Airborne Imaging Spectrometer Research Articles

Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis

AbstractImaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.

Quantifying methane emissions from United States landfills.

Methane emissions from solid waste may represent a substantial fraction of the global anthropogenic budget, but few comprehensive studies exist to assess inventory assumptions. We quantified emissions at hundreds of large landfills across 18 states in the United States between 2016 and 2022 using airborne imaging spectrometers. Spanning 20% of open United States landfills, this represents the most systematic measurement-based study of methane point sources of the waste sector. We detected significant point source emissions at a majority (52%) of these sites, many with emissions persisting over multiple revisits (weeks to years). We compared these against independent contemporaneous in situ airborne observations at 15 landfills and established good agreement. Our findings indicate a need for long-term, synoptic-scale monitoring of landfill emissions in the context of climate change mitigation policy.

Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products

Forests substantially mediate the water and carbon dioxide exchanges between terrestrial ecosystems and the atmosphere. The rate of this exchange, including evapotranspiration (ET) and gross primary production (GPP), depends mainly on the underlying vegetation type, health state, and the composition of abiotic environmental drivers. However, the complex 3D structure of forest canopies and the inherent top-view perspective of optical and thermal remote sensing complicate remote sensing-based retrievals of biotic and abiotic factors that eventually determine ET and GPP. This study investigates the sensitivity of remote sensing approaches to 3D variation of abiotic and biotic environmental drivers. We use 3D virtual scenes of two structurally different Swiss forests and the radiative transfer model DART to simulate the 3D distribution of solar irradiance and reflected radiance in the forest canopy. These simulations, in combination with LiDAR data, are used to derive the absorbed photosynthetic active radiation (APAR) and the leaf area index (LAI) in 3D space. The 3D variation of both parameters was quantified and analyzed. We then simulated images of the top-of-canopy bi-directional reflectance factor (BRF) and compared them with the hemispheric-conical reflectance factor (HCRF) data derived from HyPlant airborne imaging spectrometer measurements. The simulated BRF data was used to derive APAR and LAI, and the results were compared to their respective 3D representations. We unravel considerable spatial differences between both representations. We discuss possible reasons for the disagreement, including a potential insensitivity of the inherent top-of-canopy view for the real 3D product dynamics and limitations of the processing of remote sensing data, especially the approximation of effective surface irradiance. Our results can help understanding sources of uncertainties in remote sensing based gas exchange products and defining mitigation strategies.

Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers

Abstract. Strong methane point source emissions generate large atmospheric concentrations that can be detected and quantified with infrared remote sensing and retrieval algorithms. Two standard and widely used retrieval algorithms for one class of observing platform, imaging spectrometers, include pixel-wise and column-wise approaches. In this study, we assess the performance of both approaches using the airborne imaging spectrometer (Global Airborne Observatory) observations of two extensive controlled-release experiments. We find that the column-wise retrieval algorithm is sensitive to the flight line length and can have a systematic low bias with short flight lines, which is not present in the pixel-wise retrieval algorithm. However, the pixel-wise retrieval is very computationally expensive, and the column-wise retrieval algorithms can produce good results when the flight line length is sufficiently long. Lastly, this study examines the methane plume detection performance of the Global Airborne Observatory with a column-wise retrieval algorithm and finds minimum detection limits of between 9 of 10 kg h−1 and 90 % probability of detection between 10 and 45 kg h−1. These results present a framework of rules for guiding proper concentration retrieval selection given conditions at the time of observation in order to ensure robust detection and quantification.

