High Resolution Imaging Spectrometer Research Articles

Identification of an optimal ground-based validation site for FLEX and quantification of uncertainties using airborne HyPlant data - A case study in Italy

The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.

Compact broadband high-resolution real-time four-dimensional imaging spectrometer

A broadband high-resolution real-time four-dimensional imaging spectrometer (HRRFDIS) is presented, which can acquire both broadband fine spectra and high-resolution three-dimensional (3D) spatial images of a 3D object in real time. The HRRFDIS consists of a first microlens array arranged in a plane to achieve orthographic view spatial imaging, a second microlens array arranged on a conical surface to measure the depth and to achieve 360-degree side-view spatial imaging, multiple optical fibers, a collimating microlens array arranged in a straight line, a parallel planar transmission grating pair to obtain high spectral resolution over a broadband spectral range, and an area-array detector. Compared with the scanning four-dimensional imaging spectrometer (FDIS), the HRRFDIS can obtain a broadband high-resolution four-dimensional dataset using only one frame of data, and it is more stable, compact, small-sized, and lightweight. Compared to the staring FDIS using a liquid crystal filter and requiring at least one modulation period of liquid crystal to acquire a complete hyperspectral image, the HRRFDIS can acquire a complete broadband hyperspectral image in real time. Compared to existing snapshot FDIS, the HRRFDIS can achieve much higher spectral resolution, especially over a broadband spectral range. The HRRFDIS is a unique concept that is the first to obtain both high-resolution broadband spectral information and high-resolution 3D spatial information in real time, to the best of our knowledge. The HRRFDIS will be suitable for real-time measurement of 3D objects in the ultraviolet to infrared spectral range.

First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK

Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.

Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers

Abstract. Remote sensing emerges as an important tool for the detection of methane plumes emitted by so-called point sources, which are common in the energy sector (e.g., oil and gas extraction and coal mining activities). In particular, satellite imaging spectroscopy missions covering the shortwave infrared part of the solar spectrum are very effective for this application. These instruments sample the methane absorption features at the spectral regions around 1700 and 2300 nm, which enables the retrieval of methane concentration enhancements per pixel. Data-driven retrieval methods, in particular those based on the matched filter concept, are widely used to produce maps of methane concentration enhancements from imaging spectroscopy data. Using these maps enables the detection of plumes and the subsequent identification of active sources. However, retrieval artifacts caused by particular surface components may sometimes appear as false plumes or disturbing elements in the methane maps, which complicates the identification of real plumes. In this work, we use a matched filter that exploits a wide spectral window (1000–2500 nm) instead of the usual 2100–2450 nm window with the aim of reducing the occurrence of retrieval artifacts and background noise. This enables a greater ability to discriminate between surface elements and methane. The improvement in plume detection is evaluated through an analysis derived from both simulated data and real data from areas including active point sources, such as the oil and gas (O&G) industry from San Joaquin Valley (US) and the coal mines from the Shanxi region (China). We use datasets from the Precursore IperSpettrale della Missione Applicativa (PRISMA) and the Environmental Mapping and Analysis Program (EnMAP) satellite imaging spectrometer missions and from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) instrument. We find that the interference with atmospheric carbon dioxide and water vapor is generally almost negligible, while co-emission or overlapping of these trace gases with methane plumes leads to a reduction in the retrieved concentration values. Attenuation will also occur in the case of methane emissions situated above surface structures that are associated with retrieval artifacts. The results show that the new approach is an optimal trade-off between the reduction in background noise and retrieval artifacts. This is illustrated by a comprehensive analysis in a PRISMA dataset with 15 identified plumes, where the output mask from an automatic detection algorithm shows an important reduction in the number of clusters not related to CH4 emissions.

