Spectral Instrument Research Articles

Mapping of Burnt surfaces in the Context of Protected Areas in the Sudanian Savannahs of Northern Cameroon: Contribution of Time Series of Sentinel-2 Images

In Africa, bushfires are common practices in savannah regions. Their impact, is the subject of many scientific debates. The protected areas, of North Cameroon are subjected to ambivalent effects of these fires. Using high-resolution remote sensing data, this work aims at assessing burned areas at the interannual and intraannual scale in order to explain the complex environmental dynamics of landscapes, to contribute to fire management in a context of protected areas. To achieve this, time series of sentinel-2 images from the Multi Spectral Instrument from the year 2015-2020 have been used. By the unitemporal and multitemporal methods, the development of spectral indices has made it possible to locate the burned surfaces and to determine the periods of the fires. Observations and field surveys aimed to understand the factors of fires using, their typology as well as the roles of the actors. The findings show that each spectral index has its own ability to detect burnt surfaces: The NDVI can detect fires only at the start of the dry season, the SAVI is suitable for identifying intermediate and late fires, while the NBR is best suited for separating burnt and unburnt surfaces throughout the season, regardless of the method employed. Depending on their periods of occurrence, the fires spread more in parks with a fairly extensive grass cover compared to overexploited ordinary areas. They then depend on socio-environmental factors, namely the state of land used, the continuity of the herbaceous cover and the social practices in place. The mapping and monitoring of burned surfaces by remote sensing therefore constitute a tool and a method for diagnosing the state of the herbaceous plant cover and for managing natural resources.

3D structured biochip for label free determinations at the point-of-need

A novel optical label-free biosensing approach is introduced aiming to remove the current limitations of optical biosensors in their application of on-site food analysis. The proposed methodology (GRADual thin film IntErferometry, GRADE) is based on light interferometry employing an illumination source of a few nm spectral width and a 3D micro-patterned sensor surface consisting of multilevel staircase structures, fabricated through grayscale electron beam lithography and appropriate pattern transfer on thick dielectric layer. The formation of the biomolecular adlayer on the 3D structured surface sensor, is monitored with a 2D camera, without the need for broadband illumination sources and spectral analysis instrumentation. Consequently, this novel sensing approach has the potential to enable the cost-efficient monitoring of bioreactions employing a simple optical setup, a low-cost photodetector and light source, thus being an ideal platform for use at the Point-of-Need. In the present study the system is demonstrated for the detection of aflatoxin B 1 (AFB1), a very potent carcinogen detected in various food categories. • A novel optical label-free biosensing approach is introduced aiming to remove the current limitations of optical biosensors. • Based on a sensing area with a gradually-varying-thickness dielectric, fabricated through grayscale e-beam lithography. • Cost-efficient monitoring of bioreactions, ideal platform for use at the Point-of-Need.

Development of a quasi-coaxis dual-energy flat spectral response X-ray imaging instrument for measuring hotspot electron temperature.

The measurement of hotspot electron temperature is a paramount technique of implosion physics research in inertial confinement fusion. This study proposes a novel quasi-coaxis dual-energy flat spectral response high-resolution X-ray imaging instrument comprising a dual-channel total-reflection Kirkpatrick-Baez microscope and two flat non-periodic multilayer mirrors, which can image at 6.4 ± 0.5 and 9.67 ± 0.5 keV simultaneously. Various theoretical simulations were performed to verify the performance and feasibility of the imaging instrument, which was assembled and characterized in a laboratory. Experimental results show that the imaging instrument could achieve a high spatial resolution of 5 µm in a ± 150 µm field of view (FOV), the root mean square(RMS) deviation values of the measured reflection efficiency are 1.71% and 1.82% for the 6.4 keV and 9.67 keV imaging channels, respectively, in the ± 150 µm FOV.

