spectral absorption features having sensitivity towards soil salinity through a hybrid feature selection algorithm. Soil electrical conductivity (EC) was estimated using different machine learning (ML) based models such as random forest (RF), gradient boosting machines (GBM), and deep learning (DL). An ensemble of RF and DL models showed the best performance with the coefficient of determination (R2) of 0.89 and 0.55 and normalized root-mean-squared error of 0.15 and 0.16 in training and test datasets, respectively. We also proposed a new hyperspectral soil salinity index using Shannon entropy-based aggregation of selected absorption features. The newly proposed index outperformed other majorly used remote sensing-based salinity indices. It also showed a strong correlation with measured EC (r = 0.68) and ML-predicted soil EC (r = 0.78), both being significant at 1% level of significance. The index was effective in classifying HRS images into six distinct salinity classes. We also assessed the feasibility of applying the proposed salinity index for future hyperspectral missions through the simulation of various spectral-spatial resampling scenarios and estimated the optimal spectral and spatial resolution for salinity prediction. The hyperspectral salinity index can be directly estimated from HRS data without the need for time-consuming and expensive field samplings and used as a proxy to evaluate soil salinity status under field conditions.

Examining effect of super-resolution on AVIRIS-NG data: A precursor to generation of large-scale urban material and natural cover maps

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.

Estimation of soil and crop residue parameters using AVIRIS-NG hyperspectral data

ABSTRACT Detailed ground cover information and efficient modelling approaches are needed for estimating soil properties from hyperspectral remote sensing (HRS) data. With the objective to estimate both soil and crop residue (CR) parameters using HRS data from the airborne visible-infrared imaging spectrometer-next generation (AVIRIS-NG) sensor, soil and CR samples were collected from 101 locations in the Western Catchment of Chilika lagoon, India. Nonlinear unmixing and two chemometric models were examined for estimating basic soil properties, nutrient contents, soil and CR wetness, and masses of fresh crop residue (FCR) and dry crop residue (DCR). Both soil and CR parameters showed wide variation with FCR and DCR masses varying from 0 to 13.33 Mg ha−1 and 0 to 10.16 Mg ha−1, respectively. Soils were relatively dry with an average gravimetric water content (θg) of 14% whereas CR moisture content (θc) ranged from 2.4 to 93%. Estimated coefficient of determination (R2) values in the validation datasets varied from 0.51 for soil base saturation to 0.91 for exchangeable Mg+2 using soil spectra obtained through linear polynomial unmixing of AVIRIS-NG spectra. The R2 values were more than 0.70 for clay content, soil organic carbon (SOC), cation exchange capacity (CEC), and several exchangeable cations although the CR parameters showed lower R2 values than soil parameters. With the chemometric models calibrated, high spatial resolution maps for soil and CR parameters were generated, which offer a continuous mapping capability for such parameters. Extensive soil property data also allowed us to evaluate SOC sequestration capability of different land use systems. A critical value of 1/25 for the SOC/Clay content ratio was observed for most of our agricultural land uses and degraded landscapes showed lower SOC/Clay ratios. Thus, high spatial resolution soil and crop residue parameters may be accurately assessed for large areas with multiple land use and soil cover conditions.

Radiometric calibration of AVIRIS-NG sensor using Indian desert sites

This manuscript describes reflectance-based vicarious calibration exercise of the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor during its second phase of observation campaigns over the Indian subcontinent. Vicarious calibration methods are one of the significant approaches that have been practiced successfully for the absolute radiometric calibration of various spaceborne and airborne sensors to ascertain its desired performance in terms of data quality and its accuracy. Calibration campaigns were performed for AVIRIS-NG sensor during its phase-2 campaign overpass at the Desalpar (25th March 2018) and Amarapur (27th March 2018) calibration sites of Gujarat, India. Results from ground-based measurements of atmospheric conditions and surface reflectance including descriptions of the test sites are summarized in this work. Based on the in-situ data and images from the AVIRIS-NG sensor, the at-sensor apparent radiances are simulated using the 6SV radiative transfer model. The comparison shows good agreement between AVIRIS-NG measured radiance and 6SV simulated apparent radiance at the two calibration sites using desert aerosol model. The gains (spectrally) derived agree to within ∼ 2 % each other. Furthermore, an analysis is performed to determine or identify systematic and random errors, and the overall uncertainty is evaluated for reflectance-based method (total uncertainty of less than 4 % spectrally).

