Spectral Response Function Research Articles

A Study of the Method for Retrieving the Vegetation Index from FY-3D MERSI-II Data

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product’s quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS.

Intelligent synthesis of hyperspectral images from arbitrary web cameras in latent sparse space reconstruction

<abstract><p>Synthesizing hyperspectral images (HSI) from an ordinary camera has been accomplished recently. However, such computation models require detailed properties of the target camera, which can only be measured in a professional lab. This prerequisite prevents the synthesizing model from being installed on arbitrary cameras for end-users. This study offers a calibration-free method for transforming any camera into an HSI camera. Our solution requires no controllable light sources and spectrometers. Any consumer installing the program should produce high-quality HSI without the assistance of optical laboratories. Our approach facilitates a cycle-generative adversarial network (cycle-GAN) and sparse assimilation method to render the illumination-dependent spectral response function (SRF) of the underlying camera at the first part of the setup stage. The current illuminating function (CIF) must be identified for each image and decoupled from the underlying model. The HSI model is then integrated with the static SRF and dynamic CIF in the second part of the stage. The estimated SRFs and CIFs have been double-checked with the results by the standard laboratory method. The reconstructed HSIs have errors under 3% in the root mean square.</p></abstract>

Spectral-Learning-Based Transformer Network for the Spectral Super-Resolution of Remote-Sensing Degraded Images

Hyperspectral images (HSIs) are widely used as data formats for remote sensing. Correspondingly, the spectral super-resolution (SSR) technique used to generate high-spatial-resolution HSIs from high-spatial-resolution remote sensing multispectral images (MSIs) has emerged as a popular research topic owing to its high costs and hardware requirements. Generally, existing SSR methods obtain an HSI from the MSI of natural scenes. However, these methods barely learn the complex spectra found in remote sensing images and lack effective treatments for the degradation phenomenon in remote sensing scenes. In this study, a spectral-learning-based transformer network composed of a spectral-response-function (SRF)-guided multilevel feature extraction module (MFEM) and spectral nonlinear mapping learning module (NMLM) is cascaded. The NMLM uses different blocks to gradually learn spatial and spectral information from remote sensing images. Additionally, we focus on atmospheric effects and other factors that influence the generation of remote sensing images and design an MFEM to eliminate these effects. To augment the spectral dimension, an SRF is precisely added to the MFEM as a guide. Experimental results on the Obita Hyperspectral Satellites, Pavia Center, and Washington DC Mall datasets reveal that our method outperforms other state-of-the-art methods in terms of the root mean square error, mean relative absolute error, and relative root mean square error.

Effect analysis of spectral response function of microwave humidity and temperature sounder onboard the FY-3D satellite

Effect analysis of spectral response function of microwave humidity and temperature sounder onboard the FY-3D satellite

Effect of Axisymmetrical Spectral Response Function on Microwave Radiance Simulation of Quadruple-Sideband Channel

Effect of Axisymmetrical Spectral Response Function on Microwave Radiance Simulation of Quadruple-Sideband Channel

Hyperspectral Multispectral Image Fusion via Fast Matrix Truncated Singular Value Decomposition

Recently, methods for obtaining a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) and high spatial resolution multispectral image (HR-MSI) have become increasingly popular. However, most fusion methods require knowing the point spread function (PSF) or the spectral response function (SRF) in advance, which are uncertain and thus limit the practicability of these fusion methods. To solve this problem, we propose a fast fusion method based on the matrix truncated singular value decomposition (FTMSVD) without using the SRF, in which our first finding about the similarity between the HR-HSI and HR-MSI is utilized after matrix truncated singular value decomposition (TMSVD). We tested the FTMSVD method on two simulated data sets, Pavia University and CAVE, and a real data set wherein the remote sensing images are generated by two different spectral cameras, Sentinel 2 and Hyperion. The advantages of FTMSVD method are demonstrated by the experimental results for all data sets. Compared with the state-of-the-art non-blind methods, our proposed method can achieve more effective fusion results while reducing the fusing time to less than 1% of such methods; moreover, our proposed method can improve the PSNR value by up to 16 dB compared with the state-of-the-art blind methods.

