The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Predicting key water stress indicators of Eucalyptus viminalis and Callitris rhomboidea using high-resolution visible to short-wave infrared spectroscopy

Drought is one of the main factors contributing to tree mortality worldwide and drought events are set to become more frequent and intense in the face of a changing climate. Quantifying water stress of forests is crucial in predicting and understanding their vulnerability to drought-induced mortality. Here, we explore the use of high-resolution spectroscopy in predicting water stress indicators of two native Australian tree species, Callitris rhomboidea and Eucalyptus viminalis. Specific spectral features and indices derived from leaf-level spectroscopy were assessed as potential proxies to predict leaf water potential (Ψleaf), equivalent water thickness (EWT) and fuel moisture content (FMC) in a dedicated laboratory experiment. New spectral indices were identified that enabled very high confidence linear prediction of Ψleaf for both species (R2 > 0.85) with predictive capacity increasing when accounting for a breakpoint in the relationships using segmented regression (E. viminalis, R2 > 0.89; C. rhomboidea, R2 > 0.87). EWT and FMC were also linearly predicted to a high accuracy (E. viminalis, R2 > 0.90; C. rhomboidea, R2 > 0.80). This study highlights the potential of spectroscopy as a tool for predicting measures of plant water noninvasively, enabling broader applications for monitoring and managing plant water stress.

Characterizing and Implementing the Hamamatsu C12880MA Mini-Spectrometer for Near-Surface Reflectance Measurements of Inland Waters

In recent decades, inland water remote sensing has seen growing interest and very strong development. This includes improved spatial resolution, increased revisiting times, advanced multispectral sensors and recently even hyperspectral sensors. However, inland waters are more challenging than oceanic waters due to their higher complexity of optically active constituents and stronger adjacency effects due to their small size and nearby vegetation and built structures. Thus, bio-optical modeling of inland waters requires higher ground-truthing efforts. Large-scale ground-based sensor networks that are robust, self-sufficient, non-maintenance-intensive and low-cost could assist this otherwise labor-intensive task. Furthermore, most existing sensor systems are rather expensive, precluding their employability. Recently, low-cost mini-spectrometers have become widely available, which could potentially solve this issue. In this study, we analyze the characteristics of such a mini-spectrometer, the Hamamatsu C12880MA, and test it regarding its application in measuring water-leaving radiance near the surface. Overall, the measurements performed in the laboratory and in the field show that the system is very suitable for the targeted application.

Detecting rare earth elements using EnMAP hyperspectral satellite data: a case study from Mountain Pass, California

Rare earth elements (REEs) exhibit diagnostic absorption features in the visible-near infrared region, enabling their detection and identification via spectroscopic methods. Satellite-based remote sensing mapping of REEs, however, has not been attainable so far due to the necessity for high-quality hyperspectral data to resolve their narrow absorption features. This research leverages EnMAP hyperspectral satellite data to map REEs in Mountain Pass, California-a mining area known to host bastnaesite-Ce ore in sövite and beforsite carbonatites. By employing a polynomial fitting technique to characterize the diagnostic absorption features of Neodymium (Nd) at ∼740 and ∼800 nm, the surface occurrence of Nd was successfully mapped at a 30m pixel resolution. The relative abundance of Nd was represented using the continuum-removed area of the 800 nm feature. The resulting map, highlighting hundreds of anomalous pixels, was validated through laboratory spectroscopy, surface geology, and high-resolution satellite imagery. This study marks a major advancement in REE exploration, demonstrating for the first time, the possibility of directly detecting Nd in geologic environments using the EnMAP hyperspectral satellite data. This capability can offer a fast and cost-effective method for screening Earth's surfaces for REE signature, complementing the existing exploration portfolio and facilitating the discovery of new resources.