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ∼5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. We evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

Mapping of understorey invasive plant species clusters of Lantana camara and Chromolaena odorata using airborne hyperspectral remote sensing

The present study highlights delineation of the distribution of understorey invasive plant species (IPS) in Mudumalai Tiger Reserve (MTR), Western Ghats, India using very high resolution airborne imaging spectroscopy images by evaluating the performance of a Multiple Endmember Spectral Mixture Analysis (MESMA). Lantana camara L. and Chromolaena odorata (L.) R.M.King & H.Rob. have imposed a serious threat to natural ecosystems in MTR by altering the ecosystem processes. The Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) based analysis delineated the distribution of understorey IPS, viz., L. camara and C. odorata in MTR, with an overall accuracy of 87 % (k = 0.84) and 84 % (k = 0.8), respectively, with minimal root-mean-square error (RMSE) of MESMA classification (0.001 to 0.0025). In MTR, both the IPS were distributed in large parts and in association with all dominant canopy tree species barring Eucalyptus species. L. camara was majorly found in the southern parts of central MTR with ‘high’ to ‘very high’ presence covering 23.09 % and 11.51 % of the total area of central MTR, respectively, in association with Terminalia anogeissiana Gere & Boatwr. and Tectona grandis L.f. Whereas, C. odorata was dominantly found with high (18.11 % area of central MTR) to very high (11.85 % area of central MTR) intensity in the south and southeastern parts within the vicinity of T. anogeissiana and Terminalia elliptica Willd. The study highlights the utilization of high spectral agility in mapping of understorey IPS through spectral differentiation of the patchy distribution of IPS and its intermixing with other local species may be helpful in ecological restoration, conservation of biodiversity, and provisioning long-term management of ecosystem services in highly invaded tropical forests.

Retrieval of crop biophysical-biochemical variables from airborne AVIRIS-NG data using hybrid inversion of PROSAIL-D

Sustainable agricultural growth and management reduces over-utilization of farm resources and squeezes risk of negative impacts on environment. Monitoring continuous crop growth and health under various conditions at different spatio-temporal resolutions is a key to assess yield stability, crop diversity, adaptability, mitigation for stress and response. The quantification of crop biophysical and biochemical variables spatially from remotely sensed data with help of spectroscopic methods provide a reliable discerning information in the context of crop foliar condition like leaf greenness, senescence, canopy density, crop growth, stress and eco-physiological processes. Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) airborne hyperspectral data offers high spatial and spectral resolution giving a unique advantage and opportunity to test retrievals of crop biophysical (BP)-biochemical (BC) variables under varying conditions over different types of crops. A hybrid inversion of leaf-canopy Radiative Transfer model – PROSAIL-D complemented with use of data driven nonlinear non-parametric methods which offer simplicity, fastness, reliability and competency is a powerful method to retrieve crop biophysical and biochemical variables. The Hyperspectral band (feature) selection is a computationally cost efficient method to overcome data redundancy in high dimensional correlated input spectral bands. The determination of optimum subset or combination of hyperspectral bands specific to retrieval of vegetation properties (including canopy effects) determined using feature selection algorithm for regression-based retrievals are active research topic unlike classification problems in which it is more common. A band selection algorithm based on Gaussian Processes Regression was used to choose most sensitive bands from in-situ biophysical-biochemical measurements and crop spectral signatures collected for analysis in two different agricultural regions: Raichur (Karnataka) and Anand (Gujarat) districts of India representing diverse landscapes, heterogeneous crop canopies and agro climatic settings. The retrieval algorithm for AVIRIS-NG image employed a decision tree ensemble algorithm Canonical Correlation Forests using the optimum subset of bands for retrieval of targeted crop variable. Validation of retrieved crop variables were done using in-situ ground observations collected over heterogeneous diverse crop landscape. The results showed chlorophyll (RMSE = 6.61 µg cm−2), equivalent water thickness (RMSE = 0.002 cm), leaf area index (RMSE = 0.35 m2/m−2) and dry matter (RMSE = 0.003 g cm−2), carotenoid (RMSE = 14.3 µg cm−2), anthocyanin (RMSE = 12.92 µg cm−2) retrieved with better accuracies. The results obtained in context of feature (band) selection approach for Radiative Transfer model inversion show overlapping of sensitive spectral bands of chlorophyll-ab, carotenoid and anthocyanin especially between narrow spectral range 480 to 560 nm as predominant reason for decreased accuracies of anthocyanin and carotenoid compared to other variable. This poses a limitation as well as opportunity for further research in signal separation in context of feature selection approach especially in context of broader spectral bandwidths. It also sets ground for further development of feature selection algorithms that use hybrid regression methods customized for crop specific traits.