Spatial-spectral dual path hyperspectral image super-resolution reconstruction network based on spectral response functions

Recently, hyperspectral image (HSI) super-resolution (SR) techniques based on deep learning have been actively developed. However, most hyperspectral image super-resolution reconstruction methods usually use all spectral bands simultaneously, leading to a mismatch of spectral properties between reconstructed HSI bands. Therefore, we proposed a new method of spatial-spectral dual path residual network (SGDPRN) based on spectral response function (SRF) to address the above problem. The SGDPRN is composed of the SRF guided grouping part, the spatial-spectral feature extraction part, and the final reconstruction part. Firstly, the reconstructed features for different spectral ranges are identified separately using SRF as a guide. Then, based on the grouping results, a spatial-spectral dual-path residual block is used to explore the spatial and spectral features by the designed parallel structure simultaneously. The spatial path is designed to extract sharp edges and realistic textures, and the spectral path is designed to model inter-spectral correlations to refine spectral features. At last, the third block of SGDPRN concatenates features of all groups and finishes the reconstruction of HSISR. QUST-1 satellite images have been applied in experiments, and the results showed that SGDPRN produced a higher peak signal to noise ratio, structural similarity metric, correlation coefficient, and lower spectral angle mapper, root mean square error than the other methods. This demonstrates that our method can effectively maintain the correlation of spectral bands while improving the spatial resolution.

The pre-launch on-ground characterization of Mars Multispectral Imager for Subsurface Studies (Ma_MISS) spectrometer for ExoMars rover mission: Spectral calibration.

The Ma_MISS spectrometer is integrated within the drilling system of the Rosalind Franklin ExoMars rover. This paper reports the on-ground calibration campaign performed on the spectrometer. Here, we focus on the spectral calibration of the instrument. The experimental setup used to carry out calibration is described, and the methods used for data processing and key parameters retrieval are explained. In particular, the spectral parameters such as (i) pixel central wavelengths, (ii) spectral response function, (iii) spectral resolution, (iv) sampling, and (v) range are determined. In a follow-up paper, the linearity and radiometric calibrations are described, while in De Sanctis etal. [Planet. Sci. J. 3, 142 (2022)], the validation of spectral measurements performed on synthetic and natural rock targets is presented.

Absolute radiometric calibration with lunar surface reflectance as reference for earth-observing VNIR camera

The absolute radiometric accuracy of earth-observing camera is crucial for the applications of natural resources, environment, agriculture and other industries. To continue the progress in this filed, a lunar surface reflectance based radiometric calibration approach is given in this paper. We chose IIM, M3, SP lunar models as references and GF-4 VNIR camera as sensor under calibrating. The lunar calibration sites were MS-2 site, Apollo-16 site and CE-3 site. The equivalent reflectance models of lunar were retrieved by Multiplying and integrating with the spectral response function of VNIR camera. Absolute radiometric calibrations with the equivalent reflectance models of lunar were carried out for 520–590nm, 630–690nm and 770–890nm spectral bands. The ground-based validation experiments were conducted with low, medium and high reflectance targets. The calibration accuracy was evaluated by comparing the relative errors of derived radiance after radiometric calibration with the benchmarks of TOA radiance transferred by in-situ measured reflectance. Lunar-based calibration models, lab and on-orbit filed-based models were used to compare the relative errors between proposed method and traditional way. The results showed that using IIM lunar model had better radiometric accuracy than other models, and SP model had the similar performance with traditional on-orbit filed-based model. The results indicated that using lunar to calibrate the earth-observing camera had the capability to improve the radiometric calibration accuracy.

Cryogenic Refocusing of an Ultrawide FOV Long-Wave Infrared Imaging Spectrometer in a Geostationary Orbit

Imaging spectrometers in a geostationary orbit have unique advantages for various applications ranging from atmospheric to ocean remote sensing. To increase remote sensing range, we developed an ultrawide FOV LWIR imaging spectrometer. We compared several spectrometers and picked the “Offner + center single prism” form, which can simplify the system structure and increase cryogenic adaptability, making it ideal for ultrawide FOV designs. However, to reduce background radiation in LWIR bands, the imaging spectrometer was cooled to 123 K, causing the image plane to deviate from the focal plane. Therefore, we propose a cryogenic refocusing method based on an inclined slit in this paper that employs the full width half maximum (FWHM) of the spectral response function (SRF) as the refocusing evaluation function. By analyzing the effect of deviation on FWHM, the refocusing principle is well explained. The slit is tilted to find the minimal function value, so the deviation amount is calculated using differences in FOVs corresponding to the minimum FWHM at different temperatures. Experiments show that the FWHM is nearly 2.1 pixels after refocusing and that the actual spectral resolution is within 120 nm after spectral calibration. The method reduced the design time and provided references for optical systems experiencing refocusing issues in remote sensing imaging.

