Earth's dynamic carbon cycle-unique among known planets—drives the growth of our food, sustains the oxygen we breathe, and generated the fossil fuels that powered the Industrial Revolution. The Carbon Investigation (Carbon-I) mission provides unprecedented clarity on this vital cycle by delivering high-resolution measurements of the main carbon-bearing molecules in the atmosphere: methane $\left(\text{CH}_{4}\right)$, carbon dioxide $\left(\text{CO}_{2}\right)$, and carbon monoxide (CO). Carbon-I directly addresses multiple NASA Decadal Survey objectives by closing the tropical data gap, focusing on natural emission sources (wetlands, permafrost, agriculture), measuring fire emissions, and providing robust source attribution across scales. The Carbon-I instrument builds on more than 40 years of imaging spectrometer technology development at the Jet Propulsion Laboratory. This Dyson imaging spectrometer provides a $\sim 100 ~\text{km}$ swath that delivers monthly global land coverage at $\leq 400 ~\mathrm{m}$ with $\sim 10$ times finer sampling for high-priority areas. This spatial detail is combined with high-resolution atmospheric spectroscopy in the $2.04-2.37 \mu ~\mathrm{m}$ shortwave infrared range to capture absorption lines of $\text{CH}_{4}, \text{CO}_{2}, \text{CO}$, and $\mathrm{N}_{2} \mathrm{O}$ (nitrous oxide) at a 0.7 nm spectral sampling interval combined with a spectral response function of $\leq 2.5 ~\text{nm}$ full width at half maximum. This ensures unambiguous discrimination of gas absorption lines and clear separation from surface albedo variations. Carbon-I is a NASA Earth System Explorer Mission Step 2 concept. The Carbon-I instrument flies on the LM400 bus, a proven midsized spacecraft architecture with significant flight heritage for all subsystems and components, including three-axis stabilization and low-jitter capabilities for high spatial sampling science needs. The payload data processor transmitter stores up to 8 Tbit of data, which is downlinked via a commercial Ka-band communication network, providing 1.46 Tb daily data $(\geq 100$ million spectra per day). Carbon-I delivers actionable trace gas results at the local to regional scale to a diverse set of stakeholders and scientific communities. Beyond its core objectives, Carbon-I also contributes to water-cycle research by measuring $\mathrm{H}_{2} \mathrm{O} / \text{HDO}$ to quantify evapotranspiration and study tropical land-atmosphere interactions. A Science Enhancement Option expands Carbon-I's scope to enhanced geologic mapping (critical minerals and rare earth elements), vegetation health (lignocellulose features for fire risk), and pollution monitoring (agricultural plastics, oil spills).

Sources of natural and anthropogenic metal contamination present significant health risks to humans and ecosystems. Yet, identifying the spatial extent of toxic metal abundance remains challenging due to their distributed nature. We demonstrate the potential for visible to shortwave infrared imaging spectroscopy to detect and map foliar metal concentrations (copper, iron, and zinc) across complex mountainous terrain. To model foliar metal chemistry across the study area, we developed and applied two independent models that pair hyperspectral reflectance data with corresponding in situ foliar elemental chemistry. These foliar metal estimates are used to assess metal hotspots and identify key spectral regions associated with elevated copper, iron, and zinc foliar concentrations. Spectral features in the visible, near-infrared, and shortwave infrared regions – particularly those linked with plant physiological stress – serve as indicators of elevated metal levels. This work underscores the potential utility of imaging spectroscopy in monitoring metal mobilization and toxicity in vegetated regions.

Declines in canopy water content (CWC) derived from visible/near-infrared imaging spectroscopy data have been linked to tree water stress and mortality, suggesting that CWC could be a valuable tool for monitoring ecological drought. CWC is sensitive to both the area of leaves in a tree canopy (leaf area index, LAI) and to the relative proportions of water mass and dry mass in the leaf (leaf water content, LWC).However, the relative contribution of each of these properties to CWC is not well understood. As a result, any single CWC measurement is likely underdetermined and challenging to reliably link to specific tree drought responses. Here we leverage a first-ofits-kind high-frequency imaging spectroscopy time series to explore how comparatively sensitive CWC is to LAI and LWC across both time and space in an oak savanna. Coincident with four imaging spectroscopy data acquisition flights, we measured LAI and LWC, as well as leaf water potential (a diagnostic of tree hydraulic stress). We found that LAI was the dominant control on CWC (R2 = 0.11-0.25), and that across space, CWC shows no sensitivity to LWC (R2 = 0.01-0.18). However, as tree water stress increased over time and LWC declined, CWC declined in step (average LWC vs. average CWC over time yield R2 = 0.89). Furthermore, we found that more negative leaf water potentials were associated with low LWC and reduced LAI, both across time and space, and therefore low CWC. Altogether, these results suggest that the main utility of CWC for monitoring tree water stress comes from its status as an integrated measure of both LAI and LWC, each of which may show coordinated or independent responses to drought that can differ across space and time.

Imaging spectroscopy technology is transforming the way Earth is viewed from space, with applications across diverse science communities. A global imaging spectrometer mission with Landsat-like spatial and temporal coverage could fully realize this potential.