Global Land Cover Mapping Using Annual Clear-sky Composites from FY3D/MERSI-II

ABSTRACT Medium Resolution Spectral Imager-II (MERSI-II) on board FY-3D satellite is a visible and infrared spectral imaging instrument featured with six 250-m spatial resolution channels (blue, green, red, NIR, 10.8 µm, 12.0 µm). Such unique spectral and spatial attributes provide significant opportunities and challenges for land cover (LC) mapping research. This paper reports the production of a 250-m spatial resolution global LC classification (under IGBP scheme) from MERSI-II NDVI operational products from May 2019 to April 2020. To extract robust features for discriminating LC types, we adopted three classic compositing criteria (MaxNDVI, Mean8NDVI, and Mean4BT) and two new compositing criteria (3minRedBlue and 3minRedNIR) to obtain high-quality annual composites. Random Forest (RF) algorithm was used for supervised learning, and 10-fold cross validation (CV) based on ‘site-splits’ was implemented to evaluate classification accuracy. The derived MERIS-II LC map was compared at pixel level with two widely used LC products (MODIS and VIIRS), and consistency among the three maps was investigated. Results showed that the higher resolution afforded by 250-m MERSI-II data in combination with a refined input features produced a global LC map with an overall accuracy of 79–86%. Most classes exhibited low commission and omission errors except for some classes of mixed nature (i.e. wetland, cropland/natural vegetation mosaic, savanna, woody savanna). The MERSI-II LC map generally agreed better with MODIS than with VIIRS, and the proportion of land pixels (excluding Antarctic) for which MERSI-II shows the same LC label with either MODIS or VIIRS is very large (0.70–0.82). Composite data contributed differently to total accuracy, with Mean8NDVI being the most spatial coherent and most contributive and MaxNDVI being the opposite. The other three composites (Mean4BT, 3minRedBlue, and 3minRedNIR) showed moderate spatial coherence and contributed to classification variedly by extracting information from different phenology phases. The unique 250-m blue and green bands of MERSI-II were found to be helpful to discriminate between wetland and needleleaf forest in high latitudes and between cities and grassland. This study confirms that with the RF algorithm (one of the state-of-art supervised machine learning algorithms) and image compositing techniques, accurate LC map can be derived from MERSI-II. Future work will attempt to include more bands and compositing criteria to further improve classification accuracy.

Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data

ABSTRACT A recent study has proposed and tested a semi-empirical method to estimate crop irrigation based on a water balance logic and Sentinel-2 Multi Spectral Instrument (MSI) NDVI imagery. The current paper aims at extending the same approach to the analysis of the main irrigation patterns occurred in Tuscany (Central Italy) during the 2000–2019 period. This operation was made possible by feeding the irrigation water (IW) estimation method with 250-m spatial resolution Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The results of this operation were first assessed versus various reference datasets available for the region; next, the annual maps of IW estimated for the 20 study years were analyzed at province scale in conjunction with relevant agricultural statistics. The use of MODIS in place of MSI images reduces the IW estimation accuracy irregularly at local scale, depending on the size and spatial arrangement of irrigated and non-irrigated fields; the reduction in accuracy is, however, marginal over relatively large areas. Irrigated crops are decreasing throughout most Tuscany provinces, while they are increasing in the most southern and driest province. The possible reasons and implications of these findings are finally discussed in relation to the main environmental issues affecting the region.

Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite

This study presents a methodology that focuses on detecting agricultural burned areas using Sentinel-2 multispectral data at 10 m. We developed a simple, locally adapted, straightforward approach of multi-index threshold to extract post-winter agricultural burned areas at high resolution for 2019-21. Further, we design a new method for virtual sample collection using already validated fire location data and visual interpretation conditioned using strict selection criteria to improve sample accuracy. Sampling accuracy showed near-perfect agreement with an average Cohen's Kappa value of 0.98. We retrieved monthly ABAs at a resolution of 10 m, and these products were validated against reference burned sample plots identified using visual interpretation of Planet (3m) satellite data. Overall, we found that our method performed well, with an F1 score of 83.63% and low commission (20%) and omission (7%) errors. When compared to global burnt area products, validation accuracy demonstrated an exceptional subpixel scale detecting capability. The study also addresses the complexity of residue burnings and burn signatures’ volatile nature by performing multilevel masking and temporal corrections.•A novel remotely sensed data aided virtual sampling approach to acquire burned and unburned samples.•An integrated method to extract smallholder agricultural burned area using Sentinel-2 multispectral data at a high resolution of 10 m

Conversion of Central Subfield Thickness Measurements of Diabetic Macular Edema Across Cirrus and Spectralis Optical Coherence Tomography Instruments.

PurposeDevelop equations to convert Cirrus central subfield thickness (CST) to Spectralis CST equivalents and vice versa in eyes with diabetic macular edema (DME).MethodsThe DRCR Retina Network Protocol O data were split randomly to train (70% sample) and validate (30% sample) conversion equations. Data from an independent study (CADME) also validated the equations. Bland–Altman 95% limits of agreement between predicted and observed values evaluated the equations.ResultsProtocol O included 374 CST scan pairs from 187 eyes (107 participants). The CADME study included 150 scan pairs of 37 eyes (37 participants). Proposed conversion equations are Spectralis = 40.78 + 0.95 × Cirrus and Cirrus = 1.82 + 0.94 × Spectralis regardless of age, sex, or CST. Predicted values were within 10% of observed values in 101 (90%) of Spectralis and 99 (88%) of Cirrus scans in the validation data; and in 136 (91%) of the Spectralis and 148 (99%) of the Cirrus scans in the CADME data. Adjusting for within-eye correlations, 95% of conversions are estimated to be within 17% (95% confidence interval, 14%–21%) of CST on Spectralis and within 22% (95% confidence interval, 18%–28%) of CST on Cirrus.ConclusionsConversion equations developed in this study allow the harmonization of CST measurements for eyes with DME using a mix of current Cirrus and Spectralis device images.Translational RelevanceThe CSTs measured on Cirrus and Spectralis devices are not directly comparable owing to outer boundary segmentation differences. Converting CST values across spectral domain optical coherence tomography instruments should benefit both clinical research and standard care efforts.

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

Revisited Estimation of Moderate Resolution Sargassum Fractional Coverage Using Decametric Satellite Data (S2-MSI)

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil and West Africa, leading to significant environmental and socio-economic impacts. The AFAI algal index (Alternative Floating Algae Index) is often used with remote sensing data in order to estimate the Sargassum coverage, and more precisely the AFAI deviation, which consists of the difference between AFAI and AFAI of the Sargassum-free background. In this study, the AFAI deviation is computed using NASA’s 1 km Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and ESA/Copernicus’s 20 m Sentinel-2/MSI (Multi Spectral Instrument) for the same sites and at the same time. Both MODIS and MSI AFAI deviations are compared to confirm the relevance of AFAI deviation technique for two very different spatial resolutions. A high coefficient of determination was found, thus confirming a satisfactory downsampling from 20 m (MSI) to 1 km (MODIS). Then, AFAI deviations are used to estimate the fractional coverage of Sargassum (noted FC). A new linear relationship between the MODIS AFAI deviation and FC is established using the dense Sargassum aggregations observed by MSI data. The AFAI deviation is proportional to FC with a factor of proportionality close to 0.08. Finally, it is shown that the factor is dependent on the Sargassum spectral reflectance, submersion or physiological state.

X-ray simulations of polar gas in accreting supermassive black holes

ABSTRACT Recent observations have shown that a large portion of the mid-infrared (MIR) spectrum of active galactic nuclei (AGNs) stems from the polar regions. In this paper, we investigate the effects of this polar gas on the X-ray spectrum of AGNs using ray-tracing simulations. Two geometries for the polar gas are considered, (1) a hollow cone corresponding to the best-fitting MIR model and (2) a filled cone, both with varying column densities (ranging from 1021 to 1022.5 cm−2) along with a torus surrounding the central X-ray source. We find that the polar gas leads to an increase in the equivalent width of several fluorescence lines below ∼5 keV (e.g. O, Ne, Mg, Si). A filled geometry is unlikely for the polar component, as the X-ray spectra of many type 1 AGNs would show signatures of obscuration. We also consider extra emission from the narrow-line region such as a scattered power law with many photoionized lines from obscured AGNs, and different opening angles and matter compositions for the hollow cone. These simulations will provide a fundamental benchmark for current and future high spectral resolution X-ray instruments, such as those onboard XRISM and Athena.

Synthesis and characterization of Cu(II)-pyrazole complexes for possible anticancer agents; conformational studies as well as compatible in-silico and in-vitro assays

New pyrazole derivatives were prepared and used to synthesize new bioactive agents from Cu(II) complexes that have OSN donors. Analytical and spectral (IR, UV-Vis, MS, 1H NMR, ESR & XRD) instruments characterized these complexes as well as their corresponding ligands. The bonding mode has been modified from ligand to ligand and the molar ratio for isolated complexes has also varied (1:1/1:2, M:L). The geometry of isolated complexes was commonly proposed, based on electronic transitions and ESR spectral-parameters. Via computational approaches, these structures were optimized using standard programs (Gaussian 09 & HyperChem 8.1) under the required basis set. Consequently, important physical characteristics have been obtained after finishing the optimization process. Inhibition behavior of all new synthesizes was studied by MOE module as in-silico approach which conducted versus the crystal structure of NUDT5 protein (6gru) of breast cancer cells. The interaction features summarized from docking processes, reveal effective inhibition validity for new Cu(II) complexes versus breast cancer cells. This according to scoring energy values and the stability of docking complexes in true interaction path (bond length ≤3.5 Å) particularly with Cu(II)-L3 and Cu(II)-L4 complexes. This reflects the possibility of successful behavior during practical application through in-vitro assay that intended in this study. Finally, the degree of toxicity of such new compounds to the breast cancer cell line was determined by in-vitro screening. To judge perfectly on their toxicity, in-vitro screening must compared to positive control as Doxorubicin (reference drug). IC50 values were calculated and represent Cu(II) complexes as outstanding cytotoxic agents which revealed superiority on the reference drug itself.

Application of Near Infrared Spectroscopy and Hyperspectral Imaging Technology to Detect Fruit Maturity

Maturity, as an essential evaluation index of fruit quality, is closely related to the harvest, storage, processing, transportation, sales and other links of fruit, and is also one of the key factors affecting its output and quality. In this paper, the research status of fruit maturity detection using near-infrared spectroscopy and hyperspectral imaging technology in the recent ten years are reviewed. Starting from qualitative identification of fruit maturity and quantitative prediction of maturity parameters, the effects of spectral instrument working band, spectral acquisition mode, spectral sampling area, maturity characterization factor, single maturity parameter, and multiple maturity index on the accuracy and stability of the final detection model were analyzed in detail. Finally, the development trend of near-infrared spectroscopy and hyperspectral imaging technology in fruit maturity detection has been prospected in order to provide a scientific basis and technical reference for related research work.

Kink Oscillations of Coronal Loops

Kink oscillations of coronal loops, i.e., standing kink waves, is one of the most studied dynamic phenomena in the solar corona. The oscillations are excited by impulsive energy releases, such as low coronal eruptions. Typical periods of the oscillations are from a few to several minutes, and are found to increase linearly with the increase in the major radius of the oscillating loops. It clearly demonstrates that kink oscillations are natural modes of the loops, and can be described as standing fast magnetoacoustic waves with the wavelength determined by the length of the loop. Kink oscillations are observed in two different regimes. In the rapidly decaying regime, the apparent displacement amplitude reaches several minor radii of the loop. The damping time which is about several oscillation periods decreases with the increase in the oscillation amplitude, suggesting a nonlinear nature of the damping. In the decayless regime, the amplitudes are smaller than a minor radius, and the driver is still debated. The review summarises major findings obtained during the last decade, and covers both observational and theoretical results. Observational results include creation and analysis of comprehensive catalogues of the oscillation events, and detection of kink oscillations with imaging and spectral instruments in the EUV and microwave bands. Theoretical results include various approaches to modelling in terms of the magnetohydrodynamic wave theory. Properties of kink oscillations are found to depend on parameters of the oscillating loop, such as the magnetic twist, stratification, steady flows, temperature variations and so on, which make kink oscillations a natural probe of these parameters by the method of magnetohydrodynamic seismology.

Uncertainty analysis of SVD-based spaceborne far–red sun-induced chlorophyll fluorescence retrieval using TanSat satellite data

The singular value decomposition (SVD) method, a sun-induced chlorophyll fluorescence (SIF) retrieval approach, has been used widely in far-red SIF spaceborne retrievals on a global scale. However, due to its semi-empirical nature, setting different parameter values may affect its retrieval accuracy, and ultimately have a large impact on its application. Hence, in this study, we evaluated the impact of parameter selection on the far-red SIF retrieval of this approach using TanSat satellite data. We first retrieved the far-red SIF within a narrow spectral window of 757.4–759.2 nm using the first four singular vectors (SVs) that were derived from the snow and soil spectra in a globally distributed training set. The retrievals are highly consistent with TanSat mission SIF (R2 = 0.72), indicating the reliability of our retrieval results. Then, the uncertainty was executed based on the above SIF retrievals and evaluated from five metrics: the number of SVs, length of the fitting window, type of training set elements, proportion of training set elements, and spatial distribution of the training set. Results showed that an unwise selection of the number of SVs would result in large retrieval errors (R2 = 0.03). Meanwhile, a fitting window that is too short or does not include a strong Fraunhofer line would cause severe errors and a large number of negative values (R2 = 0.06). Further, failure to include training samples below the equatorial zone in the Southern Hemisphere, and high latitude samples in the Northern Hemisphere, leads to poorer outcomes (R2 = 0.57). In contrast, the other global sampling metrics had little effect on the retrieval results (generally, R2 > 0.97). Accordingly, the relevant suggestions for using this method in the future are listed in this study for reference, with the potential to improve the accuracy of SIF retrievals from ultra-high spectral resolution instruments in the far-red spectrum.

High-precision photometry with Ariel

In this paper we describe the photometry instruments of Ariel, consisting of the VISPhot, FGS1 and FGS2 photometers in the visual and mid-IR wavelength. These photometers have their own cadence, which can be independent from each other and the cadence of the spectral instruments. Ariel will be capable to do high cadence and high precision photometry in independent bands. There is also a possibility for synthetic Jsynth, Hsynth, and wide-band thermal infrared photometry from spectroscopic data. Although the cadence of the synthetic bands will be identical to that of the spectrographs, the precision of synthetic photometry in the suggested synthetic bands will be at least as precise as the optical data. We present the accuracy of these instruments. We also review selected fields of new science which will be opened up by the possibility of high cadence multiband space photometry, including stellar rotation, spin-orbit misalignment, orbital precession, planetary rotation and oblateness, tidal distortions, rings, and moons.

Effect of smart phone cameras on color-based prediction of soil organic matter content

Compared with the complicated operation of traditional laboratory methods or expensive spectral instruments, soil organic matter (SOM) content prediction based on smart phone photos has recently received heightened attention. However, as one of the most popular mobile devices, the imaging characteristics of smart phone cameras are quite different due to the differences in manufacturer technologies, which may affect the relationship between the photo colors and SOM content. Whether the highly accurate model built based on a single phone can be applied to other phone types is still an open question. This study has validated the shared capacity of color-based prediction models, analyzed the intrinsic factors affecting the shared capacity, and proposed potential methods to enhance the shared capacity. In total, five smart phones were selected for the study, and dried soil samples were photographed in an optical dark chamber. Imaging spectroscopy was used to scan the samples. The photo and spectral data were pretreated, and stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) models were built. The spectral response curves of the five smart phone cameras were also obtained separately to clarify their imaging characteristics. Results indicated the RGB color distribution conditions of soil photos obtained by different phones were different, which affects the correlation between the color parameters and SOM. The prediction ability of the models constructed by the five smart phones were similar to the spectral devices, achieving an R2 of 0.68–0.77 and an RMSE of 5.32–7.12 g/kg. However, when substituting the color parameter datasets obtained by the five phones into the models constructed by the other phones to verify the shared capacity, we found that most of the prediction results could not meet the requirements for use. The poor shared capacity might be extremely disruptive to users. We proposed several potential methods that might enhance the model’s shared capacity. The results of this study showed that smart phone cameras have a good capability of modeling SOM content independently, but the shared capacity of different phones still needs further investigation.

Applications of AOTF Spectrometers in In Situ Lunar Measurements.

Spectrometers based on acousto-optic tunable filters (AOTFs) have several advantages, such as stable temperature adaptability, no moving parts, and wavelength selection through electrical modulation, compared with the traditional grating and Fourier transform spectrometers. Therefore, AOTF spectrometers can realize stable in situ measurement on the lunar surface under wide temperature ranges and low light environments. AOTF imaging spectrometers were first employed for in situ measurement of the lunar surface in the Chinese Chang’e project. The visible and near-infrared imaging spectrometer and the lunar mineralogical spectrometer have been successfully deployed on board the Chang’e-3/4 and Chang’e-5 missions. In this review, we investigate the performance indicators, structural design, selected AOTF performance parameters, data acquisition of the three lunar in situ spectral instruments used in the Chang’e missions. In addition, we also show the scientific achievement of lunar technology based on in situ spectral data.

Micro two-dimensional slit-array for super resolution beyond pixel Nyquist limits in grating spectrometers.

This paper presents a new micro 2-D slit-array device for spectral resolution enhancement in grating spectrometers. The 2-D slit-array is encoded in Hadamard matrix and the device is fabricated based on the micro-electromechanical system (MEMS) technology. By just using this 2-D slit-array to replace the single slit in the conventional grating spectrometer, real-time super spectral resolution detection beyond the pixel Nyquist limit, which is determined by the size of the detector pixel, can be realized. Furthermore, no other configuration of the spectrometer is changed, no movable parts are used, and the spectral range and instrument size remain almost unchanged while the resolution is improved. A series of experimental verifications for the feasibility of this design are included in this work.

Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: Challenges and potentials

Despite the importance of remote detection of marine debris, nearly all published studies are focused on either controlled experiments, or Sentinel-2 data with mixed band resolutions that are subject to large uncertainties. To date, key questions such as the following have not been addressed adequately: To what extent can the various forms of marine debris be remotely detected and differentiated through satellite observations in the visible and near infrared (NIR) spectral range, and how? Here, using published reflectance spectra of various types of floating matters, I address these questions through sensitivity analyses, simulations, and spectral analyses of satellite images. While the study is by no means comprehensive, several observations can still be made. First, it appears impossible to remotely detect marine microplastics from all existing and planned optical sensors. This is simply because the contribution of these particles to the sensor signal, even when they are aggregated on the water surface at the reported maximum particle density, is at least 60 times lower than the required signal (~0.2% subpixel coverage) and 20 times lower than the sensor noise for a sensor with a signal-to-noise ratio (SNR) of 200. In contrast, detecting macroplastics and other debris is possible when they form large patches along ocean fronts or windrows. Second, assuming a SNR of 200, discriminating large patches of marine debris from floating algae is only possible with a subpixel coverage of >0.3%. These threshold values are based on the sensor SNRs only, and they represent the lower bounds of detection and discrimination, respectively. The real threshold values above which a detection or discrimination is possible also depend on the observing conditions, and therefore higher. Third, currently, Sentinel-2 MSI (Multi Spectral Instrument) sensors provide an optimal trade between resolution and coverage, yet MSI sensors have SNRs <200, and interpretation of the MSI spectra requires extra caution due to variable spatial resolutions in different bands, among other factors. From the perspective of pure spectroscopy, it is possible to discriminate floating algae from non-algae floating matters but difficult to differentiate the type of the latter (either plastic or non-plastic debris, foam, etc) because different non-algae floating matters all show relatively flat reflectance spectral shapes in the vis-NIR spectral range. Finally, based on these results, recommendations are made on algorithm designs and sensor designs, for example spectral analysis should be performed over the difference spectra to minimize the impact of variable subpixel coverage, and certain spectral bands are more important than others for the remote detection of marine debris.

Multichannel autocalibration for the Atmospheric Imaging Assembly using machine learning

Context. Solar activity plays a quintessential role in affecting the interplanetary medium and space weather around Earth. Remote-sensing instruments on board heliophysics space missions provide a pool of information about solar activity by measuring the solar magnetic field and the emission of light from the multilayered, multithermal, and dynamic solar atmosphere. Extreme-UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, that is, the chromosphere and the corona. Unfortunately, instruments such as the Atmospheric Imaging Assembly (AIA) on board the NASA Solar Dynamics Observatory (SDO), suffer from time-dependent degradation that reduces their sensitivity. The current best calibration techniques rely on flights of sounding rockets to maintain absolute calibration. These flights are infrequent, complex, and limited to a single vantage point, however. Aims. We aim to develop a novel method based on machine learning (ML) that exploits spatial patterns on the solar surface across multiwavelength observations to autocalibrate the instrument degradation. Methods. We established two convolutional neural network (CNN) architectures that take either single-channel or multichannel input and trained the models using the SDOML dataset. The dataset was further augmented by randomly degrading images at each epoch, with the training dataset spanning nonoverlapping months with the test dataset. We also developed a non-ML baseline model to assess the gain of the CNN models. With the best trained models, we reconstructed the AIA multichannel degradation curves of 2010–2020 and compared them with the degradation curves based on sounding-rocket data. Results. Our results indicate that the CNN-based models significantly outperform the non-ML baseline model in calibrating instrument degradation. Moreover, multichannel CNN outperforms the single-channel CNN, which suggests that cross-channel relations between different EUV channels are important to recover the degradation profiles. The CNN-based models reproduce the degradation corrections derived from the sounding-rocket cross-calibration measurements within the experimental measurement uncertainty, indicating that it performs equally well as current techniques. Conclusions. Our approach establishes the framework for a novel technique based on CNNs to calibrate EUV instruments. We envision that this technique can be adapted to other imaging or spectral instruments operating at other wavelengths.