Physical-Based Spatial-Spectral Deep Fusion Network for Chlorophyll-a Estimation Using MODIS and Sentinel-2 MSI Data

Satellite-derived Chlorophyll-a (Chl-a) is an important environmental evaluation indicator for monitoring water environments. However, the available satellite images either have a coarse spatial or low spectral resolution, which restricts the applicability of Chl-a retrieval in coastal water (e.g., less than 1 km from the shoreline) for large- and medium-sized lakes/oceans. Considering Lake Chaohu as the study area, this paper proposes a physical-based spatial-spectral deep fusion network (PSSDFN) for Chl-a retrieval using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 Multispectral Instrument (MSI) reflectance data. The PSSDFN combines residual connectivity and attention mechanisms to extract effective features, and introduces physical constraints, including spectral response functions and the physical degradation model, to reconcile spatial and spectral information. The fused and MSI data were used as input variables for collaborative retrieval, while only the MSI data were used as input variables for MSI retrieval. Combined with the Chl-a field data, a comparison between MSI and collaborative retrieval was conducted using four machine learning models. The results showed that collaborative retrieval can greatly improve the accuracy compared with MSI retrieval. This research illustrates that the PSSDFN can improve the estimated accuracy of Chl-a for coastal water (less than 1 km from the shoreline) in large- and medium-sized lakes/oceans.

TSC-1 Optical Payload Hyperspectral Imager Preliminary Design and Performance Estimation

The Thai Space Consortium aims at building capacities in space technologies and industries with the objective to develop satellites in Thailand. In this framework, the first Earth Observation satellite that will be developed by this consortium is called TSC-1. This satellite comprises a hyperspectral imager orbiting in a Sun-Synchronous Low-Earth Orbit at the altitude equal to 630 km. The optical payload is specified to provide data cubes with a Ground Sample Distance equal to 30 m, a swath equal to 30 km, a spectral resolution equal to 10 nm over the spectral domain from 400 nm to 1000 nm with a Signal-to-Noise Ratio (SNR) higher than 100. Firstly, we present the trade-off performed to select the design of the Front Telescope and the Spectrometer. Secondly, we describe the payload design and present the image quality, Modulation Transfer Function and distortion. Next, we establish the tolerance budget to estimate the performance of the optical system including manufacturing errors, assembly errors and stability of the mechanical structure. After that, we calculate the instrument’s spatial and spectral response functions and the contamination of the adjacent pixels due to the straylight. Finally, we estimate radiometric performance in both nadir pointing mode and forward motion compensation mode.

Wavelength calibration and spectral sensitivity correction of luminescence measurements for dosimetry applications: Method comparison tested on the IR-RF of K-feldspar

Spectroscopic investigations provide important insights into the composition of luminescence emissions relevant to trapped-charge dating of sediments. Accurate wavelength calibration and a correction for the wavelength-dependent detection efficiency of the spectrometer system are crucial to ensure the correct spectrum interpretation and allow for its comparison with those obtained from other systems. However, to achieve an accurate detection efficiency correction, it is necessary to obtain the device-specific spectral response function (SRF). Here, we compare two SRF approximation methods by using either a calibration lamp of known irradiance or calculating the product of efficiency curves provided by the manufacturers of all known optical elements. We discuss the results using radiofluorescence (RF) measurements of two K-feldspar samples as an example. Feldspar infra-red (IR) RF spectra are known to be composed of several overlapping emissions, whose variation with sample mineralogy is still poorly understood and requires more extensive investigations. We find that both methods of sensitivity correction yield broadly similar results. However, the observed differences can alter a spectrum's interpretation. For example, we observe that after peak deconvolution the maximum signal wavelength of the IR-RF peak used for dating applications differs by ∼3–13 nm between the two methods, depending on sample and diffraction grating. We recommend using calibration lamps to determine a device's SRF but highlight the need to consider issues such as higher-order signals in the choice of filters to establish the SRF's reliable wavelength range. Additionally, we find that a simple and inexpensive fluorescent white light yields an acceptable wavelength calibration comparable to that obtained from a specialized light source.

Water Quality Retrieval from ZY1-02D Hyperspectral Imagery in Urban Water Bodies and Comparison with Sentinel-2