High-resolution multi-mode electron and ion imaging spectrometer at SXFEL

A new multi-mode electron and ion (MEI) imaging spectrometer with two arms of VMI and COLTRIMS/VMI (velocity map imaging/cold-target recoil-ion momentum spectroscopy) is designed to combine various photoelectron and ion detection modes for experiments at Shanghai soft x-ray free-electron laser (SXFEL) facility. The experiments can be optionally operated either with both ion and electron detection in a coincidence/covariance manner (VMI arm and COLTRIMS/VMI arm), or only photoelectron/photoion is detected with the high-resolution VMI arm. The simulated resolutions for 30–150 eV photoelectrons and 3.3 eV–18.0 eV N+ and N2+ photoions are up to 1.0% and 3.1%–1.0% according to our simulation, respectively. MEI spectrometer is expected to improve the experimental abilities significantly considering the low-repetition rate of the SXFEL and to enable the investigation of a diverse range of atomic and molecular phenomena triggered by soft x-ray free electron laser irradiation.

High-Precision Ultra-Long Air Slit Fabrication Based on MEMS Technology for Imaging Spectrometers.

The increasing demand for accurate imaging spectral information in remote sensing detection has driven the development of hyperspectral remote sensing instruments towards a larger view field and higher resolution. As the core component of the spectrometer slit, the designed length reaches tens of millimeters while the precision maintained within the μm level. Such precision requirements pose challenges to traditional machining and laser processing. In this paper, a high-precision air slit was created with a large aspect ratio through MEMS technology on SOI silicon wafers. In particular, a MEMS slit was prepared with a width of 15 μm and an aspect ratio exceeding 4000:1, and a spectral spectroscopy system was created and tested with a Hg-Cd light source. As a result, the spectral spectrum was linear within the visible range, and a spectral resolution of less than 1 nm was obtained. The standard deviation of resolution is only one-fourth of that is seen in machined slits across various view fields. This research provided a reliable and novel manufacturing technique for high-precision air slits, offering technical assistance in developing high-resolution wide-coverage imaging spectrometers.

First Nighttime Light Spectra by Satellite—By EnMAP

For the first time, nighttime VIS/NIR—SWIR (visible and near-infrared—shortwave infrared) spectra from a satellite mission have been analyzed using the EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectrometer. This article focuses on the spectral characteristics. Firstly, we checked the spectral calibration of EnMAP using sodium light emissions. Here, By applying a newly devised general method, we estimated shifts of +0.3nm for VIS/NIR and −0.2nm for SWIR; the uncertainties were found to be within the range of [−0.4nm,+0.2nm] for VIS/NIR and [−1.2nm,+1.0nm] for SWIR. These results emphasize the high accuracy of the spectral calibration of EnMAP and illustrate the feasibility of methods based on nighttime Earth observations for the spectral calibration of future nighttime satellite missions. Secondly, by employing a straightforward general method, we identified the dominant lighting types and thermal emissions in Las Vegas, Nevada, USA, on a per-pixel basis, and we considered the consistency of the outcomes. The identification and mapping of different types of LED (light-emitting diode) illuminations were achieved—with 75% of the identified dominant lighting types identified in VIS/NIR—as well as high- and low-pressure sodium and metal halide, which made up 22% of the identified dominant lighting types in VIS/NIR and 29% in SWIR and other illumination sources, as well as high temperatures, where 33% of the identified dominant emission types in SWIR were achieved from space using EnMAP due to the elevated illumination levels in the observed location. These results illustrate the feasibility of the precise identification of lighting types and thermal emissions based on nighttime high-resolution imaging spectroscopy satellite products; moreover, they support the specification of spectral characteristics for upcoming nighttime missions.

Sensitivity of solar-induced fluorescence to spectral stray light in high resolution imaging spectroscopy