Comparing airborne algorithms for greenhouse gas flux measurements over the Alberta oil sands

Abstract. To combat global warming, Canada has committed to reducing greenhouse gases to be (GHGs) 40 %–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation's (SciAv) Gaussian algorithm and the Environment and Climate Change Canada's top-down emission rate retrieval algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance box-flight methods produce similar estimates that agree (3 %–25 %) within algorithm uncertainties (4 %–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv algorithm may improve emission estimation. Algorithms disagreed when non-ideal conditions occurred (i.e., under non-stationary atmospheric conditions). Overall, the results provide confidence in the box-flight methods and indicate that emissions estimates are not overly sensitive to the choice of algorithm but demonstrate that fundamental algorithm assumptions should be assessed for each flight. Using a different method, the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) independently mapped individual plumes with emissions 5 times larger than the source SciAv sampled three days later. The range in estimates highlights the utility of increased sampling to get a more complete understanding of the temporal variability of emissions and to identify emission sources within facilities. In addition, hourly on-site activity data would provide insight to the observed temporal variability in emissions and make a comparison to reported emissions more straightforward.

Identification of key altered/weathered minerals near to the base metal mineral in Jahazpur, India using AVIRIS-NG data

Hydrothermally altered minerals are invariably associated with base metal mineralization. The narrow spectral resolution of Airborne Visible/Infrared Imaging spectrometer -Next Generation (AVIRIS-NG) facilitates the identification and mapping of altered minerals. Spectral angle mapper (SAM) of supervised classification technique was applied on AVIRIS-NG reflectance data for identifying and mapping altered minerals such as talc, kaolinite, soapstone, kaosmec, and montmorillonite. The identified minerals were verified during the ground-truthing survey and, lab generated spectral reflectance curve confirms the presence of minerals. During the field survey, the surface signature of base metal mineralisation was found in the form of malachite stains in the carbonaceous phyllite lithologic unit. Conventional geological technique such as transmitted light petrography and X-ray diffraction analysis confirms the occurrence of base metal mineralization in the area. Therefore, this study justified the delineation of hydrothermally altered zones using AVIRIS-NG hyperspectral data and their validation through lab-generated spectra, petrography, and X-ray diffraction analysis.

