Satellite Imaging Spectrometers Research Articles

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m:n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion.

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.

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.

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Abstract. Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.

Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing of acquisition and resolution of digital elevation models (DEM) that are used for topographic correction. We use the fine-resolution data (2 m) from the Airborne Coastal Observatory (ACO), an imaging spectroscopy and lidar remote sensing platform, to quantify the expected error from other airborne and satellite scale platforms. First, we describe our retrieval workflow - the Imaging Spectroscopy Snow and Ice Algorithm (ISSIA) that yields spatial retrievals of broadband albedo (BBA), optical grain size, and radiative forcing by light absorbing particles (RFLAP) using the coincident 2 m lidar DEM over mountain glaciers. We use two acquisitions over Place Glacier, British Columbia, Canada that represent typical spring and summer conditions as a baseline from which synthetic datasets were generated to represent data from coarser-scale imaging spectroscopy platforms. Each synthetic dataset was motivated by specific airborne and satellite platform scenarios and quantifies four sources of error: (1) DEM spatial resolution and elevation accuracy, (2) coincident vs non-coincident DEM collection, (3) imagery spatial resolution, and (4) spectral resolution. Use of a 30 m DEM introduced the most error in steep mountain terrain, which primarily increased error in the BBA and RFLAP retrievals. The timing of DEM acquisition mattered most for high-resolution imagery and in areas surrounding the glacier toe where mass loss occurred more rapidly. Coarse-resolution imagery did not fully capture spatial variability in surface properties and topography, which lowered variance and increased overall errors. Coarsening of spectral resolution from 5 nm to 10 nm had minimal impact on surface property retrieval and associated errors were independent of DEM or imagery resolution. A simple energy analysis showed that surface albedo error, due to topographic correction with coarse and non-coincident DEMs, resulted in melt rate errors ranging from 6 to 20% for airborne and 11–20% for satellite scenarios.

Recent Advances Toward Transparent Methane Emissions Monitoring: A Review.

Given that anthropogenic greenhouse gas (GHG) emissions must be immediately reduced to avoid drastic increases in global temperature, methane emissions have been placed center stage in the fight against climate change. Methane has a significantly larger warming potential than carbon dioxide. A large percentage of methane emissions are in the form of industry emissions, some of which can now be readily identified and mitigated. This review considers recent advances in methane detection that allow accurate and transparent monitoring, which are needed for reducing uncertainty in source attribution and evaluating progress in emissions reductions. A particular focus is on complementary methods operating at different scales with applications for the oil and gas industry, allowing rapid detection of large point sources and addressing inconsistencies of emissions inventories. Emerging airborne and satellite imaging spectrometers are advancing our understanding and offer new top-down assessment methods to complement bottom-up methods. Successfully merging estimates across scales is vital for increased certainty regarding greenhouse gas emissions and can inform regulatory decisions. The development of comprehensive, transparent, and spatially resolved top-down and bottom-up inventories will be crucial for holding nations accountable for their climate commitments.

What Is a Cloud? Toward a More Precise Definition

Abstract There are a couple of reasons to stimulate a discussion about the definition of clouds. One reason is that the American Meteorological Society (AMS) and the World Meteorological Organization (WMO) define clouds differently, and that in two aspects. AMS defines clouds as visible objects, and WMO as perceivable objects. Furthermore, AMS includes all minute particles independent of their nature and composition whereas WMO considers only such minute particles that consist of water, ice, or a mixture of both. Additionally, the so-called invisible or subvisible clouds are perceivable objects in the definition of WMO but not of AMS. Clouds can be observed by humans and be detected by active and passive sensors from ground and space. The detection limits of instruments span more than two orders of magnitude in visible optical thickness ranging from about 0.001 from satellite lidar (in case of horizontal and vertical averaging or 0.07 for single lidar shots) to about 0.3 to 0.45 for multispectral satellite imaging spectrometers. Human observers see clouds when their optical thickness is larger than about 0.03 at day and 0.05 at night. This paper gives a brief overview of cloud detection from ground and space and of the occurrence, characteristics, and impacts of subvisible clouds. Pros and cons of these definitions are discussed, followed by a proposal for a more precise definition. This definition is, like those of AMS and WMO, a one-parametric definition and does not cover all aspects of clouds.