Development of an Imaging Spectrometer with a High Signal-to-Noise Ratio Based on High Energy Transmission Efficiency for Soil Organic Matter Detection

Hyperspectral detection of the change rate of organic matter content in agricultural remote sensing requires a high signal-to-noise ratio (SNR). However, due to the large number and efficiency limitation of the components, it is difficult to improve the SNR. This study uses high-efficiency convex grating with a diffraction efficiency exceeding 50% across the 360-850 nm range, a back-illuminated Complementary Metal Oxide Semiconductor (CMOS) detector with a 95% efficiency in peak wavelength, and silver-coated mirrors to develop an imaging spectrometer for detecting soil organic matter (SOM). The designed system meets the spectral resolution of 10 nm in the 360-850 nm range and achieves a swath of 100 km and a spatial resolution of 100 m at an orbital height of 648.2 km. This study also uses the basic structure of Offner with fewer components in the design and sets the mirrors of the Offner structure to have the same sphere, which can achieve the rapid adjustment of the co-standard. This study performs a theoretical analysis of the developed Offner imaging spectrometer based on the classical Rowland circular structure, with a 21.8 mm slit length; simulates its capacity for suppressing the +2nd-order diffraction stray light with the filter; and analyzes the imaging quality after meeting the tolerance requirements, which is combined with the surface shape characteristics of the high-efficiency grating. After this test, the grating has a diffraction efficiency above 50%, and the silver-coated mirrors have a reflection value above 95% on average. Finally, the laboratory tests show that the SNR over the waveband exceeds 300 and reaches 800 at 550 nm, which is higher than some current instruments in orbit for soil observation. The proposed imaging spectrometer has a spectral resolution of 10 nm, and its modulation transfer function (MTF) is greater than 0.23 at the Nyquist frequency, making it suitable for remote sensing observation of SOM change rate. The manufacture of such a high-efficiency broadband grating and the development of the proposed instrument with high energy transmission efficiency can provide a feasible technical solution for observing faint targets with a high SNR.

Quantifying NOx point sources with Landsat and Sentinel-2 satellite observations of NO2 plumes

We show that the Landsat and Sentinel-2 satellites can detect NO2 plumes from large point sources at 10 to 60 m pixel resolution in their blue and ultrablue bands. We use the resulting NO2 plume imagery to quantify nitrogen oxides (NOx) emission rates for several power plants in Saudi Arabia and the United States, including a 13-y analysis of 132 Landsat plumes from Riyadh power plant 9 from 2009 through 2021. NO2 in the plumes initially increases with distance from the source, likely reflecting recovery from ozone titration. The fine pixel resolutions of Landsat and Sentinel-2 enable separation of individual point sources and stacks, including in urban background, and the long records enable examination of multidecadal emission trends. Our inferred NOx emission rates are consistent with previous estimates to within a precision of about 30%. Sources down to ~500 kg h-1 can be detected over bright, quasi-homogeneous surfaces. The 2009 to 2021 data for Riyadh power plant 9 show a strong summer peak in emissions, consistent with increased power demand for air conditioning, and a marginal slow decrease following the introduction of Saudi Arabia's Ambient Air Standard 2012.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

Monitoring vegetation- and geodiversity with remote sensing and traits

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Semantic segmentation of methane plumes with hyperspectral machine learning models

Methane is the second most important greenhouse gas contributor to climate change; at the same time its reduction has been denoted as one of the fastest pathways to preventing temperature growth due to its short atmospheric lifetime. In particular, the mitigation of active point-sources associated with the fossil fuel industry has a strong and cost-effective mitigation potential. Detection of methane plumes in remote sensing data is possible, but the existing approaches exhibit high false positive rates and need manual intervention. Machine learning research in this area is limited due to the lack of large real-world annotated datasets. In this work, we are publicly releasing a machine learning ready dataset with manually refined annotation of methane plumes. We present labelled hyperspectral data from the AVIRIS-NG sensor and provide simulated multispectral WorldView-3 views of the same data to allow for model benchmarking across hyperspectral and multispectral sensors. We propose sensor agnostic machine learning architectures, using classical methane enhancement products as input features. Our HyperSTARCOP model outperforms strong matched filter baseline by over 25% in F1 score, while reducing its false positive rate per classified tile by over 41.83%. Additionally, we demonstrate zero-shot generalisation of our trained model on data from the EMIT hyperspectral instrument, despite the differences in the spectral and spatial resolution between the two sensors: in an annotated subset of EMIT images HyperSTARCOP achieves a 40% gain in F1 score over the baseline.