Remote sensing of solar-induced chlorophyll fluorescence (SIF) advances our ability to monitor gross primary productivity. Because SIF is usually <5% of recorded canopy radiance, sources of measurement error must be reduced for accurate SIF retrieval. Here we quantify the impact of spectral stray light on SIF retrieval using a high-resolution imaging spectrometer. We first derived the FLD retrieval solution in the presence of stray light under the simplifying assumptions that SIF, reflectance, and stray light are constant across the region of the spectrum used in the retrieval. We show that stray light contributions from the canopy and reference spectra could cancel out if obtained from the same instrument. In practice however, the simplifying assumptions are unlikely to hold because SIF and reflectance vary across regions of the spectrum commonly exploited by the FLD retrieval, and stray light can also vary across the spectrum, as we demonstrate with an empirical example. To quantify the impact of spectral stray light on SIF retrievals in more realistic scenarios, we performed a sensitivity analysis. We used a stray light signal distribution function and measurements of spectral radiance and SIF to generate spectra with known quantities of SIF and stray light over four orders of magnitude of stray light. We then quantified the bias in retrieved SIF using four SIF retrieval approaches—standard FLD, 3FLD, a spectral fitting method (SFM), and a method based on singular value decomposition (SVD)—applied to the red and/or far-red spectral domains. We found that spectral stray light can cause either a positive or negative bias in SIF estimates. In the highest stray light scenario, with spectral stray light one order of magnitude smaller than the radiance signal, the stray light error ranged from < ± 1% of SIF (for O2-A band retrievals with FLD, 3FLD, and SFM methods, and for far-red retrievals in the 748 nm to 756 nm domain with SVD-based and 3FLD methods applied to Fraunhofer lines) to >30% of SIF (O2-B band retrieval with SFM, and 3FLD applied to Fraunhofer lines in the red domain). For context, a bias of 27% of SIF is a comparable magnitude to seasonal variation in SIF observed in the Amazon basin, and to SIF differences among plant functional types. However, when spectral stray light was three orders of magnitude smaller than the radiance signal, mean bias was < ± 2% for all retrieval methods. Our analysis informs when spectral stray light corrections must be performed for SIF remote sensing.

A Transmissive Imaging Spectrometer for Ground-Based Oxygen A-Band Radiance Observation

The oxygen A-band (759–770 nm) is a commonly used band for atmospheric observations. The signal in this band has wide dynamic range and can be used to invert several atmospheric parameters, such as air pressure and atmospheric optical depth, at different altitudes. High-resolution oxygen A-band radiance imaging spectrometer (HARIS) is an imaging spectrometer that operates in the oxygen A-band, which is designed for the observation of the direct solar radiance that passes through the atmosphere. HARIS is a transmissive imaging spectrometer that uses a compact transmissive optical system combined with reflective grating spectroscopy, while an area scan CMOS detector is used as the photosensitive element for the observations. HARIS response is associated with the observed target through a calibration process, which uses a monochromator with a supercontinuum laser for the spectral calibration, an integrating sphere with a spectrophotometer for the radiometric calibration and a meridian for the geometric calibration is employed to correct for distortions. The calibration results show that HARIS has an average spectral resolution of 0.33 nm and a field-of-view of 3.085 × 0.03° with an average spatial sampling interval of 0.0138°. Finally, the performance of HARIS is verified through field tests, in which the solar radiance data with an average signal-to-noise ratio of 438.93 is obtained.

First observations from the SPICE EUV spectrometer on Solar Orbiter

Aims. We present first science observations taken during the commissioning activities of the Spectral Imaging of the Coronal Environment (SPICE) instrument on the ESA/NASA Solar Orbiter mission. SPICE is a high-resolution imaging spectrometer operating at extreme ultraviolet (EUV) wavelengths. In this paper we illustrate the possible types of observations to give prospective users a better understanding of the science capabilities of SPICE. Methods. We have reviewed the data obtained by SPICE between April and June 2020 and selected representative results obtained with different slits and a range of exposure times between 5 s and 180 s. Standard instrumental corrections have been applied to the raw data. Results. The paper discusses the first observations of the Sun on different targets and presents an example of the full spectra from the quiet Sun, identifying over 40 spectral lines from neutral hydrogen and ions of carbon, oxygen, nitrogen, neon, sulphur, magnesium, and iron. These lines cover the temperature range between 20 000 K and 1 million K (10 MK in flares), providing slices of the Sun’s atmosphere in narrow temperature intervals. We provide a list of count rates for the 23 brightest spectral lines. We show examples of raster images of the quiet Sun in several strong transition region lines, where we have found unusually bright, compact structures in the quiet Sun network, with extreme intensities up to 25 times greater than the average intensity across the image. The lifetimes of these structures can exceed 2.5 hours. We identify them as a transition region signature of coronal bright points and compare their areas and intensity enhancements. We also show the first above-limb measurements with SPICE above the polar limb in C III, O VI, and Ne VIII lines, and far off limb measurements in the equatorial plane in Mg IX, Ne VIII, and O VI lines. We discuss the potential to use abundance diagnostics methods to study the variability of the elemental composition that can be compared with in situ measurements to help confirm the magnetic connection between the spacecraft location and the Sun’s surface, and locate the sources of the solar wind. Conclusions. The SPICE instrument successfully performs measurements of EUV spectra and raster images that will make vital contributions to the scientific success of the Solar Orbiter mission.