Methane retrievals from airborne HySpex observations in the shortwave infrared

Abstract. Monitoring anthropogenic emissions is a crucial aspect in understanding the methane budget. Moreover, a reduction of methane emissions could help to mitigate global warming on a short timescale. This study compares various retrieval schemes for estimating localized methane enhancements around ventilation shafts in the Upper Silesian Coal Basin in Poland using nadir observations in the shortwave infrared acquired from the airborne imaging spectrometer HySpex. Linear and nonlinear solvers are examined and compared, with special emphasis put on strategies that tackle degeneracies between the surface reflectivity and broad-band molecular absorption features – a challenge arising from the instrument's low spectral resolution. Results reveal that the generalized nonlinear least squares fit, employed within the Beer InfraRed Retrieval Algorithm (BIRRA), can measure enhanced methane levels with notable accuracy and precision. This is accomplished by allowing the scene's background covariance structure to account for surface reflectivity statistics. Linear estimators such as matched filter (MF) and singular value decomposition (SVD) are able to detect and, under favorable conditions, quantify enhanced levels of methane quickly. Using k-means clustering as a preprocessing step can further enhance the performance of the two linear solvers. The linearized BIRRA fit (LLS) underestimates methane but agrees on the enhancement pattern. The non-quantitative spectral signature detection (SSD) method does not require any forward modeling and can be useful in the detection of relevant scenes. In conclusion, the BIRRA code, originally designed for the retrieval of atmospheric constituents from spaceborne high-resolution spectra, turned out to be applicable to hyperspectral airborne imaging data for the quantification of methane plumes from point-like sources. Moreover, it is able to outperform well-established linear schemes such as the MF or SVD at the expense of high(er) computing time.

Hydrocarbon Spectra Slope (HYSS): A Spectra Index for Quantifying and Characterizing Hydrocarbon oil on Different Substrates Using Spectra Data

Many sensors in Optical domain allow for detection of hydrocarbons in oil spills study. However, high resolution laboratory and airborne imaging spectrometers have shown potential for quantification and characterization of hydrocarbon. Available methods in literature for quantifying and characterizing hydrocarbons on these data relies mainly on shapes and positions of hydrocarbon key absorption features, mainly at 1.73 µm and 2.30 µm. Shapes formed by these absorption features are often influenced by spectral features of background substrates, thereby limiting the quality of results. Furthermore, multispectral sensors cannot resolve the shapes of key absorption features, a strong limitation for methods used in previous works. In this study, we present Hydrocarbon Spectra Slope (HYSS), a new spectra index that offers predictive quantification and characterization of common hydrocarbon oils. Slope values for the studied hydrocarbon oils enable clear discrimination for relative quantitative analysis of oil abundance classes and qualitative discrimination for common hydrocarbons on common background substrates. Data from ground-based spectrometers and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are resampled to AVIRIS, Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and LANDSAT 7 Enhanced Thematic Mapper’s (ETM+) Full Width at Half Maximum (FWHM), in order to compute spectra slope values for hydrocarbon abundance /hydrocarbon-substrate characterization. Despite limitations of nonconformity of central wavelengths and/or band widths of multispectral sensors to key hydrocarbon band, statistical significance for both quantitative and qualitative analysis at 95% confidence level (P-value ˂0.01) suggests strong potential of the use of HYSS, multispectral and hyperspectral sensors as emergency response tools for hydrocarbon mapping.

Mapping Wheat Take-All Disease Levels from Airborne Hyperspectral Images Using Radiative Transfer Models

Take-all is a root disease that can severely reduce wheat yield, and wheat leaves with take-all disease show a large amount of chlorophyll loss. The PROSAIL model has been widely used for the inversion of vegetation physiological parameters with a clear physical meaning of the model and high simulation accuracy. Based on the chlorophyll deficiency characteristics, the reflectance data under different canopy chlorophyll contents were simulated using the PROSAIL model. In addition, inverse models of spectral reflectance profiles and canopy chlorophyll contents were constructed using a one-dimensional convolutional neural network (1D-CNN), and a transfer learning approach was used to detect the take-all disease levels. The spectral reflectance data of winter wheat acquired by an airborne imaging spectrometer during the filling period were used as input parameters of the model to obtain the chlorophyll content of the canopy. Finally, the results of the distribution of winter wheat take-all disease were mapped based on the relationship between take-all disease and the chlorophyll content of the canopy. The results showed that classification based on the deep learning model performed well for winter wheat take-all monitoring. This study can provide some reference basis for high-precision winter wheat take-all disease monitoring and can also provide some technical method references and ideas for remote sensing crop pest and disease remote sensing mapping.

