Optimal estimation for imaging spectrometer atmospheric correction

Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion

Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission

Abstract. Global patterns of snow darkening and melting, induced by grain metamorphism and the accumulation of small light-absorbing particles (LAPs), such as mineral dust, black carbon, volcanic ash, or algae cells, lead to an intensified radiative forcing and retreat of Earth's snow cover. Mapping and quantifying snow grain size and LAPs on both temporal and spatial scales are needed to improve the prediction of melt rates and their impacts on climate change. High-resolution visible-to-shortwave-infrared (VSWIR) imaging spectrometers herald a new era of passive spaceborne remote sensing, which will help to fulfill this objective. This technology provides measurements of reflected solar radiation in continuous spectral channels throughout the solar spectrum, allowing for the detection of narrow ice and LAP absorption bands. One of these instruments is NASA's Earth Surface Mineral Dust Source Investigation (EMIT) that was launched to the International Space Station (ISS) in July 2022. EMIT observations include snow cover in low- to mid-latitude mountainous regions, such as the western US, the Andes in South America, and high-mountain Asia. Accurate retrievals of snow surface properties, including grain size, liquid water content, and concentrations of mineral dust and algae, require precise, ideally joint accounting for atmospheric, topographic, and anisotropic effects in the reflected radiance. However, some methods still either neglect physical effects of the surface or utilize the surface reflectance as an intermediate non-physical quantity, in part without proper error propagation from atmospheric modeling and obtained from statistical modeling. Moreover, the term “surface reflectance” is often used with ambiguity in the literature, which instantly raises the question of whether we still need this quantity as a retrieval product. In this contribution, we present a novel forward model that couples the MODTRAN atmosphere radiative transfer code with a physics-based snow reflectance model that utilizes the multistream DISORT program. Our model allows us to estimate snow surface and atmosphere properties directly from measured radiance. We apply the approach to EMIT images from Patagonia, South America, and compare our results to the EMIT L2A products that retrieve surface reflectance as a free parameter. We find discrepancies in snow grain size of up to 200 µm and in dust mass mixing ratio of up to 75 µg g−1. Furthermore, we demonstrate differences in instantaneous LAP radiative forcing of up to 400 W m−2. We conclude that we still need reflectance but only if it is clearly defined and preferably modeled as a quantity within the forward model. These findings will be essential for the conception of retrieval algorithms for future orbital imaging spectroscopy missions, such as NASA's Surface Biology and Geology (SBG).

Improved surface temperature estimates with MASTER/AVIRIS sensor fusion

Quantum dots (QDs)-based photodetectors are promising alternatives to construct short-wave infrared (SWIR) image sensors at a low cost. Improving the photoresponse and scalability of QDs is important to enable their practical applications. In this work, we developed a complementary metal–oxide–semiconductor (CMOS)-compatible SWIR image sensor based on lead sulfide (PbS) quantum dots (QDs) utilizing a p–n homojunction photoconductor (PC) architecture. Through solution-phase ligand-exchanged PbS QDs treated by lead iodide (PbI2) combined with a solid-state ligand-exchanged PbS QDs thin film treated with ethanedithiol (EDT), a p–n homojunction was constructed, enabling more efficient separation of photogenerated carriers and significantly enhancing the photoresponse. This approach allows for the monolithic integration of solution-processed QDs with silicon readout integrated circuits (ROICs), eliminating the need for complex flip-chip bonding and facilitating the fabrication of large-scale (640 × 512) QDs imagers. Consequently, the homojunction-based QDs SWIR imager delivers performance with low noise and a high average detectivity of 1.7 × 1010 Jones, and the capability to capture high-resolution SWIR images highlights its potential for diverse applications.

Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements

Infrared (IR) spectroscopic imaging is potentially useful for digital histopathology as it provides spatially resolved molecular absorption spectra, which can subsequently yield useful information by powerful artificial intelligence methods. A typical analysis pipeline in using IR imaging data for chemical pathology often involves iterative processes of segmentation, evaluation, and analysis that necessitate rapid data exploration. Here, we present a fast, reliable, and intuitive method based on a phasor representation of spectra and discuss its unique applicability for IR imaging data. We simulate different features extant in IR spectra and discuss their influence on the phasor waveforms; similarly, we undertake IR image analysis in the transform space to understand spectral similarity and variance. We demonstrate the potential of phasor analysis for biomedical tissue imaging using a variety of samples, using fresh frozen surgical prostate resections and formalin-fixed paraffin-embedded breast cancer tissue microarray samples as model systems that span common histopathology practice. To demonstrate further generalizability of this approach, we apply the method to data from different experimental conditions─including standard (5.5 μm × 5.5 μm pixel size) and high-definition (1.1 μm × 1.1 μm pixel size) Fourier transform IR (FTIR) spectroscopic imaging using transmission and transflection modes. Quantitative segmentation results from our approach are compared to previous studies, showing good agreement and quick visualization. The presented method is rapid, easy to use, and highly capable of deciphering compositional differences, presenting a convenient tool for exploratory analysis of IR imaging data.

Abstract. The subgrid spatial variability of water vapor is an important geophysical parameter for modeling tropical convention and cloud processes in atmospheric models. This study maps sub-kilometer spatial structures in total atmospheric column water vapor with visible to shortwave infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA's AVIRIS-NG spectrometer enable the highest-resolution measurement to date of water vapor's spatial variability and scaling properties. We find second-order structure function scaling exponents consistent with prior studies of convective atmospheres. Airborne lidar data show that this total column measurement provides information about variability in the lower troposphere. We conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.