Demonstration of MHz frequency domain multiplexing readout of 37 transition edge sensors for high-resolution x-ray imaging spectrometers

We report on the development and demonstration of MHz frequency domain multiplexing (FDM) technology to readout arrays of cryogenic transition edge sensor (TES) x-ray microcalorimeters. In our FDM scheme, TESs are AC biased at different resonant frequencies in the low MHz range through an array of high-Q LC resonators. The current signals of all TESs are summed at superconducting quantum interference devices (SQUIDs). We have demonstrated multiplexing for a readout of 31 pixels using room temperature electronics, high-Q LC filters, and TES arrays developed at SRON, and SQUID arrays from VTT. We repeated this on a second setup with 37 pixels. The summed x-ray spectral resolutions @ 5.9 keV are ΔE31pix MUX=2.14 ± 0.03 eV and ΔE37pix MUX=2.23 ± 0.03 eV. The demonstrated results are comparable with other multiplexing approaches. There is potential to further improve the spectral resolution, to increase the number of multiplexed TESs, and to open up applications for TES x-ray microcalorimeters.

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m−3 to 1.11 mg m−3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m−3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.

Effective and Universal Pre-Processing for Multi-Angle CHRIS/PROBA Images

Compared with traditional nadir observations, multi-angle hyperspectral remote sensing can obtain more spatial and spectral information and improve the inversion precision of structure information on the Earth’s surface. However, processing multi-angle remote-sensing images presents new challenges. Owing to the multi-angle sensors used to obtain multi-angle images, there are major differences in the spatial and spectral information between each angle in these images. Data from the Compact High-resolution Imaging Spectrometer (CHRIS) on Project for On-Board Autonomy (PROBA) should be pre-processed to extract the BRDF (Bidirectional Reflectance Distribution Functions). Given the limitations of the pre-processing software currently available for CHRIS/PROBA images, and the lack of metadata and auxiliary origin schedules, some CHRIS multi-angle images cannot be pre-processed correctly. In the study, to promote the application of multi-angle data, a formula for calculating key parameters according to in-orbit geometric imaging relationships is derived to design a multi-angle image process flow including image rollovers, bad-line repairs, orthorectification and atmospheric corrections accounting for terrain effects. Test results indicate that the pre-processing method can quickly and effectively recover multi-angle hyperspectral information and obtain spectral characteristics of multi-angle observations.

Aboveground biomass distributions and vegetation composition changes in Louisiana's Wax Lake Delta

The Wax Lake Delta (WLD) is an actively prograding delta in the Mississippi River Delta Plain that is otherwise experiencing widespread degradation and submergence of its coastal wetlands. The WLD is actively accumulating mineral and organic sediment that increases soil surface elevation, changing emergent wetland communities as the young delta develops. There is uncertainty in how the dynamics of community composition respond to net changes in soil elevation and determine aboveground biomass and carbon storage. This study utilizes high resolution imaging spectrometer data captured on October 17, 2016, to map the delta's dominant vegetation species and wetland types. We validated this vegetation map (overall accuracy = 77.62%, Kappa = 0.72) and compared it with a published species map that used WorldView-2 data collected five years earlier on October 16, 2011. This allowed us to map changes resulting from five years of delta development to determine changes in wetland forest species (Salix nigra), two dominant herbaceous wetland species (Colocasia esculenta and Polygonum punctatum), and various grass species. Results show an increase in C. esculenta and a marginal increase in forested wetlands (S. nigra). C. esculenta's expansion occurred largely from the delta island heads toward the fringes into wetland area previously occupied by P. punctatum, which saw a corresponding decline. Additionally, this study leveraged these species distributions with a published aboveground biomass (AGB) dataset to examine the dominant plant types' growth patterns across elevational gradients. We characterized variability in AGB by marsh platform elevation and across different elevational zones categorized by hydroperiod, or hydrogeomorphic zones. We found that the herbaceous plant species peak in AGB in the low intertidal zone and decrease with elevation before increasing slightly in the higher elevations, consistent with previous field-based mesocosm experiments for C. esculenta. With vegetation distributions, succession, and growth patterns in the WLD characterized, this study may inform future restoration efforts throughout the Mississippi River Delta Plain regarding the changing vegetation composition that may emerge following changes in surface elevation with sediment deposition.

Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method

The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R2 = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R2 and low NRMSE in various datasets.

Fast hyperspectral imager driven by a low-cost and compact galvo-mirror

Imaging spectrometer is a critical device for remote sensing in visible and infrared band. Nowadays, it has become a powerful instrument to obtain three-dimensional hyperspectral cube (two-dimensional spatial information and one-dimensional spectral information) of targets for high precision object recognition. However, we often cannot meet the requirements of high spatial resolution and high spectral resolution at the same time. So there is still a trade-off between detection speed and data accuracy (including spatial and spectral resolution). In this paper, we developed a fast and high spectral resolution imaging spectrometer to obtain the spectral-spatial information with the help of a low-cost and compact galvo-mirror. The spectrometer has a wavelength resolution of about 10 nm at visible-near infrared band (400 nm–800 nm), and it can measure two-dimensional spatial images of various biological samples (including corals, dragon fruits and human hands) at various wavelengths repeatedly. This spectrometer can facilitate collection of a large number of spectral image data during half cycle of galvo-mirror scanning. The theoretical acquisition speed of hyperspectral cube can reach more than 1 MHz. However, at this acquisition speed, the signal-to-noise ratio of the camera is poor, and it is unable to obtain high-quality signals. We believe that with the improvement of camera performance, our prototype can obtain hyperspectral data at a higher sampling speed.

The Solar Orbiter SPICE instrument

Aims.The Spectral Imaging of the Coronal Environment (SPICE) instrument is a high-resolution imaging spectrometer operating at extreme ultraviolet wavelengths. In this paper, we present the concept, design, and pre-launch performance of this facility instrument on the ESA/NASA Solar Orbiter mission.Methods.The goal of this paper is to give prospective users a better understanding of the possible types of observations, the data acquisition, and the sources that contribute to the instrument’s signal.Results.The paper discusses the science objectives, with a focus on the SPICE-specific aspects, before presenting the instrument’s design, including optical, mechanical, thermal, and electronics aspects. This is followed by a characterisation and calibration of the instrument’s performance. The paper concludes with descriptions of the operations concept and data processing.Conclusions.The performance measurements of the various instrument parameters meet the requirements derived from the mission’s science objectives. The SPICE instrument is ready to perform measurements that will provide vital contributions to the scientific success of the Solar Orbiter mission.

Evaluating satellite-based evapotranspiration estimates for hydrological applications in data-scarce regions: A case in Ethiopia