Methane emissions decline from reduced oil, natural gas, and refinery production during COVID-19

In the summer of 2020, the AVIRIS-NG airborne imaging spectrometer surveyed California’s Southern San Joaquin Valley and the South Bay (Los Angeles County) to identify anthropogenic methane (CH4) point source plumes, estimate emission rates, and attribute sources to both facilities and emission sectors. These flights were designed to revisit regions previously surveyed by the 2016–2017 California Methane Survey and to assess the socioeconomic responses of COVID-19 on emissions across multiple sectors. For regions flown by both the California Methane Survey and the California COVID campaigns, total CH4 point source emissions from the energy and oil & natural gas sectors were 34.8% lower during the summer 2020 flights, however, emission trends varied across sector. For the energy sector, there was a 28.2% decrease driven by reductions in refinery emissions consistent with a drop in production, which was offset in part with increases from powerplants. For the oil & natural gas sector, CH4 emissions declined 34.2% and significant variability was observed at the oilfield scale. Emissions declined for all but the Buena Vista and Cymric fields with an observed positive relationship between production and emissions. In addition to characterizing the short-term impact of COVID-19 on CH4 emissions, this study demonstrates the broader potential of remote sensing with sufficient sensitivity, spatial resolution, and spatio-temporal completeness to quantify changes in CH4 emissions at the scale of key sectors and facilities.

Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.

Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States

Understanding, prioritizing, and mitigating methane (CH4) emissions requires quantifying CH4 budgets from facility scales to regional scales with the ability to differentiate between source sectors. We deployed a tiered observing system for multiple basins in the United States (San Joaquin Valley, Uinta, Denver-Julesburg, Permian, Marcellus). We quantify strong point source emissions (>10 kg CH4 h-1) using airborne imaging spectrometers, attribute them to sectors, and assess their intermittency with multiple revisits. We compare these point source emissions to total basin CH4 fluxes derived from inversion of Sentinel-5p satellite CH4 observations. Across basins, point sources make up on average 40% of the regional flux. We sampled some basins several times across multiple months and years and find a distinct bimodal structure to emission timescales: the total point source budget is split nearly in half by short-lasting and long-lasting emission events. With the increasing airborne and satellite observing capabilities planned for the near future, tiered observing systems will more fully quantify and attribute CH4 emissions from facility to regional scales, which is needed to effectively and efficiently reduce methane emissions.

Airborne imaging spectrometer dataset for spectral characterization and predictive mineral mapping using sub-pixel based classifier in parts of Udaipur, Rajasthan, India

Imaging spectroscopy is used for the mapping of different minerals using their characteristics absorption depths. The present study covers the parts of the Udaipur region for mineral mapping with the help of a Mixture Tuned Matched Filtering (MTMF) soft classifier using the Airborne Visible Infrared Imaging Spectrometer – Next Generation dataset. The preprocessed and classified dataset by MTMF soft classifier identified carbonates, serpentine minerals, and alteration products of ultramafics, clay and mica minerals, and intimate mixture of kaolinite and muscovite in the region. These minerals show the characteristic absorption features at different wavelength positions of the SWIR region. The comparison of the image spectrum was carried out with the reference (mineral library) and laboratory collected reflectance spectral curves. The derived mineral map accurately mapped the minerals and discriminated the minerals belonging to a similar mineral class. Validation was done using field evidences, photomicrographs, XRD, laboratory spectral curves, and already published maps. The accuracy for the generated mineral map is 83.93%.

Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops

• Pronounced diurnal patterns found for both UAV- and Airborne-based far-red SIF. • Consistent spatial patterns of UAV- and Airborne-based far-red SIF over different crops. • FluorSpec far-red SIF is linearly related to HyPlant far-red SIF. • Far-red sun-induced chlorophyll fluorescence can be reliably measured from a UAV. Sun-induced chlorophyll fluorescence (SIF) is a promising proxy of the dynamic photosynthetic process. Unmanned Aerial Vehicles (UAVs) are flexible and cost-effective for acquiring SIF data at high temporal and spatial resolution. The UAV-based point spectrometer FluorSpec was designed to measure SIF within agricultural fields. To correctly understand SIF values and further photosynthetic research, the ability of the UAV-based FluorSpec to provide reliable SIF information within agricultural fields needs evaluation. In this paper, the UAV-based FluorSpec was compared with the high-performance airborne imaging spectrometer HyPlant using diurnal far-red SIF measurements over different crop types (i.e. two varieties of winter wheat, two varieties of spring barley, bean, and maize), which were acquired by almost simultaneous airborne and UAV flights during a clear sky day in 2019. After improving the footprint geolocation of FluorSpec measurements using concurrent red-green-blue (RGB) images, we compared the FluorSpec and HyPlant SIF measurements, their diurnal developments, and spatial distributions for different crop types. The results from both systems show consistent, clear diurnal patterns that are positively correlated with photosynthetically active radiation (PAR) over most crop types. Similar SIF spatial patterns were shown within crop fields as well. UAV-based FluorSpec SIF showed a good linear correlation with HyPlant SIF with an R 2 up to 0.76. The good agreement confirms that the UAV-based FluorSpec system is able to measure meaningful SIF values at the field scale and thus stimulates SIF applications in agriculture. The systematic errors up to 0.3 mW m −2 sr −1 nm −1 from the linear regression between the two systems indicate that the UAV-based FluorSpec system should be improved by considering the main sources of uncertainty discussed in this paper. Future studies with dedicated experiments are recommended to assess the systematic uncertainties of UAV-based FluorSpec derived SIF information.

Airborne Trace Gas Remote Sensing and Surface Mobile In Situ: A Novel Tool for the Study of Structural Geological Controls from a Producing Oil Field

SummaryAccurate and representative determination of greenhouse gases (GHG) from oil and gas (O&G) production facilities requires high-spatial-resolution data, which can be acquired by airborne imaging spectrometers. However, assessment of nonmethane hydrocarbon emissions, which are far less amenable to remote sensing, requires mobile surface in-situ measurements (e.g., a mobile air quality laboratory).Field in-situ measurements and airborne thermal infrared spectral imagery were acquired for three producing California oil fields (Poso Creek, Kern Front, and Kern River) located next to each other on 14 September 2018. In addition, a profile ascending a nearby mountain collected in-situ data for the Round Mountain oilfield. Plume methane to ethane ratios were consistent within different regions of the field and differed between these fields in a manner related to field geological structures.Data acquired by an airborne thermal infrared imaging spectrometer, Mako, in 2015 and 2018 showed most emissions were from distant plumes in the Kern Front and Poso Creek fields. The spatial distribution of detected plumes was strongly related to faults, particularly active faults, which are proposed to stress infrastructure, leading to higher fugitive emissions and/or emissions from natural migration pathways (seepage). Additionally, the spatial distribution of detected plumes suggested unmapped faults. Thus, high-sensitivity imaging spectroscopy can improve understanding of reservoir geological structures that impact hydrocarbon migration and field operations, highlighting the potential for a novel reservoir management tool.