A new approach to derive productivity of tropical forests using radar remote sensing measurements

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.

Effect of Spatial and Spectral Scaling on Joint Characterization of the Spectral Mixture Residual: Comparative Analysis of AVIRIS and WorldView-3 SWIR for Geologic Mapping in Anza-Borrego Desert State Park

A geologic map is both a visual depiction of the lithologies and structures occurring at the Earth's surface and a representation of a conceptual model for the geologic history in a region. The work needed to capture such multifaced information in an accurate geologic map is time consuming. Remote sensing can complement traditional primary field observations, geochemistry, chronometry, and subsurface geophysical data in providing useful information to assist with the geologic mapping process. Two novel sources of remote sensing data are particularly relevant for geologic mapping applications: decameter-resolution imaging spectroscopy (spectroscopic imaging) and meter-resolution multispectral shortwave infrared (SWIR) imaging. Decameter spectroscopic imagery can capture important mineral absorptions but is frequently unable to spatially resolve important geologic features. Meter-resolution multispectral SWIR images are better able to resolve fine spatial features but offer reduced spectral information. Such disparate but complementary datasets can be challenging to integrate into the geologic mapping process. Here, we conduct a comparative analysis of spatial and spectral scaling for two such datasets: one Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS-classic) flightline, and one WorldView-3 (WV3) scene, for a geologically complex landscape in Anza-Borrego Desert State Park, California. To do so, we use a two-stage framework that synthesizes recent advances in the spectral mixture residual and joint characterization. The mixture residual uses the wavelength-explicit misfit of a linear spectral mixture model to capture low variance spectral signals. Joint characterization utilizes nonlinear dimensionality reduction (manifold learning) to visualize spectral feature space topology and identify clusters of statistically similar spectra. For this study area, the spectral mixture residual clearly reveals greater spectral dimensionality in AVIRIS than WorldView (99% of variance in 39 versus 5 residual dimensions). Additionally, joint characterization shows more complex spectral feature space topology for AVIRIS than WorldView, revealing information useful to the geologic mapping process in the form of mineralogical variability both within and among mapped geologic units. These results illustrate the potential of recent and planned imaging spectroscopy missions to complement high-resolution multispectral imagery-along with field and lab observations-in planning, collecting, and interpreting the results from geologic field work.

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Spatial response resampling (SR2): Accounting for the spatial point spread function in hyperspectral image resampling

With the increased availability of hyperspectral imaging (HSI) data at various scales (0.03-30 m), the role of simulation is becoming increasingly important in data analysis and applications. There are few commercially available tools to spatially degrade imagery based on the spatial response of a coarser resolution sensor. Instead, HSI data are typically spatially degraded using nearest neighbor, pixel aggregate or cubic convolution approaches. Without accounting for the spatial response of the simulated sensor, these approaches yield unrealistically sharp images. This article describes the spatial response resampling (SR²) workflow, a novel approach to degrade georeferenced raster HSI data based on the spatial response of a coarser resolution sensor. The workflow is open source and widely available for personal, academic or commercial use with no restrictions. The importance of the SR² workflow is shown with three practical applications (data cross-validation, flight planning and data fusion of separate VNIR and SWIR images).•The SR² workflow derives the point spread function of a specified HSI sensor based on nominal data acquisition parameters (e.g., integration time, altitude, speed), convolving it with a finer resolution HSI dataset for data simulation.•To make the workflow approachable for end users, we provide a MATLAB function that implements the SR² methodology.