Non-optically active water quality parameters in water bodies are important evaluation indicators in monitoring urban water quality. Over the past years, satellite remote sensing techniques have increasingly been used to assess different types of substances in urban water bodies. However, it is challenging to retrieve accurate data for some of the non-optically active water quality parameters from satellite images due to weak spectral characteristics. This study aims to examine the potential of ZY1-02D hyperspectral images in retrieving non-optical active water quality parameters, including dissolved oxygen (DO), permanganate index (CODMn), and total phosphorus (TP) in urban rivers and lakes. We first simulated the in situ measured reflectance to the satellite equivalent reflectance using the ZY1-02D and Sentinel-2 spectral response function. Further, we used four machine learning models to compare the retrieval performance of these two sensors with different bandwidths. The mean absolute percentage errors (MAPE) are 24.28%, 18.44%, and 37.04% for DO, CODMn, and TP, respectively, and the root mean square errors (RMSE) are 1.67, 0.96, and 0.07 mg/L, respectively. Finally, we validated the accuracy and consistency of aquatic products retrieved from ZY1-02D and Sentinel-2 images. The remote sensing reflectance (Rrs) products of ZY1-02D are slightly overestimated compared to Sentinel-2 Rrs. ZY1-02D has high accuracy and consistency in mapping CODMn products in urban water. The results show the potential of ZY1-02D hyperspectral images in mapping non-optically active water quality parameters.

Transfer-learning-based cloud detection for Zhuhai-1 satellite hyperspectral imagery

The Zhuhai-1 hyperspectral satellite can simultaneously obtain spectral information in 32 spectral bands and effectively obtain accurate information on land features through integrated hyperspectral observations of the atmosphere and land, while the presence of clouds can contaminate remote sensing images. To improve the utilization rate of hyperspectral images, this study investigates the cloud detection method for hyperspectral satellite data based on the transfer learning technique, which can obtain a model with high generalization capability with a small training sample size. In this study, for the acquired Level-1B products, the top-of-atmosphere reflectance data of each band are obtained by using the calibration coefficients and spectral response functions of the product packages. Meanwhile, to eliminate the data redundancy between hyperspectral bands, the data are downscaled using the principal component transformation method, and the top three principal components are extracted as the sample input data for model training. Then, the pretrained VGG16 and ResNet50 weight files are used as the backbone network of the encoder, and the model is updated and trained again using Orbita hyperspectral satellite (OHS) sample data to fine-tune the feature extraction parameters. Finally, the cloud detection model is obtained. To verify the accuracy of the method, the multi-view OHS images are visually interpreted, and the cloud pixels are sketched out as the baseline data. The experimental results show that the overall accuracy of the cloud detection model based on the Resnet50 backbone network can reach 91%, which can accurately distinguish clouds from clear sky and achieve high-accuracy cloud detection in hyperspectral remote sensing images.

The Propagation of Coherent Waves Across Multiple Solar Magnetic Pores

Solar pores are efficient magnetic conduits for propagating magnetohydrodynamic wave energy into the outer regions of the solar atmosphere. Pore observations often contain isolated and/or unconnected structures, preventing the statistical examination of wave activity as a function of the atmospheric height. Here, using high-resolution observations acquired by the Dunn Solar Telescope, we examine photospheric and chromospheric wave signatures from a unique collection of magnetic pores originating from the same decaying sunspot. Wavelet analysis of high-cadence photospheric imaging reveals the ubiquitous presence of slow sausage-mode oscillations, coherent across all photospheric pores through comparisons of intensity and area fluctuations, producing statistically significant in-phase relationships. The universal nature of these waves allowed an investigation of whether the wave activity remained coherent as they propagate. Utilizing bisector Doppler velocity analysis of the Ca ii 8542 Å line, alongside comparisons of the modeled spectral response function, we find fine-scale 5 mHz power amplification as the waves propagate into the chromosphere. Phase angles approaching zero degrees between co-spatial line depths spanning different line depths indicate standing sausage modes following reflection against the transition region boundary. Fourier analysis of chromospheric velocities between neighboring pores reveals the annihilation of the wave coherency observed in the photosphere, with examination of the intensity and velocity signals from individual pores indicating they behave as fractured waveguides, rather than monolithic structures. Importantly, this work highlights that wave morphology with atmospheric height is highly complex, with vast differences observed at chromospheric layers, despite equivalent wave modes being introduced into similar pores in the photosphere.

Assessment of Sentinel-2-MSI Atmospheric Correction Processors and In Situ Spectrometry Waters Quality Algorithms

The validation of algorithms developed from in situ reflectance to estimate water quality variables has the challenge of atmospheric correction (AC) when applied to satellite images. Estimating water quality variables from satellite images requires an accurate estimation of remote sensing reflectances (Rrs) which vary according to the AC applied. Validation processes for both Rrs and water quality algorithms were carried out, relating the in situ Rrs (convoluted to Sentinel-2-MSI spectral response function) with the satellite Rrs coming from different ACs (C2RCC, C2X, C2XC, and Polymer), and also relating the in situ water quality variable data with estimated water quality variable values, applying the water quality algorithms to the Rrs obtained for each AC. Regarding the Rrs validation results, the best ACs tested in this work were C2XC and Polymer. Regarding the water quality algorithm validation, the best results were also obtained using C2XC and Polymer Rrs. The results demonstrate the usefulness of the water quality algorithms developed from in situ reflectances since they are not specific to an AC and can be used with any processor.