Water resource development opens up opportunities for improving smallholder farmer livelihoods in sub-Saharan Africa; however, implementation of water resource interventions to ensure sustainability hinges on the availability of sufficient quantity and quality data for monitoring, analysis and planning. Such data is often acquired through instrumentation of water resources (e.g. stream flow monitoring) or the use of hydrological models. In sub-Saharan Africa, data scarcity has limited the ability to monitor and make appropriate decisions for water resource allocation and use. Data derived from remote sensing has been considered a viable option to fill this gap; however, there is limited research in the region that evaluate the quality of the remotely sensed based datasets. This study evaluated actual evapotranspiration (AET) estimates derived from Advanced Very High Resolution Radiometer (AVHRR AET) images and Moderate Resolution Imaging Spectrometer (MOD16 AET) images using estimates from a grid-based Soil and Water Assessment Tool (SWAT). The SWAT model was set up for the entire country of Ethiopia, and calibrated and validated using observed streamflow at several meso-scale watersheds in which satisfactory model performance was obtained. AET estimates from the calibrated and validated SWAT model were then used to evaluate remotely sensed based AET for three landscapes. The AVHRR AET better agreed with the SWAT-simulated AET than the MOD16 AET, although the AVHRR AET overestimated the SWAT-simulated AET in all of the landscapes. Both remotely sensed AET products showed better agreement with the SWAT-simulated AET over agriculture dominated landscapes compared to grassland and forest dominated landscapes. The findings of the study suggest that remotely sensed based AET may help to fine-tune hydrological models in agricultural landscapes in data-scarce regions to improve studies on the impacts of water management interventions aiming to ensure environmental sustainability while enhancing agricultural production, and household income and nutrition.

Local, Daily, and Total Bio-Optical Models of Coastal Waters of Manfredonia Gulf Applied to Simulated Data of CHRIS, Landsat TM, MIVIS, MODIS, and PRISMA Sensors for Evaluating the Error

The spatial–temporal resolution of remote data covers coastal water variability, but this approach offers a lower accuracy than in situ observations. Two of the major error sources occur due to the parameterization of bio-optical models and spectral capability of the remote data. These errors were evaluated by exploiting data acquired in the coastal waters of Manfredonia Gulf. Chlorophyll-a concentrations, absorption of the colored dissolved organic material at 440 nm (aCDOM440nm), and tripton concentrations measured in situ varied between 0.09–1.76 mgm−3, 0.00–0.41 m−1, and 1.97–8.90 gm−3. In accordance with the position and time of in situ surveys, 36 local models, four daily models, and one total bio-optical model were parameterized and validated using in situ data before applying to Compact High-Resolution Imaging Spectrometer (CHRIS) mode 1, CHRIS mode 2, Landsat Thematic Mapper (TM), Multispectral Infrared and Visible Imaging Spectrometer (MIVIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Precursore Iperspettrale della Missione Applicativa (PRISMA) simulated data. Concentrations retrieved from PRISMA data using local models highlighted the smallest errors. Because tripton abundance is great and tripton absorptions were better resolved than those of chlorophyll-a and colored dissolved organic material (CDOM), tripton concentrations were adequately retrieved from all data using total models, while only local models adequately retrieved chlorophyll-a concentrations and aCDOM440nm from CHRIS mode 1, CHRIS mode 2, MIVIS, and MODIS data. Therefore, the application of local models shows smaller errors than those of daily and total models; however, the capability to resolve the absorption of water constituents and analyze their concentration range can dictate the model choice. Consequently, the integration of more models allows us to overcome the limitations of the data and sensors.

Cooperative evolutionary classification algorithm for hyperspectral images

Reliable crop maps are a vital source of information for many economic sectors. Hyperspectral images and efficient supervised classification algorithms can help in obtaining these maps. One of the objectives is to use a spectroradiometer as a source of a priori information for training supervised classification algorithms. Although there are many supervised classification algorithms, few can be trained using collected spectral signatures, such as spectral angle mapper (SAM). This algorithm relies on setting the angle threshold values manually to separate different classes, and it is usually a random process. So, the second objective is to use an automated process to optimize the angle threshold values to improve the efficiency of the SAM algorithm. An innovative cooperative classification algorithm for hyperspectral images based on enhancing the supervised SAM algorithm performance using a genetic algorithm (GA) is presented. The improvement is based on selecting global optimal threshold angle values for different classes using GA. The efficiency of the developed cooperative evolutionary algorithm is proved by classifying hyperspectral images to create maps of major crops, such as wheat, potato, and alfalfa. The source of the hyperspectral images is the Compact High-Resolution Imaging Spectrometer onboard of the Proba satellite. The evaluation of the results showed that the new cooperative evolutionary algorithm classified hyperspectral images with the highest accuracy compared to well-known reliable supervised classification algorithms.