A consistency analysis of surface reflectance and leaf area index retrieval from overlapping clear-sky Landsat ETM+ imagery

With the development of remote sensing technology, shortwave infrared (SWIR) imaging technology has got more and more attention because of its ability through the fog and high spatial resolution. High spatial resolution SWIR imaging often requires high frame frequency. If the frame frequency is too high, it could cause the shortage of the image’s signal to noise ratio (SNR), seriously affecting image quality. In order to solve the contradiction between high spatial resolution and sensitivity, time delay and digital accumulation (TDDA) technology is proposed in this paper to improve system’s SNR and image quality. A prototype of SWIR imaging system based on a large format area InGaAs detector is designed, which demonstrates TDDA technology. The experiment results indicate that TDDA technology can increase system’s SNR of the square root of accumulative stage and improve image’s uniformity. The results in this paper are helpful for the improvement and application of high spatial resolution SWIR imaging technology.

Design of A prototype 128 × 128 ROIC array for 2.6 μm-wavelength SWIR image sensor applications

Field studies in Amazonia have found a relationship at continental scales between soil fertility and broad trends in forest structure and function. Little is known at regional scales, however, about how discrete patterns in forest structure or functional attributes map onto underlying edaphic or geological patterns. We collected airborne LiDAR (Light Detection and Ranging) data and VSWIR (Visible to Shortwave Infrared) imaging spectroscopy measurements over 600 km2 of northwestern Amazonian lowland forests. We also established 83 inventories of plant species composition and soil properties, distributed between two widespread geological formations. Using these data, we mapped forest structure and canopy reflectance, and compared them to patterns in plant species composition, soils, and underlying geology. We found that variations in soils and species composition explained up to 70% of variation in canopy height, and corresponded to profound changes in forest vertical profiles. We further found that soils and plant species composition explained more than 90% of the variation in canopy reflectance as measured by imaging spectroscopy, indicating edaphic and compositional control of canopy chemical properties. We last found that soils explained between 30% and 70% of the variation in gap frequency in these forests, depending on the height threshold used to define gaps. Our findings indicate that a relatively small number of edaphic and compositional variables, corresponding to underlying geology, may be responsible for variations in canopy structure and chemistry over large expanses of Amazonian forest.

Electromagnetic radiance acquired by sensors is distorted mainly by atmospheric absorbing and scattering. Atmospheric correction is required for quantitatively analysis of remote sensing information. Radiation transfer model based atmospheric correction usually needs some atmospheric parameters to be chosen and estimated reasonably in advance when atmospheric observation data is lacked. In our work, a radiometric calibration was applied on the satellite data using revised coefficients at first. Then several parameters were determined for the correction process, taking into account the earth's surface and atmospheric properties of the study area. Moreover, the atmospheric correction was implemented using 6S code and the surface reflectance was retrieved. Lastly, the influence of atmospheric correction on spectral response characteristics of different land covers was discussed in respects of the spectral response curve, NDVI and the classification process, respectively. The results showed that the reflectance of all land covers decreases evidently in three visible bands, but increases in the near-infrared and shortwave infrared bands after atmospheric correction. NDVI of land covers also increases obviously after atmospheric influence was removed, and NDVI derived from the surface reflectance is the highest comparing to that from the original digital number and the top of atmosphere reflectance. The accuracy of the supervised classification is improved greatly, which is up to 87.23%, after the atmospheric effect is corrected. Methods of the parameter determination can be used for reference in similar studies.

Future global Visible Shortwave Infrared Imaging Spectrometers, such as the Surface Biology and Geology (SBG) mission, will regularly cover the Earth's entire terrestrial land area. These missions need high fidelity atmospheric correction to produce consistent maps of terrestrial and aquatic ecosystem traits. However, estimation of surface reflectance and atmospheric state is computationally challenging, and the terabyte data volumes of global missions will exceed available processing capacity. This article describes how missions can overcome this bottleneck using the spatial continuity of atmospheric fields. Contemporary imaging spectrometers oversample atmospheric spatial variability, so it is not necessary to invert every pixel. Spatially sparse solutions can train local linear emulators that provide fast, exact inversions in their vicinity. We find that estimating the atmosphere at 200 m scales can outperform traditional atmospheric correction, improving speed by one to two orders of magnitude with no measurable penalty to accuracy. We validate performance with an airborne field campaign, showing reflectance accuracies with RMSE of 1.1% or better compared to ground measurements of diverse targets. These errors are statistically consistent with retrieval uncertainty budgets. Local emulators can close the efficiency gap and make rigorous model inversion algorithms feasible for global missions such as SBG.

Visible-shortwave infrared imaging spectrometers for deep space exploration need to meet the requirements of wide spectrum coverage, high spectral resolution and miniaturization. This chapter explores the achievement of higher and more consistent diffraction efficiency of the partition multiplexing convex grating in the visible-shortwave infrared (0.4–2.5 μm). The investigation of the diffraction efficiency indicates that when the two blaze angles of the convex grating, one between 5.6° and 6.5° and the other between 16.5° and 17.5°, the first-order diffraction efficiency would be over 25% at the visible-shortwave infrared band. The partition multiplexing convex grating with 408 L/mm, a curvature radius of 51.64 mm, an aperture of 30 mm, with the two blaze angles 6.3° and 17.3°, respectively, and the two antiblaze angles 66° and 56°, has been fabricated by holographic lithography—segmented scan ion beam etching.