Identifying distinct plastics in hyperspectral experimental lab-, aircraft-, and satellite data using machine/deep learning methods trained with synthetically mixed spectral data

The growing production and use of plastics are becoming a serious progressive issue and people pay increasing attention to the effects of plastics on ecosystems and human health. The availability of hyperspectral data from space sensors inspired us to study the feasibility to detect and identify different types of plastics in aircraft -, Goafen-5 (GF-5) and PRISMA satellite data by means of deep -, and machine learning models trained with spectral signatures. In this context, various inhouse and public spectral libraries are used to create a comprehensive database with mixed pixels of different plastic and non-plastic materials. The endmembers of plastic types involved in this study are polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET) and polystyrene (PS), covering 95% of the global production. Additionally, some important varieties of industrial plastics types such as acrylonitrile butadiene styrene (ABS), ethylene vinyl acetate (EVA), polyamide (PA), polycarbonate (PC), and polymethyl methacrylate (PMMA) were included in the investigations. Different samples with varying optical properties (color, brightness, transmissivity) have been selected for each plastic type. As non-plastic materials we have chosen spectra of vegetation, rocks, soils and minerals contained in the public US libraries (ECOSTRESS and USGS). The number of spectra for the training of the deep learning and machine learning models was enlarged by a random linear mixing method and the resulting database was separated into a training and a test group for subsequent multi-label classification. Algorithms selected are a convolutional neural network (CNN), random forest (RF) and support vector machine (SVM). To investigate the transferability to any hyperspectral image data obtained by air-, and spacecraft sensors, we opted for a unification of the spectral response functions (SRF) and the spectral sampling intervals of all data. Validation is accomplished based on the test group of the spectral database, and tested by controlled laboratory and aircraft experiments recorded over surfaces with varying background materials. Results are further analyzed for the influence of different noise quantities and abundance levels. The performance of the three models is roughly balanced for the validation of the spectral data with an overall accuracy of 97%, 96%, and 95% for the CNN, RF, and SVM, models respectively. In the controlled lab experiments, various accuracy indicators, such as the recall rates and the comprehensive metrics F1-score, OA, and Kappa suggest the RF classifier as the most robust one, followed by the SVM and CNN models. As for the evaluation of the aircraft data from controlled experiments, the RF further outperforms the other two models, behaving most robustly and reliably against conditions with unknown plastics and unknown background surfaces. Thus, the RF was used to classify the ten types of plastics mentioned above in one GF-5 and two PRISMA satellite recordings of the same area. In comparison of both sensor systems, the RF produced high quality and transferable results for detecting plastic mainly related to greenhouses, sport fields, photovoltaic constructions and industrial sites that are discussed in detail in this paper.

Assessment and correction of radiometric calibration for the medium resolution spectral imager on FY-3C

As a keystone instrument on the Fengyun-3C (FY-3C) satellite, the medium resolution spectral imager (MERSI) provides global coverage of top-of-atmosphere radiances for a broad range of scientific studies of the earth system. Based on the onboard measurements of the blackbody (BB) and deep space view (SV), the modified radiometric calibration equation was first given for the MERSI thermal emission band. The accuracy of the radiometric calibration was evaluated by comparing the average irradiance corresponding to the spectral response function and the irradiance corresponding to the wavelength, whose relative error was <0.40 % . Moreover, the BB-derived brightness temperature TR calculated by the BB irradiance shows a systematic error relative to the BB equivalent temperature TE obtained by the voltage code values of seven platinum resistors, and the value of TR was lower 0.25 K than that of TE. To further verify the validity of the calibration coefficient, the influences of the BB temperature, wavelength, and emissivity offsets on the derived irradiance and temperature of the earth were analyzed in detail. For the BB temperature changing by 0.10 K, the absolute values of the earth irradiance together with its equivalent temperature were 0.01 Wm − 2 sr − 1 μm − 1 and 0.10 K, respectively, whose relative errors were about 0.16% and 0.40%. When the BB wavelength and emissivity changing by 0.1 μm and 0.005, the variations of the earth irradiance were 0.02 and 0.04 Wm − 2 sr − 1 μm − 1, respectively, whereas the relative errors of the equivalent temperature were 0.07% and 0.11%. These works provided an important theoretical guidance for the satellite data assimilations along with the quantitative remote sensing applications.

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.