Mapping the spatial distribution of NO<sub>2</sub> with in situ and remote sensing instruments during the Munich NO<sub>2</sub> imaging campaign

Abstract. We present results from the Munich Nitrogen dioxide (NO2) Imaging Campaign (MuNIC), where NO2 near-surface concentrations (NSCs) and vertical column densities (VCDs) were measured with stationary, mobile, and airborne in situ and remote sensing instruments in Munich, Germany. The most intensive day of the campaign was 7 July 2016, when the NO2 VCD field was mapped with the Airborne Prism Experiment (APEX) imaging spectrometer. The spatial distribution of APEX VCDs was rather smooth, with a horizontal gradient between lower values upwind and higher values downwind of the city center. The NO2 map had no pronounced source signatures except for the plumes of two combined heat and power (CHP) plants. The APEX VCDs have a fair correlation with mobile multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations from two vehicles conducted on the same afternoon (r=0.55). In contrast to the VCDs, mobile NSC measurements revealed high spatial and temporal variability along the roads, with the highest values in congested areas and tunnels. The NOx emissions of the two CHP plants were estimated from the APEX observations using a mass-balance approach. The NOx emission estimates are consistent with CO2 emissions determined from two ground-based Fourier transform infrared (FTIR) instruments operated near one CHP plant. The estimates are higher than the reported emissions but are probably overestimated because the uncertainties are large, as conditions were unstable and convective with low and highly variable wind speeds. Under such conditions, the application of mass-balance approaches is problematic because they assume steady-state conditions. We conclude that airborne imaging spectrometers are well suited for mapping the spatial distribution of NO2 VCDs over large areas. The emission plumes of point sources can be detected in the APEX observations, but accurate flow fields are essential for estimating emissions with sufficient accuracy. The application of airborne imaging spectrometers for studying NSCs is less straightforward and requires us to account for the non-trivial relationship between VCDs and NSCs.

Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

AbstractIncreasing evidence suggests that remotely sensed spectral diversity is linked to plant species richness. However, a conflicting spectral diversity–biodiversity relationship in grasslands has been found in previous studies. In particular, it remains unclear how well the spectral diversity–biodiversity relationship holds in naturally assembled species‐rich grasslands. To address the linkage between spectral diversity and plant species richness in a species‐rich alpine grassland ecosystem, we investigated (i) the trade‐off between spectral and spatial resolution in remote sensing data; (ii) the suitability of three different spectral metrics to describe spectral diversity (coefficient of variation, convex hull volume and spectral species richness) and (iii) the importance of confounding effects of live plant biomass, dead plant biomass and plant life forms on the spectral diversity–biodiversity relationship. We addressed these questions using remote sensing data collected with consumer‐grade cameras with four spectral bands and 10 cm spatial resolution on an unmanned aerial vehicle (UAV), airborne imaging spectrometer data (AVIRIS‐NG) with 372 bands and 2.5 m spatial resolution, and a fused data product of both datasets. Our findings suggest that a fused dataset can cope with the requirement of both high spatial‐ and spectral resolution to remotely measure biodiversity. However, in contrast to several previous studies, we found a negative correlation between plant species richness and spectral metrics based on the spectral information content (i.e. spectral complexity). The spectral diversity calculated based on the spectral complexity was sensitive to live and dead plant biomass. Overall, our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification‐based spectral metrics, such as spectral species richness, and awareness of confounding factors (e.g. plant biomass), which may be ecosystem specific.

Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer.

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet full-spectrum estimation methods such as the spectral fitting method (SFM) are computationally expensive. To bypass this computational load, this work aims to approximate the SFM-based SIF retrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogate models of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulation concept is applied to experimental data. We evaluated the possibility of approximating the SFM-like SIF output directly based on radiance data while minimizing the loss in precision as opposed to SFM-based SIF. To do so, we implemented a double principal component analysis (PCA) dimensionality reduction, i.e., in both input and output, to achieve emulation of multispectral SIF output based on hyperspectral radiance data. We then evaluated systematically: (1) multiple machine learning regression algorithms, (2) number of principal components, (3) number of training samples, and (4) quality of training samples. The best performing SIF emulator was then applied to a HyPlant flight line containing at sensor radiance information, and the results were compared to the SFM SIF map of the same flight line. The emulated SIF map was quasi-instantaneously generated, and a good agreement against the reference SFM map was obtained with a R2 of 0.88 and NRMSE of 3.77%. The SIF emulator was subsequently applied to 7 HyPlant flight lines to evaluate its robustness and portability, leading to a R2 between 0.68 and 0.95, and a NRMSE between 6.42% and 4.13%. Emulated SIF maps proved to be consistent while processing time was in the order of 3 min. In comparison, the original SFM needed approximately 78 min to complete the SIF processing. Our results suggest that emulation can be used to efficiently reduce computational loads of SIF retrieval methods.

