Spectral Index Research Articles

A New Spectral Index for Monitoring Leaf Area Index of Winter Oilseed Rape (Brassica napus L.) under Different Coverage Methods and Nitrogen Treatments

A New Spectral Index for Monitoring Leaf Area Index of Winter Oilseed Rape (Brassica napus L.) under Different Coverage Methods and Nitrogen Treatments

Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R2), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R2 and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R2 values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R2 values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

Detection of Increasing Gamma-ray Emissions from 4FGL J1718.5+4237 with Fermi-LAT

Abstract We report a gradual brightening γ-ray source, 4FGL J1718.5+4237, in 0.1– 500.0 GeV, which may be associated with a blazar NVSS J171822+423948 with a redshift of ~2.7. We analyzed 15.25 yr of γ-ray data recorded by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope and detected significant γ-ray emissions in the di- rection of the blazar with a test statistic (TS) of ~135. Based on timing analysis using a 1-year time bin, we have observed a gradual brightening γ-ray emissions from the target. In our analysis, we categorize them into two states: Quiet (TS~0) and Loud (TS~226) states, with the distinction occurring in August 2016 (MJD 57602.69). From the Quiet state to the brightest period (the last data point), the γ-ray flux in the 0.1–500.0 GeV increased by more than 12-fold from <0.2×10−8 photons cm−1 s−1 to 2.6×10−8 photons cm−1 s−1. Additionally, we studied the spectral properties in detail for the Loud state and the overall data. While no significant variation was detected, both exhibited a spectral index Γ of ~2.6. The origin of the brightening γ-ray emissions from the target is not yet clear. Future long-term multi- wavelength observations and studies will provide insight into the astrophysical mechanisms of the target.

Predicting canopy chlorophyll concentration in citronella crop using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery

Unmanned Aerial Vehicle (UAV) remote sensing in precision agriculture (PA) is a promising technique for managing crop inputs. The high spatial-resolution data enables extensive examination of the crop canopy, including identifying low-chlorophyll patches for initiating prophylactic measures as early as possible. In this study DJI M-200 equipped with MicaSense Altum multispectral camera was used to acquire multispectral images of citronella fields under various nutrient treatments at different growth stages. The study assessed the relevance of Machine Learning (ML) on multispectral images to predict citronella chlorophyll concentrations. To predict the crop canopy chlorophyll concentration for citronella, we evaluated Random Forest (RF), Support Vector Machine (SVM) and Partial Least Square (PLS) machine learning models. Based on the Rsquare value of all three models, the SVM machine learning model could best predict the crop canopy chlorophyll concentration with coefficient of determination R2 (0.79), root mean square error (0.05) and mean absolute error (0.04) respectively. The correlation between predicted chlorophyll contents and selected Vegetation Indices (VIs) also demonstrated significant relationship, implying that these can serve as a valuable indicator of crop canopy chlorophyll, which in turn can be used for providing insights into crop health, vigor, and productivity. This study showed that by employing UAV multispectral images and machine learning models the spatial distributions of chlorophyll concentration in a citronella crop at different growth phases can be predicted accurately.

Constant-roll inflation with tachyon field in the holographic braneworld

Abstract We study inflation driven by the tachyon field in the holographic braneworld by assuming the second slow-roll parameter η is constant. The parameter η can be either defined by the tachyon scalar field and the Hubble parameter or by the Hubble parameter only. By assuming a constant η, we derive and numerically solve a differential equation for the Hubble expansion rate. We calculate numerically the scalar spectral index and the tensor-to-scalar ratio. We confront the results with the observational data and find some constraints on the free model parameters. The swampland conjectures are discussed in the context of the constant-roll inflation, with some accent on the holographic model.

A superstatistical measure of distance from canonical equilibrium

Non-equilibrium systems in steady states are commonly described by generalized statistical mechanical frameworks such as superstatistics, which assumes that the inverse temperature β=1/(kBT) is an unknown quantity having some pre-established statistical distribution. The uncertainty in β is usually understood as the fluctuation of a physical observable, however, it has been previously proved (Davis and Gutiérrez 2018 Physica A 505 864–70) that β in a superstatistical model cannot be associated to an observable function B(Γ) of the microstates Γ. In this work, we provide an information-theoretical interpretation of this theorem by introducing a new quantity D , the mutual information between β and Γ. Our results show that D is also a measure of departure from canonical equilibrium, and reveal a minimum, non-zero uncertainty about β given Γ for every non-canonical superstatistical ensemble. The behavior of D is illustrated in the case of a collisionless plasma described by kappa distributions, revealing the precise sense in which the spectral index κ can be understood as a measure of distance from equilibrium.

A critical systematic review on spectral-based soil nutrient prediction using machine learning.

The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability.