Importance of AVIRIS-NG data in assessing the assemblage of guilds of tropical trees over a contrasting climate

Generating large-scale field-based guild data for assessing tree species sorting and distribution over tropical covers is difficult. In this study, we combined field, and the Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets of tropical covers across a climate gradient to develop precision maps of abundant tree species. This assisted in the extraction of a large dataset of guilds with tree species assortment and occupancy. We observed parity in the distribution of local and regional species with an increase in the proportionate presence of local species over the climate gradient. We saw greater distribution of Fabaceae members in sites with relatively lesser rainfall. To address the impact of species sorting on the functional aspects of guilds, available R codes (Ackerly and Cornwell, 2007), and three vegetation indices (VIs) as functional traits (Photochemical Reflectance Index, PRI, Chlorophyll/Carotenoid Index, CCI, and Normalized Difference Water Index, NDWI) were tested. Species assortment and spread affected the guild's functional diversity, as noticed in the abundance weighted guild means of VIs. We also observed a consistent increase in the mean CCI of all species across the climate gradient correlating positively with rainfall and negatively with vapour pressure deficit (VPD). This shows that CCI could be an adequate measure for large-scale ecological observations of these tropical regions. Wider amplitudes in the measured VIs of deciduous species demonstrated their adaptability to the climate gradient. Gradient-driven species assembly dynamics and functional traits variability indicates of tropical tree biodiversity feedback to the climate dynamics.

Spatial spectral contextual image analysis approach to map water quality of inland waterbody using AVIRIS-NG datasets

Remote sensing has already proven its capability in water quality studies. The technology enables the mapping of water quality parameters that affect its optical properties. Generally, multi-spectral remote sensing data has been utilized for such analysis. Mostly, the band rationing or simple regression approaches have been adapted to map the water quality, however, they result in qualitative estimates rather than quantitative. On the other hand, sensor technology has made steep advancements from multi-spectral to hyperspectral. The hyperspectral data provides contiguous spectral information over several hundred narrow spectral bands. This emerging technique provides a more in-depth means of investigating spatial, spectral, and temporal variations, leading to more quantifiable/accurate estimates of the process under consideration. At present, very few satellite-based hyperspectral data are available. Recently, the Indian Space Research Organisation has initiated an airborne hyperspectral remote sensing campaign to cover the important earth features in India using Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor in collaboration with the National Aeronautics and Space Administration (NASA). As the AVIRIS-NG hyperspectral data measures the spectral response in the wavelength range from 380 to 2510 nm at very narrow spectral bandwidth of 5 nm. These advanagtes of AVIRIS-NG datasets enable researchers to analyse the spectra of each and every pixel of the image spatially to match its spectral characteristics with the spectra collected in the field. In the present study, this spectral similarity approach has been expoilted within the context of turbidity, the optical property of water for Chilika Lake, Odisha, India; using the AVIRIS-NG data and field reference spectra. The lake was surveyed synchronous to AVIRIS-NG flights over it during the month of December 2015. A field-based spectral library with respect to various concentrations of turbidity in the lake has been generated using sophisticated instruments like field spectro-radiometer, handheld Global Navigation Satellite System (GNSS) receiver, and turbidimeter. The spectral angle mapper (SAM) spatial-spectral contextual image analysis has been carried out to map the turbidity of the lake. It was observed that almost at each and every pixel the image spectra were matching with the field-collected spectra. Further, the match between two spectra (field and image) measured in terms of SAM score was as high as 0.9 for most of the instances. Moreover, the estimated turbidity was in the agreement (R2 = 0.96) with the turbidity measured on the field. It was realized that the spectral similarity approach using hyperspectral remote sensing data provides more quantitative estimates as compared to multi-spectral data analysis.

Evaluation of machine learning techniques with AVIRIS-NG dataset in the identification and mapping of minerals