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s

Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade-study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas

Spaceborne imaging spectroscopy is a highly promising data source for all agricultural management and research disciplines that require spatio-temporal information on crop properties. Recently launched science-driven missions, such as the Environmental Mapping and Analysis Program (EnMAP), deliver unprecedented data from the Earth's surface. This new kind of data should be explored to develop robust retrieval schemes for deriving crucial variables from future routine missions. Therefore, we present a workflow for inferring crop carbon content (Carea ), and aboveground dry and wet biomass (AGBdry , AGBfresh ) from EnMAP data. To achieve this, a hybrid workflow was generated, combining radiative transfer modeling (RTM) with machine learning regression algorithms. The key concept involves: (1) coupling the RTMs PROSPECT-PRO and 4SAIL for simulation of a wide range of vegetation states, (2) using dimensionality reduction to deal with collinearity, (3) applying a semi-supervised active learning technique against a 4-years campaign dataset, followed by (4) training of a Gaussian process regression (GPR) machine learning algorithm and (5) validation with an independent in situ dataset acquired during the ESA Hypersense experiment campaign at a German test site. Internal validation of the GPR-Carea and GPR-AGB models achieved coefficients of determination (R 2) of 0.80 for Carea and 0.80, 0.71 for AGBdry and AGBfresh , respectively. The mapping capability of these models was successfully demonstrated using airborne AVIRIS-NG hyperspectral imagery, which was spectrally resampled to EnMAP spectral properties. Plausible estimates were achieved over both bare and green fields after adding bare soil spectra to the training data. Validation over green winter wheat fields generated reliable estimates as suggested by low associated model uncertainties (< 40%). These results suggest a high degree of model reliability for cultivated areas during active growth phases at canopy closure. Overall, our proposed carbon and biomass models based on EnMAP spectral sampling demonstrate a promising path toward the inference of these crucial variables over cultivated areas from future spaceborne operational hyperspectral missions.

On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2

The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

Shortwave infrared single-pixel spectral imaging based on a GSST phase-change metasurface

Shortwave infrared (SWIR) spectral imaging obtains spectral fingerprints corresponding to overtones of molecular vibrations invisible to conventional silicon-based imagers. However, SWIR imaging is challenged by the excessive cost of detectors. Single-pixel imaging based on compressive sensing can alleviate the problem but meanwhile presents new difficulties in spectral modulations, which are prerequisite in compressive sampling. In this work, we theoretically propose a SWIR single-pixel spectral imaging system with spectral modulations based on a Ge₂Sb₂Se₄Te₁ (GSST) phase-change metasurface. The transmittance spectra of the phase-change metasurface are tuned through wavelength shifts of multipole resonances by varying crystallinities of GSST, validated by the multipole decompositions and electromagnetic field distributions. The spectral modulations constituted by the transmittance spectra corresponding to the 11 phases of GSST are sufficient for the compressive sampling on the spectral domain of SWIR hyperspectral images, indicated by the reconstruction in false color and point spectra. Moreover, the feasibility of optimization on phase-change metasurface via coherence minimization is demonstrated through the designing of the GSST pillar height. The concept of spectral modulation with phase-change metasurface overcomes the static limitation in conventional modulators, whose integratable and reconfigurable features may pave the way for high-efficient, low-cost, and miniaturized computational imaging based on nanophotonics.

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Remote sensing techniques are increasingly used for studying ecosystem dynamics, delivering spatially explicit information on the properties of Earth over large spatial and multi-decadal temporal extents. Yet, there is still a gap between the more technology-driven development of novel remote sensing techniques and their applications for studying ecosystem dynamics. Here, I review the existing literature to explore how addressing these gaps might enable recent methods to overcome longstanding challenges in ecological research. First, I trace the emergence of remote sensing as a major tool for understanding ecosystem dynamics. Second, I examine recent developments in the field of remote sensing that are of particular importance for studying ecosystem dynamics. Third, I consider opportunities and challenges for emerging open data and software policies and suggest that remote sensing is at its most powerful when it is theoretically motivated and rigorously ground-truthed. I close with an outlook on four exciting new research frontiers that will define remote sensing ecology in the upcoming decade.

Using satellites to uncover large methane emissions from landfills

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Here, we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfills. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this approach, we detect and analyze strongly emitting landfills (3 to 29 t hour^-1) in Buenos Aires, Delhi, Lahore, and Mumbai. Using TROPOMI data in an inversion, we find that city-level emissions are 1.4 to 2.6 times larger than reported in commonly used emission inventories and that the landfills contribute 6 to 50% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane superemitters at the facility level.