Integrating Sentinel 2 Imagery with High-Resolution Elevation Data for Automated Inundation Monitoring in Vegetated Floodplain Wetlands

Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.

Pre-Harvest UV-A Supplementation in Plant Factory with Artificial Lighting Improves Growth, Photosynthesis, and Phytonutrients in Kale

UV-A (315–400 nm) is a component of solar radiation, which impacts plant physiology. Consequently, the impact was investigated of UV-A supplementation on various aspects of kale cultivation in a plant factory with artificial lighting. Three UV-A intensity treatments (5, 10, or 15 W/m2) were applied to kale plants for 5 days before harvesting. The results revealed that 5 or 10 W/m2 UV-A supplementation provided significant advantages for kale growth. The fresh weight of the kale shoots increased by over 35–50% after UV-A exposure. In particular, the 10 W/m2 UV-A exposure significantly improved the photosynthetic rate, water-use efficiency, and quantum efficiency of photosystem II (Y(II)). In addition, the treatment with UV-A positively impacted changes in leaf spectral indices, resulting in decreases in the simple ratio index (SR), the normalized phaeophytinization index (NPQI), and the normalized difference red edge index (NDRE). Furthermore, the phytonutrients in kale (Vitamin C, soluble protein, and total phenolics) were enhanced using the 10 W/m2 UV-A treatment. One notable outcome was the consistent reduction in nitrate contents across all UV-A treatment intensities. Therefore, supplementing kale cultivation with UV-A radiation was a valuable strategy for enhancing kale yield and quality in plant factory production.

Solar Eruptive Phenomena Associated with Solar Energetic Electron Spectral Types

The energy spectral shape of solar energetic electron events carries important information on the energetic electron source/acceleration at the Sun. We investigate the association of six newly identified solar energetic electron spectral types with solar eruptive phenomena, including the downward double-power-law (DDPL) spectrum with break energy E B above 20 keV (DDPL EB≥20keV ), DDPL with E B below 20 keV (DDPL EB<20keV ), upward double-power law (UDPL), single-power-law (SPL), Ellision–Ramaty–like (ER), and logarithmic-parabola (LP). We find that the SPL type shows (the other five types show) an association with hard X-ray flares of ∼38% (∼55%–82%) and an association with west-limb coronal mass ejections (CMEs) of ∼76% (∼85%–93%). Among the other five types, the DDPL EB<20keV and ER (LP and DDPL EB≥20keV ) types only have an association with type II radio bursts of ∼7%–8% (∼16%–20%) and an association with halo CMEs of ∼5%–9% (∼11%–21%); however, the UDPL type exhibits a significant (∼47% and ∼50%) association with type II bursts and halo CMEs, with a significantly faster median CME speed of 1000−120+550 km s−1. For DDPL EB≥20keV (DDPL EB<20keV ) with a positive (no) correlation between spectral indexes and no (a positive) correlation between the spectral index and break energy, the spectrum appears to be flatter as the associated CME (flare) becomes faster (stronger). These results suggest that the SPL type can result from the initial acceleration process that likely occurs high in the corona, and then provide seed populations for further acceleration processes to form the other five types: the DDPL EB<20keV and ER types via flare-related processes, the LP and DDPL EB≥20keV types via CME-related processes, and the UDPL type via CME-driven shocks.

Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi- and BATSE-detected Gamma-Ray Bursts

Gamma-ray bursts (GRBs), often attributed to massive star collapse or binary compact object mergers, exhibit diverse emission characteristics hinting at multiple GRB classes based on various factors like progenitors, radiation mechanisms, and central engines. This study employs unsupervised clustering using the nested Gaussian mixture model algorithm to analyze data from Fermi and BATSE, identifying four classes (A–D) based on duration, spectral peak, and spectral index of time-integrated spectra of GRBs. Class proportions are approximately 70%, 10%, 3%, and 17%, respectively, with A and B comprising mostly long GRBs, C mainly short GRBs, and D encompassing both types. The classes are further assessed based on spectral index α, indicating distinct radiation mechanisms: α > −0.67 for photospheric emission, α ≤ −1.5 for fast-cooling synchrotron, and −1.5 < α ≤ −0.67 for slow-cooling synchrotron. Classes B and C align with photospheric emission, while A and D predominantly exhibit synchrotron radiation. Short GRBs are predominantly photospheric emission, whereas long GRBs tend to favor synchrotron emission. Overall, 63% of the total bursts exhibit α profiles indicative of synchrotron emission, with the remaining 37% associated with photospheric emission. Considering the limited data of kilonova and supernova associated with GRBs, classes are examined for progenitor origins, suggesting a hybrid nature for A and D, and collapsar and merger origins for B and C, respectively. This clustering analysis results in four GRB classes, which, upon investigation, reveal the diverse and complex nature of GRBs in terms of their radiation, duration, and progenitor.