Wildfires in wildland-urban interfaces (WUIs) are a growing concern due to their devastating impact on human communities and ecosystems. Low-latency impact assessment is critical for wildfire response, yet immediate access to fire-affected communities can be limited. Here, we demonstrate that unmixing char/ash fractional cover using imaging spectroscopy data can support rapid structural damage assessment. Using acquisitions over the 2025 Eaton Fire burn scar collected by the Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3), we demonstrate that a generic spectral endmember library is sufficient for binary structural damage classification between undamaged and destroyed buildings with an accuracy of 86.3%. Incorporating locally collected Eaton Fire endmembers into the library improves the accuracy by 1.3%. This demonstrates the feasibility of assessing structural damage from airborne imaging spectroscopy to rapidly inform post-fire response and recovery efforts in WUI communities.

We stand at the threshold of a transformative era in Earth observation, marked by space‐borne visible‐to‐shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter‐ and intra‐annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub‐seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High‐Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub‐seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open‐source processing methods and cloud‐based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high‐frequency time series data from space‐borne VSWIR imaging spectrometers.

Imaging spectrometers like NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) and the Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3) have similar instrument parameters and methane and CO2 mapping capability that enables direct attribution of observed plumes to the oil and gas, waste, and agriculture sectors. Onboard the International Space Station, EMIT can constrain methane and CO2 emissions over a significant portion of the Earth’s surface. With improved spatial resolution, the airborne AVIRIS-3 instrument enables quantification of smaller emissions sources that compliment EMIT observations from space. We provide an update of EMIT methane and CO2 observations to date and highlight examples from the oil and gas, waste, and agriculture sectors. For the first time, we present AVIRIS-3 methane and CO2 results. The fine spatial resolution of these instruments allows pinpointing of multiple emission sources in close proximity from different sectors, which is not possible with coarser spatial resolution instruments. These instruments offer the potential to improve understanding of greenhouse gas budgets, inform mitigation strategies, and in some cases lead to voluntary mitigation. In support of NASA’s Open Source Science Initiative, all EMIT data and greenhouse gas data products are available through the Land Processes Distributed Active Archive Center (LP DAAC) and code is open source. EMIT results are also available through the greenhouse gas applications online mapping tool (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gfases/) and U.S. Greenhouse Gas Center (https://earth.gov/ghgcenter/). Figure 1: Over 900 methane plume complexes observed by NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) are available through the EMIT greenhouse gas applications online mapping tool (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gfases/) and U.S. Greenhouse Gas Center (https://earth.gov/ghgcenter/).

Remote sensing holds promise for ecosystem-level monitoring of plant drought stress but is limited by uncertain linkages between physiological stress and remotely sensed metrics of water content. Here, we investigate the stability of relationships between water potential (Ψ) and water content (measured in situ and via repeat airborne VSWIR imaging) over diel, seasonal, and spatial variation in two xeric oak tree species. We also compare these field-based relationships with ones established in laboratory settings that might be used as calibration. Due to confounding physiological processes related to growth, both in situ and remotely sensed metrics lacked consistent relationships with stress when measured across space or through time. Relationships between water content and physiological drought stress measured over the growing season were stronger and more closely related to established laboratory-based drydown methods than those measured across space (i.e., between wet trees and dry trees). These results provide insight into the utility of "space for time" approaches in remote sensing and demonstrate both important limitations and the potential power of high temporal resolution remote sensing for detecting drought stress.

Spaceborne and airborne imaging spectrometers can identify methane (CH4) plumes and enable emission quantification and direct sectoral attribution necessary to better constrain methane emissions and inform mitigation strategies. We will show CH4 emission quantification results and accompanying uncertainty products for CH4 plume observations from NASA’s Earth surface Mineral dust source InvesTigation (EMIT) imaging spectrometer onboard the International Space Station, as well as the recently developed Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3). The differing spatial resolution and instrument sensitivity of the EMIT and AVIRIS-3 sensors are highly complementary for tiered CH4 plume detection and quantification. The large spatial coverage from EMIT allows us to identify and quantify previously unknown emissions from CH4 point sources across large regions of the Earth’s surface, while AVIRIS-3 has higher sensitivity and increased spatial resolution for characterizing CH4 emissions below EMIT’s detection limit. Building on a legacy of greenhouse gas retrievals first developed for airborne imaging spectrometers (e.g., AVIRIS, AVIRIS-NG), we use a matched filter approach to retrieve CH4 enhancements and the per-plume integrated mass enhancement (IME) method with windspeed data to estimate hourly CH4 emission rates. We take a two-pronged approach to validating our CH4 emission detection and quantification method: (1) an AVIRIS-3 CH4 controlled release experiment with multiple flow rates, and (2) evaluation of a simultaneous collection of AVIRIS-3 and EMIT in West Texas’ Permian Basin oil-and-gas producing region. This validation work will help provide confidence in EMIT’s plume quantification approach, which is important as imaging spectrometers are necessary for more comprehensive understanding of global CH4 point source emissions and greenhouse gas budgets, particularly in areas with limited reporting requirements. Lastly, the EMIT greenhouse gas portal (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gases/) is actively distributing methane data products in support of NASA’s Open Source Science Initiative and AVIRIS-3 data will soon be publicly available for interested decision-makers and users (e.g., U.S. Greenhouse Gas Center).

Abstract Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne‐field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.

Current biodiversity metrics derived from remote sensing data are typically applied to small local areas, require significant training data, and are not easily extensible globally. Here we propose the mathematical concept of intrinsic dimensionality (ID) as a method to quantify terrestrial vegetation variability without a need for in situ training data. We apply this technique to airborne imaging spectroscopy data from the Surface Biology and Geology High Frequency Time series (SHIFT) airborne campaign, with weekly overflights from February to May 2022 over a region in California stretching from Figueroa Mountain in the Los Padres National Forest to Point Conception and adjacent coastal areas. ID is considered in both spatial and temporal context—spatial ID represents spectral variability across a geographical region at a single time step, and temporal ID represents spectral variability over time for a single geographical location. Results show an encouraging and significant correlation between spatially calculated ID and in situ vegetation species richness data despite different spatial scales between the two ( p = 0.01). Spatial ID remained largely unchanged at each time step over the course of three months during the spring green‐up period when vegetation characteristics and spectral responses were changing rapidly (number of species remains unchanged even though spectra reflect phenological change over time). The temporal ID remained constant for pseudo‐invariant surfaces such as parking lots, roofs, and rock, but showed increased ID with time for trees, shrubs, and grasses. This robustness of spatial ID to seasonal change is desirable in any measure of species richness because it is insulated from changes in vegetation condition that are unrelated to plant species richness. Even though the spatial ID is consistent across acquisition dates, when considering the full time series (temporal ID), we find that subweekly sampling may be necessary to spectrally capture the full phenological cycle of certain vegetation types.

Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multi-temporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) collected as part of the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data were used to investigate flowering species' spectro-temporal variation and spatial distribution using spectral mixture residual (SMR), Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, and dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.

The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that has been operating onboard the International Space Station since July 2022. This article describes EMIT's on-orbit spectroradiometric calibration and validation. Accurate spectroscopy is vital to achieve consistent mapping results with orbital imaging spectrometers. EMIT takes a unique approach to this challenge, with just six optical elements, no shutter, and no onboard calibration systems. Its simple design focuses on uniformity and stability to enable vicarious spectroradiometric calibration. Our experiments demonstrate that this approach is successful, approaching the fidelity of manual field spectroscopy in some cases, and enabling new and more accurate products across diverse Earth science disciplines. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary solar-reflected surface signals. Its noise performance enables percent-level discrimination in the depths of mineral absorption features. In these aspects, EMIT satisfies the stringent performance needs for the next generation of VSWIR imaging spectrometers to observe the Earth's ecosystems, geology, and water resources.

Imaging spectrometers like EMIT and AVIRIS-3 have similar instrument parameters and methane and CO2 mapping capability that enables direct attribution of observed plumes to the oil and gas, waste, and agriculture sectors. Onboard the International Space Station, EMIT can constrain methane and CO2 emissions over a significant portion of the Earth’s surface. With improved spatial resolution, the airborne AVIRIS-3 instrument enables quantification of smaller emissions sources that compliment EMIT observations from space. These instruments offer the potential to improve understanding of greenhouse gas budgets, inform mitigation strategies, and in some cases lead to voluntary mitigation.

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The Earth Surface Mineral Dust Source Investigation (EMIT) acquires new observations of the Earth from a state-ofthe-art, optically fast F/1.8 visible to short wavelength infrared imaging spectrometer with high signal-to- noise ratio and excellent spectroscopic uniformity. EMIT was launched to the International Space Station from Cape Canaveral, Florida, on July 14, 2022 local time. The EMIT instrument is the latest in a series of more than 30 imaging spectrometers and testbeds developed at the Jet Propulsion Laboratory, beginning with the Airborne Imaging Spectrometer that first flew in 1982. EMIT's science objectives use the spectral signatures of minerals observed across the Earth's arid and semi-arid lands containing dust sources to update the soil composition of advanced Earth System Models (ESMs) to better understand and reduce uncertainties in mineral dust aerosol radiative forcing at the local, regional, and global scale, now and in the future. EMIT has begun to collect and deliver high-quality mineral composition determinations for the arid land regions of our planet. Over 1 billion high-quality mineral determinations are expected over the course of the one-year nominal science mission. Currently, detailed knowledge of the composition of the Earth's mineral dust source regions is uncertain and traced to less than 5,000 surface sample mineralogical analyses. The development of the EMIT imaging spectrometer instrumentation was completed successfully, despite the severe impacts of the COVID-19 pandemic. The EMIT Science Data System is complete and running with the full set of algorithms required. These tested algorithms are open source and will be made available to the broader community. These include calibration to measured radiance, atmospheric correction to surface reflectance, mineral composition determination, aggregation to ESM resolution, and ESM runs to address the science objectives. In this paper, the instrument characteristics, ground calibration, in-orbit performance, and early science results are reported.