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

A Newly Developed Algorithm for Cloud Shadow Detection—TIP Method

The masking of cloud shadows in optical satellite imagery is an important step in automated processing chains. A new method (the TIP method) for cloud shadow detection in multi-spectral satellite images is presented and compared to current methods. The TIP method is based on the evaluation of thresholds, indices and projections. Most state-of-the-art methods solemnly rely on one of these evaluation steps or on a complex working mechanism. Instead, the new method incorporates three basic evaluation steps into one algorithm for easy and accurate cloud shadow detection. Furthermore the performance of the masking algorithms provided by the software packages ATCOR (“Atmospheric Correction”) and PACO (“Python-based Atmospheric Correction”) is compared with that of the newly implemented TIP method on a set of 20 Sentinel-2 scenes distributed over the globe, covering a wide variety of environments and climates. The algorithms incorporated in each piece of masking software use the class of cloud shadows, but they employ different rules and class-specific thresholds. Classification results are compared to the assessment of an expert human interpreter. The class assignment of the human interpreter is considered as reference or “truth”. The overall accuracies for the class cloud shadows of ATCOR and PACO (including TIP) for difference areas of the selected scenes are 70.4% and 76.6% respectively. The difference area encompasses the parts of the classification image where the classification maps disagree. User and producer accuracies for the class cloud shadow are strongly scene-dependent, typically varying between 45% and 95%. The experimental results show that the proposed TIP method based on thresholds, indices and projections can obtain improved cloud shadow detection performance.

Atmospheric Lengthscales for Global VSWIR Imaging Spectroscopy

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.

Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data

In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI (R 2 = 0.91, R 2 = 0.86) and lowest for SLA mapping (R 2 = 0.53). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME.

Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.

Wavelength Extension of the Optimized Asymmetric-Order Vegetation Isoline Equation to Cover the Range from Visible to Near-Infrared

Vegetation isoline equations describe analytical relationships between two reflectances of different wavelengths. Their applications range from retrievals of biophysical parameters to the derivation of the inter-sensor relationships of spectral vegetation indexes. Among the three variants of vegetation isoline equations introduced thus far, the optimized asymmetric-order vegetation isoline equation is the newest and is known to be the most accurate. This accuracy assessment, however, has been performed only for the wavelength pair of red and near-infrared (NIR) bands fixed at ∼655 nm and ∼865 nm, respectively. The objective of this study is to extend this wavelength limitation. An accuracy assessment was therefore performed over a wider range of wavelengths, from 400 to 1200 nm. The optimized asymmetric-order vegetation isoline equation was confirmed to demonstrate the highest accuracy among the three isolines for all the investigated wavelength pairs. The second-best equation, the asymmetric-order isoline equation, which does not include an optimization factor, was not superior to the least-accurate equation (i.e., the first-order isoline equation) in some cases. This tendency was prominent when the reflectances of the two wavelengths were similar. By contrast, the optimized asymmetric-order vegetation isoline showed stable performance throughout this study. A single factor introduced into the optimized asymmetric-order isoline equation was concluded to effectively reduce errors in the isoline for all the wavelength combinations examined in this study.

Spatial Surface Reflectance Retrievals for Visible/Shortwave Infrared Remote Sensing via Gaussian Process Priors

Remote Visible/Shortwave Infrared (VSWIR) imaging spectroscopy is a powerful tool for measuring the composition of Earth’s surface over wide areas. This compositional information is captured by the spectral surface reflectance, where distinct shapes and absorption features indicate the chemical, bio- and geophysical properties of the materials in the scene. Estimating this surface reflectance requires removing the influence of atmospheric distortions caused by water vapor and particles. Traditionally reflectance is estimated by considering one location at a time, disentangling atmospheric and surface effects independently at all locations in a scene. However, this approach does not take advantage of spatial correlations between contiguous pixels. We propose an extension to a common Bayesian approach, Optimal Estimation, by introducing atmospheric correlations into the multivariate Gaussian prior. We show how this approach can be implemented as a small change to the traditional estimation procedure, thus limiting the additional computational burden. We demonstrate a simple version of the technique using simulations and multiple airborne radiance data sets. Our results show that the predicted atmospheric fields are smoother and more realistic than independent inversions given the assumption of spatial correlation and may reduce bias in the surface reflectance retrievals compared to post-process smoothing.