Quantifying Global Power Plant Carbon Dioxide Emissions With Imaging Spectroscopy

AbstractAnthropogenic carbon dioxide (CO2) emissions dominate uncertainties in the global carbon budget. Global inventories, such as the National Greenhouse Gas Inventories, have latencies of 12–24 months and may not keep pace with rapidly changing infrastructure, particularly in the developing world. Our work reveals that airborne and satellite imaging spectrometers provide 3–30 m spatial resolution and accurate quantification of CO2 emissions at the facility scale. Examples from 17 coal and gas fired power plants across the United States demonstrate robust correlation and 21% agreement on average between our remotely sensed estimates and simultaneous in situ measured emissions. We highlight four examples of coal‐fired power plants in India, Poland, and South Korea, where we quantify significant carbon dioxide emissions from power plants where limited public emissions data exist. Leveraging previous work on methane (CH4) plume detection, we present a strategy to exploit joint CO2 and CH4 plume imaging to quantify carbon emissions across widely distributed industrial infrastructure, including facilities that co‐emit CO2 and CH4. We show an example of a coal operation, where we attribute 25% of greenhouse gas emissions to coal extraction (CH4) and the remaining 75% to energy generation (CO2). Satellite spectrometers could track high emitting coal‐fired power plants that collectively contribute to 60% or more of global coal CO2 emissions. Multiple revisits and coordinated targeting of these high emitting facilities by multiple spaceborne instruments will be key to reducing uncertainties in global anthropogenic CO2 emissions and supporting emissions mitigation strategies.

Methane Mapping with Future Satellite Imaging Spectrometers

This study evaluates a new generation of satellite imaging spectrometers to measure point source methane emissions from anthropogenic sources. We used the Airborne Visible and Infrared Imaging Spectrometer Next Generation(AVIRIS-NG) images with known methane plumes to create two simulated satellite products. One simulation had a 30 m spatial resolution with ~200 Signal-to-Noise Ratio (SNR) in the Shortwave Infrared (SWIR) and the other had a 60 m spatial resolution with ~400 SNR in the SWIR; both products had a 7.5 nm spectral spacing. We applied a linear matched filter with a sparsity prior and an albedo correction to detect and quantify the methane emission in the original AVIRIS-NG images and in both satellite simulations. We also calculated an emission flux for all images. We found that all methane plumes were detectable in all satellite simulations. The flux calculations for the simulated satellite images correlated well with the calculated flux for the original AVIRIS-NG images. We also found that coarsening spatial resolution had the largest impact on the sensitivity of the results. These results suggest that methane detection and quantification of point sources will be possible with the next generation of satellite imaging spectrometers.

DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence

To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three-dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-D RB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.

Mapping acidic mine waste with seasonal airborne hyperspectral imagery at varying spatial scales

Airborne imaging spectrometer (also known as hyperspectral) remote sensing has been widely used to characterize mineralogy on mine waste surfaces, which is useful for predicting potential sources of acidity and metal leaching. The most successful applications employ fine spatial resolution—20-m pixels or smaller. Future satellite imaging spectrometer sensors are proposed to provide coarser spatial resolution—30- to 60-m pixels. This study examined the ability to map minerals related to acid mine drainage with visible to shortwave infrared hyperspectral imagery at varying spatial scales (2-, 15-, 30-, 60-m pixels) at the Leviathan mine Superfund site, located in the Eastern Sierra Nevada. Mineral maps were produced using spectral angle mapper and matched filtering algorithms. The 15-m images provided comparable maps to the 2-m images. The 30- and 60-m images lost the ability to identify smaller features; however, they were still able to identify high- and low-priority remediation zones at least 75 m in width. Based on our results, we believe 30-m spatial resolution on a satellite hyperspectral sensor will be sufficient for identifying hazardous surfaces at larger mine waste sites and provide important reconnaissance information that can help prioritize detailed ground-based studies.

Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: I. Model formulation

Forest photosynthetic exchange rates at landscape scales have proven difficult to either accurately measure or estimate. Recent developments (Hall et al., 2011, 2008; Hilker et al., 2011a, 2010a) permit us to infer photosynthetic forest light use efficiency (ε) using multi-angle measurements of photochemical reflectance index (PRI) from the CHRIS/PROBA satellite imaging spectrometer, thus completing a long sought-after capability to remotely sense the major inputs driving gross primary production GPP i.e., ε and absorbed photosynthetically active radiation (APAR). In this first of two companion papers we introduce the theoretical underpinnings of an innovative approach that utilizes our recent developments to produce remotely sensed and spatially explicit maps of ε and GPP from space, and a data assimilation approach to extend the spatially explicit maps to diurnal, daily and annual time scales. We quantify GPP using the traditional radiation-limited approach of Monteith (1972); however we apply it in an innovative way. [I] Using CHRIS/PROBA we quantify ε at each satellite overpass for a 625km2 area at 30m resolution. [II] We use a novel physiologically-based multivariate function of APAR, temperature and water vapor pressure deficit model (described herein) and use it to down-regulate ε at 30 minute intervals. [III] We use the CHRIS/PROBA images of spatial variation in ε, and NDVI to quantify APAR, hence produce snapshots of GPP. We use a data assimilation approach to extend ε and GPP to temporally continuous and spatially contiguous maps of vegetation carbon uptake.In the second part of this study (Hilker et al., 2011b) we demonstrate and validate our approach over eight different forest flux tower sites in North America.