Identifying valuable mineral resources is obligatory for human development and survival due to their scientific and economic implications. Hyperspectral Remote Sensing (HRS) is an efficient technique that empowers us to precisely identify and map altered minerals based on spectral absorption curves in the VNIR and SWIR range of the electromagnetic spectrum. Machine Learning Algorithms (MLAs) are a subclass of Artificial Intelligence (AI) that improves and automate HRS based lithological mapping using spectra based classification approach. The present study evaluates various MLAs in identifying and mapping hydrothermally altered and weathered minerals such as kaolinite, talc, kaosmec, and montmorillonite. The study was performed with the Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) hyperspectral dataset of Jahazpur town of Bhilwara district in the state of Rajasthan, India (75° 9′ 57′' E, 25° 32′ 24″ N). The Spectral Angle Mapper (SAM) algorithm was applied to create a reference mineral distribution map for the target mineral classes. Further, the reference map has been verified with the field validation survey. A total of 828 pixels were identified as end member pixels. The obtained endmember pixels were further used for developing predictive machine learning based models for mapping the mineral resources using eight supervised classifiers, namely Support Vector Machine, k-Nearest Neighbor, Decision Tree, Random Forest, Logistic Regression, Artificial Neural Network, Linear Discriminant Analysis, and Naïve Bayes. The performance of the classifiers was measured using the kappa coefficient, overall accuracy, precision, recall, F1-score, Matthews’s correlation coefficient, and ROC curve. It was observed in the study that the Support Vector Machine outperformed all the classifiers in terms of overall accuracy and AVIRIS-NG data poses an excellent potential for mineral mapping using machine learning based models in a diverse mountainous area.

A novel method to simulate AVIRIS-NG hyperspectral image from Sentinel-2 image for improved vegetation/wildfire fuel mapping, boreal Alaska

• - Vegetation mapped from simulated AVIRIS-NG image offers greater details and accuracy. • − 33% improvement in the new vegetation map accuracy. • - A novel approach to generate improved vegetation maps for Alaska’s boreal domain. Detailed vegetation maps are one of the primary inputs for forest and wildfire management. Hyperspectral remote sensing is a proven technique for detailed and accurate vegetation mapping. However, the availability of recent hyperspectral imagery in Alaska is limited because of the logistics and high cost involved in its acquisition. In this study, we simulated AVIRIS-NG (Airborne Visible InfraRed Imaging Spectrometer - Next Generation) hyperspectral data from widely available Sentinel-2 multispectral data using the Universal Pattern Decomposition Method (UPDM). The UPDM is a spectral unmixing technique that uses detailed ground spectra of vegetation classes and the Spectral Response Functions of AVIRIS-NG and Sentinel-2 sensors to simulate imagery with the same number of bands and spectral resolution as an AVIRIS-NG image. We simulated three images (each covering an area of 100 km × 100 km) from two ecoregions to test portability of the approach. We collected ground spectra of vegetation and bare ground during summers (2019–2021) using a PSR+ 3500 hand-held spectroradiometer and created a spectral library for this study. The Iterative Endmember Selection (IES) algorithm was used to optimize the spectral library and to select the most representative endmembers for simulation: birch, spruce, and gravel. We validated the simulated hyperspectral imagery by comparing it with available AVIRIS-NG images. The simulated image was visually and spectrally similar to the AVIRIS-NG image (RMSE of 0.03 and 0.02 for birch and spruce spectra, respectively). We applied the Random Forest image classification model to derive detailed vegetation maps from the simulated images. Our vegetation map showed an improvement of 33% in the map accuracy compared to the LANDFIRE EVT map. This study demonstrated an efficient and cost-effective approach to derive detailed vegetation maps at the Sentinel scene scale by simulating hyperspectral images in Google’s cloud environment. It offers a novel pathway to generate detailed vegetation and fuel maps for the whole boreal region of Alaska to aid effective forest and fire management.

A non-destructive estimation of chlorophyll-a and -b over different crops using airborne imaging spectroscopy observations

Non-destructive estimation of chlorophyll-a (Chl-a) and chlorophyll-b (Chl-b), is required for assessment and understanding of vegetation structural and functional dynamics. We have developed an empirical model for estimating Chl-a and Chl-b for different crops using Airborne Visible-Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data. Second derivative reflectance at 672 nm and 587 nm has shown maximum sensitivity towards Chl-a and Chl-b content, respectively. Separate linear models within a bootstrapped resampling framework were developed. During calibration, mean R2 of 0.46 and 0.51 were obtained for Chl-a and -b respectively. While during model validation, in both the cases mean R2 was found to be 0.3. This work shows the capability of airborne hyperspectral imaging data in segregating chlorophyll types using a simplistic modeling framework.