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.

Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery

The recently launched and upcoming hyperspectral satellite missions, featuring contiguous visible-to-shortwave infrared spectral information, are opening unprecedented opportunities for the retrieval of a broad set of vegetation traits with enhanced accuracy through novel retrieval schemes. In this framework, we exploited hyperspectral data cubes collected by the new-generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency to develop and test a hybrid retrieval workflow for crop trait mapping. Crop traits were mapped over an agricultural area in north-east Italy (Jolanda di Savoia, FE) using PRISMA images collected during the 2020 and 2021 vegetative seasons. Leaf chlorophyll content, leaf nitrogen content, leaf water content and the corresponding canopy level traits scaled through leaf area index were estimated using a hybrid retrieval scheme based on PROSAIL-PRO radiative transfer simulations coupled with a Gaussian processes regression algorithm. Active learning algorithms were used to optimise the initial set of simulated data by extracting only the most informative samples. The accuracy of the proposed retrieval scheme was evaluated against a broad ground dataset collected in 2020 in correspondence of three PRISMA overpasses. The results obtained were positive for all the investigated variables. At the leaf level, the highest accuracy was obtained for leaf nitrogen content (LNC: r2=0.87, nRMSE=7.5%), while slightly worse results were achieved for leaf chlorophyll content (LCC: r2=0.67, nRMSE=11.7%) and leaf water content (LWC: r2=0.63, nRMSE=17.1%). At the canopy level, a significantly higher accuracy was observed for nitrogen content (CNC: r2=0.92, nRMSE=5.5%) and chlorophyll content (CCC: r2=0.82, nRMSE=10.2%), whereas comparable results were obtained for water content (CWC: r2=0.61, nRMSE=16%). The developed models were additionally tested against an independent dataset collected in 2021 to evaluate their robustness and exportability. The results obtained (i. e., LCC: r2=0.62, nRMSE=27.9%; LNC: r2=0.35, nRMSE=28.4%; LWC: r2=0.74, nRMSE=20.4%; LAI: r2=0.84, nRMSE=14.5%; CCC: r2=0.79, nRMSE=18.5%; CNC: r2=0.62, nRMSE=23.7%; CWC: r2=0.92, nRMSE=16.6%) evidence the transferability of the hybrid approach optimised through active learning for most of the investigated traits. The developed models were then used to map the spatial and temporal variability of the crop traits from the PRISMA images. The high accuracy and consistency of the results demonstrates the potential of spaceborne imaging spectroscopy for crop monitoring, paving the path towards routine retrievals of multiple crop traits over large areas that could drive more effective and sustainable agricultural practices worldwide.

PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400–750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021. L1 radiance values were compared with the TOA radiances simulated with a radiative transfer code configured using measurements of the atmospheric profile and the surface spectral characteristics. The L2 reflectance products were also compared with the data obtained by using the ImACor code atmospheric correction tool. A preliminary assessment to identify PRISMA noise characteristics was also conducted. The results showed that: (i) the PRISMA performance, as measured at the Pignola site over different seasons, is characterized by relative mean absolute differences (RMAD) of about 5–7% up to 1800 nm, while a decrease in accuracy was observed in the SWIR; (ii) a coherent noise could be observed in all the analyzed images below the 630th scan line, with a frequency of about 0.3–0.4 cycles/pixel; (iii) the most recent version of the standard reflectance L2 product (i.e., Version 2.05) matched well the reflectance values obtained by using the ImACor atmospheric correction tool. All these preliminary results confirm that PRISMA imagery is suitable for an accurate retrieval of the bio-geochemical variables pertaining to a complex fragmented ecosystem such as that of the Southern Apennines. Further studies are needed to confirm and monitor PRISMA data performance on different land-cover areas and on the Radiometric Calibration Network (RadCalNet) targets.