The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping

Airborne measurements of sun-induced chlorophyll fluorescence (SIF) are a promising tool for monitoring plant functioning on different scales. However, currently operational airborne imaging spectrometers for SIF measurements still have limited spatial resolution and pointing accuracy. This is challenging in terms of the practical use of SIF maps for crop breeding and plant phenotyping. We developed and tested two spatial aggregation approaches to make airborne SIF data usable in experimental settings with a high number of small experimental plots. The two aggregation approaches generating representative SIF values for experimental plots demonstrated the potential to be used in crop phenotyping. The first aggregation approach (Approach A) aggregates pixel values directly on SIF maps, whereas the second approach (Approach B) aggregates at-sensor radiance before SIF retrieval. The statistical analysis showed that Approaches A and B led to significantly different SIF products for single experimental plots (p < 0.001). To evaluate the usability of the two approaches, aggregated SIF products were fitted against ground-based reference measurements. We found that Approach B provided a better representation of ground truth SIF760 (R2 = 0.61, p < 0.001) than Approach A (R2 = 0.55, p < 0.001) when combined with weighted averaging and robust outlier detection. Furthermore, our results suggest that a slight decrease in the spatial resolution of the image data improves accuracy of aggregation.

Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

Impact of scene-specific enhancement spectra on matched filter greenhouse gas retrievals from imaging spectroscopy

Matched filter techniques have been widely used for retrieval of greenhouse gas enhancements from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the greenhouse gas target spectrum used for concentration enhancement estimation has remained largely unaltered since the introduction of quantitative matched filter retrievals. The magnitude of retrieved methane and carbon dioxide enhancements, and thereby integrated mass enhancements (IME) and estimated flux of point-source emitters, is heavily dependent on this target spectrum. Current standard use of molecular absorption coefficients to create unit enhancement target spectra does not account for absorption by background concentrations of greenhouse gases, solar and sensor geometry, or atmospheric water vapor absorption. We introduce geometric and atmospheric parameters into the generation of scene-specific unit enhancement spectra to provide target spectra that are compatible with all greenhouse gas retrieval matched filter techniques. Specifically, we use radiative transfer modeling to model four parameters that are expected to change between scenes: solar zenith angle, column water vapor, ground elevation, and sensor altitude. These parameter values are well defined, with low variation within a single scene. A benchmark dataset consisting of ten AVIRIS-NG airborne imaging spectrometer scenes was used to compare IME retrieved using a matched filter algorithm. For methane plumes, IME resulting from use of standard, generic enhancement spectra varied from −22 to +28.7% compared to scene-specific enhancement spectra. Due to differences in spectral shape between the generic and scene-specific enhancement spectra, differences in methane plume IME were linked to surface spectral characteristics in addition to geometric and atmospheric parameters. IME differences were much larger for carbon dioxide plumes, with generic enhancement spectra producing integrated mass enhancements −76.1 to −48.1% compared to scene-specific enhancement spectra. Fluxes calculated from these integrated enhancements would vary by the same percentages, assuming equivalent wind conditions. Methane and carbon dioxide IME were most sensitive to changes in solar zenith angle and ground elevation. We introduce an interpolation approach that can efficiently generate scene-specific unit enhancement spectra for given sets of parameters. Scene-specific target spectra can improve confidence in greenhouse gas retrievals and flux estimates across collections of scenes with diverse geometric and atmospheric conditions.

Detecting high-temperature anomalies from Sentinel-2 MSI images

High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites.