Spectral Index Research Articles

Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

Sensitivities of Spectral Optical Properties of Dust Aerosols to Their Mineralogical and Microphysical Properties

AbstractDust aerosol is a key component in global radiative forcing and climate modeling. We developed a mineralogy‐resolved spectral optical property model for dust aerosols. The model incorporates nine mineral groups and applies the effective medium methods to obtain the spectral complex refractive indices of inhomogeneous dust particles. This study considers 5,000 sets of dust mineral composition mixtures along with the use of the bimodal particle size distribution. We calculate the bulk properties of these samples in a wide spectral range from 0.2 to 50 μm using a comprehensive database of hexahedral dust single‐scattering properties. Through sensitivity analyses, this study reveals that the illite and hematite components substantially affect the optical properties of dust. Furthermore, the effective radii of both the fine‐mode and the coarse‐mode dust particles have significant impacts on the bulk optical properties, albeit to varying degrees.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the “trilateral” parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0–2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the “trilateral” parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0–2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Using spectral vegetation indices and machine learning models for predicting the yield of sugar beet (Beta vulgaris L.) under different irrigation treatments

Yield prediction is essential for production planning, resource management, and competitive advantages. The use of vegetation indices and machine learning for yield prediction is rapid, practical, and accurate and contributes to the development of digital agriculture. Although many studies have been carried out on yield estimation, the number of studies on sugar beet and irrigation applications is limited. To solve this problem, in this study yield prediction of sugar beet under different irrigation treatments was carried out using spectral reflection-based vegetation indices obtained by proximal measurements and developed machine learning models. The multilayer perceptron (MLP), random forest (RF), k-nearest (kNN), bagging (BAG), support vector regression (SVR), gaussian processes (GP), and CustomNet (CN) predictors were used in yield prediction. The irrigation treatments included I100 (application of irrigation to increase the available moisture to field capacity by around 45–50% of the water holding capacity at the root depth), I75 (75% of the water applied in the control group), I50 (50% of the water applied in the control group), and I125 (25% of the water applied in the control group). The combinations of vegetation indices were determined using Pearson correlation and principal component analysis. OSAVI, SAVI, and NDVI had the most significant influence on the yield prediction of sugar beet. Among the combinations used, the kNN1 model (including all attributes as input) showed high R2 values of 0.99 for training and 0.64 for testing. In addition, the kNN3 model (including Treatment, NDVI, NWI, and OSAVI as inputs) had R2 values of 0.97 for training and 0.65 for testing, indicating successful outcomes. However, the MLP1 and SVR1 models had lower results with test R2 values of 0.53 and 0.56, respectively. The results demonstrate the successful application of machine learning and vegetation indices for yield prediction. The proposed approaches could encourage agricultural experts and researchers in future work for yield mapping and identification.

Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images

Abstract. This work aims to compare and assess the performance of certain methodologies for shoreline mapping based on the use of medium (10 m) and high resolution (3 m) multi-spectral imagery, provided by Sentinel-2 (S2) and PlanetScope (PS), respectively. Being Sentinel-2 part of the Copernicus missions, its data are freely available. PS imagery are also freely available for scientific research, upon approval by the European Space Agency of a related project proposal. Several spectral indices, including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), and Water Index (WI), were used for shoreline detection. In particular, two unsupervised classification techniques, the Gaussian Mixture Model (GMM) and K-means clustering were deployed as shoreline extraction methods. The outcomes of such approaches were validated using reference shorelines derived from aerial orthomosaics, generated from images acquired as close as possible to the satellite imagery dates, and the ”baseline and transect” approach for accuracy verification. Three tide-less Mediterranean beaches were used as study cases for comparison: the beach between Castelldefels and Gava in Spain, Feniglia and Marina di Grosseto in Italy. The results demonstrated sub-pixel accuracy in shoreline extraction, with Mean Absolute Distances ranging from 2 m to 5 m for S2 data and 1.5 m to 2 m for PS data. These findings highlight the potential of freely available satellite data for semi-automatic shoreline detection. Results obtained by using the combination of different indices and methodologies show that the best option may change depending on the considered context, hence future investigations should be dedicated to the development of a procedure for automatically determining the context-based (close to) optimal index-classifier combination.

The time-series GF-1 WFV data monitoring of sugarcane using a Random Forest Algorithm in South China

Abstract. There is large distribution of sugarcane growth in south China which is play an important role of sugar industry. Remote sensing technology is used in sugarcane monitoring for large areas. However, the optical satellite data coverage is influenced by the rainy weather especially in the grand growth period of sugarcane. GF-1 WFV has widely swath 800km and short revisit time which is ideal data for this study area. In this paper, the random forest model was chosen to get a precise classification result of sugarcane based on time-series band value and 5 spectral indexes image is 89.73% and the Kappa coefficient is 0.65 which is satisfied the overall extraction of sugarcane for large area is the southern China. Furthermore, the decision tree classification was chosen as a comparative experience research.

Unmixing-based forest recovery indicators for predicting long-term recovery success

Recovery from forest disturbances is a pivotal metric of forest resilience. Forests globally are facing unprecedented levels of both natural and anthropogenic disturbances, yet our understanding of their recovery from these disturbances remains incomplete. Remote sensing is an effective tool for understanding post-disturbance recovery, but existing approaches largely rely on spectral recovery indicators that are difficult to interpret and require long time series after disturbance, which limits their applicability to recent disturbance pulses. We here introduce a novel, ecologically informed set of recovery indicators based on fractional cover maps derived from spectral unmixing analysis of Landsat and Sentinel-2 time series. We estimated annual pre- and post-disturbance tree cover and bare ground fractions over the eastern Alps (∼130,000 km2) for the period from 1990 to 2021. From these fraction time series, we derived recovery intervals defined as the time it takes to reach a pre-defined tree cover threshold after disturbance, referred to as canopy recovery. We found mean recovery intervals between 5.5 and 13.4 years, depending on recovery threshold and disturbance severity. Comparing our results to traditional remote sensing-based approaches of mapping forest recovery, we found that spectral unmixing-based recovery indicators give considerably more realistic recovery intervals than approaches based on spectral indices because they effectively distinguish tree regeneration from other post-disturbance vegetation (e.g., shrubs, grasses). Finally, we were able to accurately predict the long-term forest recovery success based on the information available only three years after disturbance, which underlines the high importance of a short window of reorganization post-disturbance, and highlights the utility of remote sensing to inform post-disturbance forest management (e.g., in identifying areas in need of tree planting). Our study thus provides an important step ahead in the remote sensing-based monitoring of forest recovery and resilience, which is urgently needed in a time of rapid forest change.

An arcsecond view at 1–2 GHz into the Galactic Bulge

Abstract We present the results of a high angular resolution (1.1”) and sensitivity (maximum of ∼0.1 mJy) radio survey at 1–2 GHz in the Galactic Bulge. This complements the X-ray Chandra Galactic Bulge Survey, and investigates the full radio source population in this dense Galactic region. Radio counterparts to sources at other wavelengths can aid in classification, as there are relatively few types of objects that are reasonably detectable in radio at kiloparsec distances, and even fewer that are detected in both X-rays and radio. This survey covers about 3 square degrees of the Galactic Bulge Survey area (spanning the Galactic coordinate range of −3○ < l < +3○ and +1.6○ < b < +2.1○) as a first look into this region of the Galaxy with this combination of frequency, resolution, and sensitivity. Spectral indices within the observed band of 1–2 GHz were calculated for each source to assist in determining its emission mechanism. We find 1617 unique sources in the survey, 25 of which are radio counterparts to X-ray sources, and about 100 of which are steep-spectrum (α ≲ −1.4) point sources that are viable pulsar candidates. Four radio sources are of particular interest: a compact binary; an infrared transient with an inverted radio spectrum; a potential transitional millisecond pulsar candidate; and a very steep spectrum radio source with an X-ray and bright infrared counterpart. We discuss other notable sources, including possible radio transients, potential new planetary nebulae, and active galactic nuclei.

Multispectral analysis-ready satellite data for three East African mountain ecosystems

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.

H.E.S.S. observations of the 2021 periastron passage of PSR B1259-63/LS 2883

is a gamma-ray binary system that hosts a pulsar in an eccentric orbit, with a 3.4 year period, around an O9.5Ve star (LS 2883). At orbital phases close to periastron passages, the system radiates bright and variable non-thermal emission, for which the temporal and spectral properties of this emission are, for now, poorly understood. In this regard, very high-energy (VHE) emission is especially useful to study and constrain radiation processes and particle acceleration in the system. We report on an extensive VHE observation campaign conducted with the High Energy Stereoscopic System, comprised of approximately 100 hours of data taken over five months, from p -24$ days to $t_ p +127$ days around the system's 2021 periastron passage (where $t_ p $ is the time of periastron). We also present the timing and spectral analyses of the source. The VHE light curve in 2021 is consistent overall with the stacked light curve of all previous observations. Within the light curve, we report a VHE maximum at times coincident with the third X-ray peak first detected in the 2021 X-ray light curve. In the light curve -- although sparsely sampled in this time period -- we see no VHE enhancement during the second disc crossing. In addition, we see no correspondence to the 2021 GeV flare in the VHE light curve. The VHE spectrum obtained from the analysis of the 2021 dataset is best described by a power law of spectral index $ stat sys $, a value consistent with the spectral index obtained from the analysis of data collected with H.E.S.S. during the previous observations of the source. We report spectral variability with a difference of $ stat sys $ at $95 <!PCT!>$ confidence intervals, between sub-periods of the 2021 dataset. We also detail our investigation into X-ray/TeV and GeV/TeV flux correlations in the 2021 periastron passage. We find a linear correlation between contemporaneous flux values of X-ray and TeV datasets, detected mainly after $t_ p +25$ days, suggesting a change in the available energy for non-thermal radiation processes. We detect no significant correlation between GeV and TeV flux points, within the uncertainties of the measurements, from $ t_p-23$ days to $ t_p+126$ days. This suggests that the GeV and TeV emission originate from different electron populations.

Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network

ABSTRACT This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

Abell 0399-Abell 0401 radio bridge spectral index: First multi-frequency detection

Recent low-frequency radio observations at 140 MHz discovered a bridge of diffuse emission with a length of 3 Mpc that connects the galaxy clusters Abell 0399 and Abell 0401. We present follow-up observations at 60 MHz to constrain the spectral index of the bridge, which has only been detected at 140 and 144 MHz so far. We analysed deep (sim 18 hours) LOw Frequency ARray (LOFAR) Low Band Antenna (LBA) data at 60 MHz to detect the bridge at very low frequencies. We then conducted a multi-frequency study with LOFAR HBA data at 144 MHz and uGMRT data at 400 MHz. Assuming second-order Fermi mechanisms for the re-acceleration of relativistic electrons driven by turbulence in the radio bridge regions, we compared the observed radio spectrum with theoretical synchrotron models. The bridge is detected in the $75''$ resolution LOFAR image at 60 MHz, and its emission fully connects the region between the two galaxy clusters. Between 60 MHz and 144 MHz, we found an integrated spectral index value of $ 0.16$ for the bridge emission. For the first time, we produced spectral index and related uncertainties maps for a radio bridge. We produce a radio spectrum that shows a significant steepening between 144 and 400 MHz. This detection at low frequencies provides important information for models of particle acceleration and magnetic field structure on very extended scales. The spectral index gives important clues about the origin of inter-cluster diffuse emission. The steepening of the spectrum above 144 MHz can be explained in a turbulent re-acceleration framework, assuming that the acceleration timescales are longer than sim 200 Myr.

Hyperspectral Imaging Spectroscopy for Non-Destructive Determination of Grape Berry Total Soluble Solids and Titratable Acidity

Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids (TSS) and titratable acidity (TA) in wine grape berries. A normalized difference spectral index (NDSI) spectral preprocessing method was built and compared with the conventional preprocessing method: multiplicative scatter correction and Savitzky–Golay smoothing (MSC+SG). Different machine learning models were built to examine the performance of the preprocessing methods. The results show that the NDSI preprocessing method demonstrated better performance than the MSC+SG preprocessing method in different classification models, with the best model correctly classifying 93.8% of the TSS and 84.4% of the TA. In addition, the TSS can be predicted with moderate performance using support vector regression (SVR) and MSC+SG preprocessing with a root mean squared error (RMSE) of 0.523 °Brix and a coefficient of determination (R2) of 0.622, and the TA can be predicted with moderate performance using SVR and NDSI preprocessing (RMSE = 0.19%, R2 = 0.525). This study demonstrates that hyperspectral imaging data and NDSI preprocessing have the potential to be a method for grading wine grapes for producing quality wines.

Aquatic vegetation mapping with UAS-cameras considering phenotypes

Aquatic vegetation species at the genus level in an oxbow lake were identified in Hungary based on a multispectral Uncrewed Aerial System (UAS) survey within an elongated oxbow lake area of the Tisza River under continental climate. Seven and 13 classes were discriminated using three different classification methods (Support Vector Machine [SVM], Random Forest [RF], and Multivariate Adaptive Regression Splines [MARS]) using different input data in ten combinations: original spectral bands, spectral indices, Digital Surface Model (DSM), and Haralick texture indices. We achieved a high (97.1%) overall accuracies (OAs) by applying the SVM classifier, but the RF performed only <1% worse, as it was represented in the first places of the classification rank before the MARS. The highest classification accuracies (>84% OA) were obtained using the most important variables derived by the Recursive Feature Elimination (RFE) method. The best classification required DSM as an input variable. The poorest classification performance belonged to the model that used only texture indices or spectral indices. On the class level, Stratiotes aloides exhibit the lowest degree of separability compared to the other classes. Accordingly, we recommend using supplementary input data for the classifications besides the original spectral bands, for example, DSM, spectral, and texture indices, as these variables significantly improve the classification accuracies in the proper combinations of the input variables.

Potential use of crop water stress index (CWSI) and spectral vegetation indices for black cumin under deficit irrigation

This study was carried out in 2022 to examine the yield, yield components and changes in crop water stress index (CWSI) and vegetation index in black cumin with deficit irrigation. Five different irrigation water levels (I0, I25, I50, I75, I100) were used. The amount of irrigation water applied changed between 20 and 276 mm. Plant water consumption (ET) values varied between 182 and 425 mm. The highest seed yields were obtained from I100 treatments (692 t ha−1) and the lowest from I0 treatments (25 t ha−1). Biological yield, plant height, stem diameter, first capsule height, number of capsules per plant, number of branches per plant, number of seeds per capsule and 1000-seed weight of black cumin were affected by deficit irrigation. CWSI lower limit equation to be used in irrigation scheduling was identified Tc−Ta = − 1.7524 × VPD + 0.7698 (R2 = 0.54) and the upper limit 10.9 ℃. For black cumin plants, irrigation is recommended when the CWSI value is between 0.08 and 0.12. 9 different spectral vegetation indices were evaluated in this study. It has been determined that there are significant correlations between yield, yield components and CWSI and spectral vegetation indices.

The colliding-wind binary HD 168112

Radio surveys of early-type stars have revealed a number of non-thermal emitters. Most of these have been shown to be binaries, where the collision between the two stellar winds is responsible for the non-thermal emission. HD 168112 is a non-thermal radio emitter, whose binary nature has only recently been confirmed spectroscopically. We obtained independent spectroscopic observations to determine its orbit, in addition to radio observations to see if the thermal or non-thermal nature of the emission changes during the periastron passage. We monitored HD 168112 spectroscopically for a 13 year time span. From these data, we determined the orbital parameters, which we compared to the previous results in the literature. The stellar parameters of both components were determined by comparing the spectra to TLUSTY models. From the spectral index of the radio observations, we found how the nature of the emission changes as the system goes through periastron. Combining our results with other literature data allowed us to further constrain the orbital and stellar parameters. We find HD 168112 to have an orbital period of $P = 512.17^ $ d, an eccentricity of $e = 0.7533^ $, and a mass ratio close to one. From our spectroscopic modelling, we derived the stellar parameters, but we had difficulty arriving at a spectroscopic mass ratio of one. The radio observations around periastron show only thermal emission, suggesting that most of the synchrotron photons are absorbed in the two stellar winds at that phase. Combining our data with the optical interferometry detection, we could constrain the inclination angle to $i and the mass of each component to $ 26$ M$_ We have provided an independent spectroscopic confirmation of the binary nature of HD 168112. Although detected as a non-thermal radio emitter, near periastron the radio emission of this highly eccentric system is thermal and is mainly formed in the colliding-wind region. This effect will also occur in other colliding-wind binaries.

Fractionation of biomass-burning smoke-derived dissolved organic matters on the surface of clay minerals: Variations of molecular properties and components

Biomass burning (e.g., wildfire) frequently occurs globally, inevitably produces abundant biomass-burning smoke-derived dissolved organic matters (BBS-DOMs) which eventually deposits on the surface environment. The adsorption and fractionation of BBS-DOMs on clays inevitably alter their biogeochemical process and environmental behaviors in the surface environment. It is therefore important to clarify the adsorption and fractionation of BBS-DOM on clay surfaces. This study found that the fractionation of BBS-DOMs on clays (montmorillonite and kaolinite) were controlled by their functional groups, aromaticity, molecular size and organic components. The spectral indexes (SUVA254 and S275–295) of BBS-DOMs in solution after clays adsorption suggested that with the increasing DOC concentration, the primary interaction between BBS-DOMs and clays changed from hydrogen bond to hydrophobic/pore filling effects, and the adsorption ratio of the large molecules increased, which were very different from natural fulvic acid. Furthermore, various BBS-DOMs and fulvic acid had different component fractionation behaviors during clay adsorption, because they had different abundances of protein-like matters (hydrogen bond donors), pyridine-N/pyrimidine-N (positive charge doners of electrostatic interaction), and fulvic-like matters (hydrophobic interaction and pore filling effect). Additionally, the increasing pH weakened the adsorption of bulk BBS-DOMs and enhanced the adsorption ratio of aromatic matters and smaller BBS-DOM molecules. Meanwhile, at a higher pH, the adsorption ratio of protein-like matters increased, while the adsorption ratio of humic- and fulvic-like matters decreased. The result was ascribed to the enhanced hydrogen bond between protein-like matters and clays as well as the enhanced electrostatic repulsion between humic−/fulvic-like matters and clays. This study is helpful for deeply understanding the multimedia-crossing environmental behavior of BBS-DOMs in the surface environment.

The relationship between the turnover frequency and photo-ionization in radio sources

ABSTRACT We investigate the connection between the turnover frequency in the radio spectrum, νTO, and the rate of ionizing ultraviolet photons, $Q_{\rm {H{\small I}}}$, in extragalactic sources. From a large, optically selected sample, we find νTO to be correlated with $Q_{\rm {H\,{\small I}}}$ in sources that exhibit a turnover. The significance of the correlation decreases when we include power-law radio sources as limits, by assuming that the turnover frequency occurs below the lowest value observed. However, the power-law fit sources are less well sampled across the band and so these may just be contributing noise to the data. Given that the observed νTO–$Q_{\rm {H{\small I}}}$ correlation is purely empirical, we use the ionizing photon rate to obtain the electron density in a free–free absorption model. For each of the constant, exponential, constant plus exponential (Milky Way), and spherical models of the gas distribution, there is also an increase in the turnover frequency with ionizing photon rate. Furthermore, for a given gas mass, we find that the turnover frequency is anticorrelated with the scale-factor of the gas density. While other mechanisms, such as ageing electrons or synchrotron self-absorption, may be required to reproduce the spectral indices, for an exponential scale-factor similar to the linear size, this simple free–free absorption model reproduces the turnover-size correlation seen in radio sources.

Estimating wheat spike-leaf composite indicator (SLI) dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to wheat yield grain yield (GY) has been overlooked in crop models. Our objectives were to (i) construct a yield-related phenotypic trait, spike–leaf composite indicator (SLI), accounting for the contribution of the spike to photosynthesis, (ii) develop an enhanced triangle vegetation index 3 (ETVI3) based on the SLI response to canopy spectra, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and eXtreme Gradient Boosting with feature selection (XGB-FS) algorithm and four regression models, using field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years. The results showed that spike photosynthesis accounted for 28 % of GY. SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than leaf area index. ETVI3 displayed SLI differences through wheat canopy tissue abundance, chlorophyll content, and plant water content, was strongly correlated with SLI, whereas other spectral indices were more poorly correlated. Spectral features selected from XGB-FS algorithm enhanced the interpretability of the regression models and improved the estimation accuracy of SLI, wherein XGB-FS-LASSO was validated with higher accuracy for SLI estimation at the post-heading stage with coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71 %, 0.047 %, and 26.93 %, respectively. These results provide new insights into the role of fruiting organs for yield prediction. This high-throughput SLI estimation approach can be applied for accurate spectral prediction of GY at whole growth stages and may be assisted with agronomical practices and variety selection.