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.

Comprehensive design analysis and verification of space-based short-wave infrared coded spectrometer via curved prism dispersion

The spaceborne dispersive spectrometer is widely used in environmental, resource, and ocean observations. The coded spectrometer has higher energy advantages than the dispersion spectrometer, so it has great application prospects. In the current study, we developed an off-axis short-wave infrared coded optical system (SICOS) based on curved prism dispersion, and we further explored the design and optimization of the SICOS structure. Finite element analyses of a space-based short-wave infrared coded spectrometer based on curved prism dispersion (SSICS-CPD), including static simulation, modal analysis, sinusoidal vibration mechanical analysis, and random vibration mechanical analysis, were carried out. Simulation results showed that the SICOS support structure had excellent mechanical and thermal stability. As off-axis optical systems cannot meet the requirements of optical position accuracy through centering processing, a point source microscope and three-coordinate measuring machines were employed to complete the high-precision and rapid assembly of the SSICS-CPD. In addition, verification tests of surface shape error, stress relief, random vibration, and optical design parameters were carried out to validate the high stability and imaging performance of the SSICS-CPD. Results showed that the average modulation transfer function in the full field was 0.43 at 16.67 lp/mm, the spectral smile was <0.2 pixels, and the spectral keystone was <0.1 pixels. The design, analysis, assembly, and verification of the SSICS-CPD provide a useful reference for the development of other spaceborne prism dispersion spectrometers.

Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250 m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2 model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737 km² region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 trees km^-2  year^-1 in 2010-2011, to ~535 trees km^-2  year^-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control

Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, São Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae. aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health.

Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data

Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable of providing detailed spectral information to identify the spectral features of PV, and thus potentially become a promising resource for automated and operational PV detection. However, PV detection with imaging spectroscopy data must cope with the vast spectral diversity of surface materials, which is commonly divided into spectral intra-class variability and inter-class similarity. We have developed an approach to detect PV modules based on their physical absorption and reflection characteristics using airborne imaging spectroscopy data. A large database was implemented for training and validating the approach, including spectra-goniometric measurements of PV modules and other materials, a HyMap image spectral library containing 31 materials with 5627 spectra, and HySpex imaging spectroscopy data sets covering Oldenburg, Germany. By normalizing the widely used Hydrocarbon Index (HI), we solved the intra-class variability caused by different detection angles, and validated it against the spectra-goniometric measurements. Knowing that PV modules are composed of materials with different transparencies, we used a group of spectral indices and investigated their interdependencies for PV detection with implementing the image spectral library. Finally, six well-trained spectral indices were applied to HySpex data acquired in Oldenburg, Germany, yielding an overall PV map. Four subsets were selected for validation and achieved overall accuracies, producer's accuracies and user's accuracies, respectively. This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data. We aim to create greater awareness of the potential importance and applicability of airborne and spaceborne imaging spectroscopy data for PV modules identification.

Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

Mapping landscape canopy nitrogen content from space using PRISMA data

Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced opportunities for the development of new-generation retrieval models of multiple vegetation traits. Among these, canopy nitrogen content (CNC) is one of the most promising variables given its importance for agricultural monitoring applications. This work presents the first hybrid CNC retrieval model for the operational delivery from spaceborne imaging spectroscopy data. To achieve this, physically-based models were combined with machine learning regression algorithms and active learning (AL). The key concepts involve: (1) coupling the radiative transfer models PROSPECT-PRO and SAIL for the generation of a wide range of vegetation states as training data, (2) using dimensionality reduction to deal with collinearity, (3) applying an AL technique in combination with Gaussian process regression (GPR) for fine-tuning the training dataset on in field collected data, and (4) adding non-vegetated spectra to enable the model to deal with spectral heterogeneity in the image. The final CNC model was successfully validated against field data achieving a low root mean square error (RMSE) of 3.4 g/m² and coefficient of determination (R ²) of 0.7. The model was applied to a PRISMA image acquired over agricultural areas in the North of Munich, Germany. Mapping aboveground CNC yielded reliable estimates over the whole landscape and meaningful associated uncertainties. These promising results demonstrate the feasibility of routinely quantifying CNC from space, such as in an operational context as part of the future CHIME mission.