The quiescent population at 0.5 ≤ z ≤ 0.9: Environmental impact on the mass–size relation

Context. How the quiescent galaxies evolve with redshift and the factors that impact their evolution are still debated. It is still unclear what the dominant mechanisms of passive galaxy growth are and what role is played by the environment in shaping their evolutionary paths over cosmic time. Aims. The population of quiescent galaxies is altered over time by several processes that can affect their mean properties. Our aim is to study the mass–size relation (MSR) of the quiescent population and to understand how the environment shapes the MSR at intermediate redshift. Methods. We used the VIMOS Public Extragalactic Redshift Survey (VIPERS), a large spectroscopic survey of ∼90 000 galaxies in the redshift range 0.5 ≤ z ≤ 1.2. We selected a mass-complete sample of 4786 passive galaxies based on the NUVrK diagram and refined it using the Dn4000 spectral index to study the MSR of the passive population over 0.5 ≤ z ≤ 0.9. The impact of the environment on the MSR and on the growth of the quiescent population is studied through the density contrast. Results. The slope and the intercept of the MSR, α = 0.62 ± 0.04 and log(A) = 0.52 ± 0.01, agree well with values from the literature at the same redshift. The intercept decreases with redshift, Re(z) = 8.20 × (1 + z)−1.70, while the slope remains roughly constant, and the same trend is observed in the low-density (LD) and high-density (HD) environments. Thanks to the largest spectroscopic sample at 0.5 ≤ z ≤ 0.9, these results are not prone to redshift uncertainties from photometric measurements. We find that the average size of the quiescent population in the LD and HD environments are identical within 3σ and this result is robust against a change in the definition of the LD and HD environments or a change in the selection of quiescent galaxies. In the LD and HD environments, ∼30 and ∼40% of the population have experienced a minor merger process between 0.5 ≤ z ≤ 0.9. However, minor mergers account only for 30–40% of the size evolution in this redshift range, the remaining evolution likely being due to the progenitor bias.

Quantum Gravity Corrections to the Inflationary Spectrum in a Bohmian Approach

A precise interpretation of the universe wave function is forbidden in the spirit of the Copenhagen School since a precise notion of measure operation cannot be satisfactorily defined. Here, we propose a Bohmian interpretation of the isotropic universe quantum dynamics, in which the Hamilton–Jacobi equation is restated by including quantum corrections, which lead to a classical trajectory containing effects of order ℏ2. This solution is then used to determine the spectrum of gauge-invariant quantum fluctuations living on the obtained background model. The analysis is performed adopting the wave function approach to describe the fluctuation dynamics, which gives a time-dependent harmonic oscillator for each Fourier mode and whose frequency is affected by the ℏ2 corrections. The properties of the emerging spectrum are discussed, outlining the modification induced with respect to the scale-invariant result, and the hierarchy of the spectral index running is discussed.

Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy

Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management.

Recovering 21cm Monopole Signals Without Smoothness

We expect the monopole signal at the lowest frequencies below 100MHz to be composed to two components: the deep Rayleigh-Jeans tail of the cosmic microwave background and two distinct features: the dark ages trough at ∼17MHz and the cosmic dawn trough at ∼75Mhz. These are hidden under orders of magnitude brighter foregrounds whose emission is approximately a power-law with a spectral index ≈−2.5. It is usually assumed that monopole signals of interest are separable from foregrounds on the basis of spectral smoothness. We argue that this is a difficult approach and likely impossible for the Dark Ages trough. Instead, we suggest that the fluctuations in the foreground emission around the sky should be used to build a model distribution of possible shapes of foregrounds, which can be used to constrain presence of a monopole signal. We implement this idea using normalizing flows and show that this technique allows for efficient unsupervised detection of the amplitude, width and center of the Dark Ages trough as well as Rayleigh-Jeans tail of the cosmic microwave background for a sufficiently sensitive experiment. We show that achromatic and smooth response significantly helps with foreground separation. We discuss the limitations of the inherent assumptions in this method and the impact on the design of future low-frequency experiments.

Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize

Aboveground biomass (AGB) directly reflects crop carbon fixation capacity. Utilizing unmanned aerial vehicle (UAV) remote sensing for high-throughput AGB monitoring offers benefits to agricultural management and food production. This study aimed to develop a method for monitoring AGB in film-mulched crops using UAV multispectral data. A three-year field experiment was conducted using wheat (Exps. 1 and 2 in 2021–2022, and Exps. 5 and 6 in 2022–2023) and maize (Exps. 3 and 4) with varying cropping patterns. Spectral indices (SIs) of different feature reflectances (e.g., vegetation, soil, and mulch pixels) were reconstructed using least absolute shrinkage and selection operator methods. The Bayesian model averaging (BMA) method integrated seven independent machine learning algorithms to construct a Bayesian ensemble model (BEM). The results demonstrated that spectral index reconstruction (SIR) enhanced the correlation between SIs and AGB, while BMA reduced model uncertainty and improved AGB prediction accuracy. The BEM model, based on Exps. 1 and 2 datasets, achieved determination coefficients of 0.78–0.84 and root mean square error (RMSE) of 0.81–0.89 t ha−1. Model validation across years and crops demonstrated the generalization of the SIR + BEM method for AGB estimation. The RMSEs of the model inter-annual transport predictions were 0.83 and 0.93 t ha−1 in Exps. 5 and 6, respectively, and RMSE of the predictions between crops was 1.43 t ha−1 for the maize dataset (Exps. 3 + 4). The approach proposed in this study has great potential for monitoring growth status of film-mulched crops at the farm scale and providing guidance for precision agriculture management.

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Application Model of Hyperspectral Technology Based on Novel Spectral Indices for Salinity Assessment in Soil Heritage Sites

Abstract. The Dunhuang murals are a precious treasure of China's cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive , circumventing issues of high costs, low efficiency. Building on this, our study developed a high-spectral feature inversion model for mural phosphate content using Fractional Order Differentiation (FOD), a novel three-band spectral index, and Partial Least Squares Regression (PLSR) algorithm. The specific research contents include: 1) Exploring the absorption mechanism of phosphates and their characteristic bands, combined with the optimal spectral index to construct a univariate linear regression model, providing a basis for rapid quantitative measurement of mural phosphate content. 2) By comparing the accuracy of the PSR and PNDI spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study's findings include: 1) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (R2) of 0.728. 2) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural phosphate content with a determination coefficient (R2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

Multi-resolution remote sensing for flark area detection in boreal aapa mires

ABSTRACT Peatlands have suffered significant degradation globally due to human impacts, which has increased the need to monitor the condition and changes in peatland ecosystems. With remote sensing, point-based in-situ observations can be upscaled to larger areas but there is a need to develop scalable monitoring methods. We predicted wet flark area extent, a key ecological indicator for patterned flark fens, in five sites in central Finland. Our primary aim was to test how the spatial and spectral resolution of different optical satellite image products (Landsat 8–9, Sentinel-2, PlanetScope) affect flark area coverage prediction. We also tested if there were seasonal or site-specific differences in prediction accuracy. Lastly, we upscaled the flark area coverage to entire mire areas. We used unmanned aerial vehicle (UAV)-derived flark area classification as a ground reference data to compare satellite sensors’ prediction accuracies. We predicted flark area coverage using spectral bands and indices as predictor variables using random forest regression. All sensors provided accurate results with some differences between Landsat (pseudo-R2 32−84%, root-mean squared error (RMSE) 10 − 18%), Sentinel-2 (R2 61−92%, RMSE 6−14%), and PlanetScope (R2 46 − 92%, RMSE 6 − 17%). The shortwave infrared bands of Landsat and Sentinel-2 did not increase the prediction accuracy. There were notable site-specific variations in prediction accuracy despite all the sites having typical open aapa mire wet flark – dry string patterns. With single-site models, the prediction accuracies were similar for early and late summer data, but when transferring the models to the other sites, performance significantly decreased, especially with the models using the late-summer imagery. Finally, we successfully upscaled the flark area coverage across entire mire areas. Our results demonstrate that the combination of UAV and satellite imagery can be used successfully to monitor peatland conditions and changes in them.

Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices

Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS–NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700–1100 nm). The correlations between LWC and the two-band index (RVI1156, 1628 nm), three-band indices (3BI-3(766, 478, 1042 nm), 3BI-4(1129, 1175, 471 nm), 3BI-5(814, 929, 525 nm), 3BI-6(1156, 1214, 802 nm), 3BI-7(929, 851, 446 nm)) based on FOD were higher than that of moisture indices and single-band spectrum, with r of − 0.71**, 0.74**, 0.73**, − 0.72**, 0.75** and − 0.76** for the correlation. The prediction accuracy of the two-band spectral indices (DVI(698, 1274 nm) DVI(698, 1274 nm) DVI(698, 1274 nm)) was higher than that of the moisture spectral index, with R2 of 0.81 and R2 of 0.79 for the calibration and validation, respectively. Due to a large amount of spectral indices, the correlation coefficient method was used to select the characteristic spectral index from full three-band indices. Among twenty seven models, the FWBI-3BI− 0.8 order model performed the best predictive ability (with an R2 of 0.86, RMSE of 2.11%, and RPD of 2.65). These findings confirm that combining spectral index optimization with machine learning is a highly effective method for inverting the leaf water content in spring wheat.