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

Priority list of biodiversity metrics to observe from space

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

Satellite-based survey of extreme methane emissions in the Permian basin

Industrial emissions play a major role in the global methane budget. The Permian basin is thought to be responsible for almost half of the methane emissions from all U.S. oil- and gas-producing regions, but little is known about individual contributors, a prerequisite for mitigation. We use a new class of satellite measurements acquired during several days in 2019 and 2020 to perform the first regional-scale and high-resolution survey of methane sources in the Permian. We find an unexpectedly large number of extreme point sources (37 plumes with emission rates >500 kg hour^-1), which account for a range between 31 and 53% of the estimated emissions in the sampled area. Our analysis reveals that new facilities are major emitters in the area, often due to inefficient flaring operations (20% of detections). These results put current practices into question and are relevant to guide emission reduction efforts.

Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery

The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.

Retrieval of Arctic Vegetation Biophysical and Biochemical Properties from CHRIS/PROBA Multi-Angle Imagery Using Empirical and Physical Modelling

Mapping and monitoring of Arctic vegetation biochemical and biophysical properties is gaining importance as global climate change is disproportionately affecting this region. Previous studies using remote sensing to model Arctic vegetation biochemical and biophysical properties have generally involved empirical modelling with nadir looking broadband sensors and have typically been conducted at the field scale in one study area. Satellite hyperspectral remote sensing has not been previously investigated for retrieving leaf and canopy biochemical and biophysical properties of Arctic vegetation across multiple sites using either empirical or physically-based modelling approaches. Furthermore, multi-angle hyperspectral sensors (CHRIS/PROBA), which can provide insight into vegetation reflectance anisotropy and potentially improve vegetation parameter estimation, have also not been investigated for this purpose. In this study, three modelling approaches previously investigated with field spectroscopy data (Kennedy et al., 2020) were used with CHRIS Mode-1 imagery to predict leaf chlorophyll content, plant area index and canopy chlorophyll content across a bioclimatic gradient in the Western Canadian Arctic. Modelling approaches included: parametric linear regression based on vegetation indices (VI), non-parametric machine learning Gaussian processes regression (GPR) and inversion of the PROSAIL radiative transfer model using a look-up table approach (LUT). CHRIS imagery was acquired with −55°, −36°, 0°, +36°, +55° view zenith angles (VZA) between 2011 and 2014 over three field sites extending from the Richardson Mountains in central Yukon, Canada to the north end of Banks Island, Northwest Territories, Canada. Field measurements were acquired within several weeks of satellite acquisitions. GPR had the best model fit (mean cross-validated (cv) coefficient of determination, r2cv = 0.61 across all vegetation variables, sites and VZAs vs. 0.59 for the simple ratio, SR) and predictive performance (normalized root mean square error, NRMSEcv = 0.13 vs. 0.14 for SR). The revised optimized soil adjusted VI (ROSAVI) performance was slightly poorer (r2cv = 0.51; NRMSEcv = 0.15). The physically-based PROSAIL model performed poorer than all empirical models (r2 = 0.50; NRMSE = 0.18). This ranking of model performance is similar to that found in the previous field spectroscopy study, where empirical model fits and predictive performance were only slightly worse. With respect to view angle performance, NRMSE varied only slightly, indicating no distinct advantage for any one VZA. Overall, strong potential has been demonstrated for empirical modelling of Arctic vegetation chlorophyll and plant area index using hyperspectral data combined with band selection/optimization procedures in the Arctic. Recently launched and future hyperspectral satellites, including next generation airborne sensors, will likely provide improvements to the model performance reported here.

Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.

Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (Cab ), leaf water content (Cw ), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI * Cab (laiCab) and LAI * Cw (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μg/cm2 (BOA) vs. 8 μg/cm2 (TOA) in the case of Cab . For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m2 (BOA) vs. 113 g/m2 (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management.

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.