Spectral Measurements Research Articles

Performance Evaluation and Comparison of Deep Neural Network Models for African Soil Properties Prediction

ABSTRACT The significance of soil in agriculture and human survival cannot be overstated. Given its limited supply, improving soil properties is imperative. This study addresses this challenge by predicting five major soil properties—organic carbon, pH values, Mehlich-3 extractable calcium, Mehlich-3 extractable phosphorus, and sand content—utilizing mid-infrared absorbance measurements from the African Soil Information Service (AfSIS) dataset, covering non-desert regions of Africa. With a pressing need for a reliable and efficient model to predict African soil properties using spectral measurements, this study fills a crucial gap, addressing the scarcity of functional soil property databases in Africa. The developed model eliminates costly soil sample preparation and lengthy chemical analysis, applicable in both onsite and laboratory settings for determining soil functional properties. By employing stacked autoencoders for feature dimensionality reduction and combining discrete wavelet transform with two feature selection methods (stepwise regression and random forest) to build robust multi-layer perceptron (MLP) models, the study offers a comprehensive approach. Evaluation metrics including root mean square error (RMSE), scatter index (SI), Variance Accounted For (VAF), Nash-Sutcliffe Model Efficiency (NSE), and Correlation Coefficient (R) demonstrate the superior performance of the MLP model informed by stacked autoencoder-selected features, outperforming models informed by wavelet-transformed features and partial least square regression. This best-performing autoencoder-based model presents a valuable tool for soil scientists tasked with modeling African soil properties.

Mode division multiplexing reconstructive spectrometer with an all-fiber photonics lantern

This study presents a high-accuracy, all-fiber mode division multiplexing (MDM) reconstructive spectrometer (RS). The MDM was achieved by utilizing a custom-designed 3 × 1 mode-selective photonics lantern to launch distinct spatial modes into the multimode fiber (MMF). This facilitated the information transmission by increasing light scattering processes, thereby encoding the optical spectra more comprehensively into speckle patterns. Spectral resolution of 2 pm and the recovery of 2000 spectral channels were accomplished. Compared to methods employing single-mode excitation and two-mode excitation, the three-mode excitation method reduced the recovered error by 88% and 50% respectively. A resolution enhancement approach based on alternating mode modulation was proposed, reaching the MMF limit for the 3 dB bandwidth of the spectral correlation function. The proof-of-concept study can be further extended to encompass diverse programmable mode excitations. It is not only succinct and highly efficient but also well-suited for a variety of high-accuracy, high-resolution spectral measurement scenarios.Graphical

Development of a semi-supervised machine learning based noise filter for quantum cascade laser-coupled mid-infrared spectrometer

The rapid and accurate quantitative determination of molecular rovibrational spectral features is highly desirable for applications in fundamental high-resolution spectroscopy, trace chemical sensing, and environmental monitoring. But significant spectral noise can impede the accuracy and precision of such spectral measurements as well as the precise classification of fine and hyperfine spectral fingerprints. Nevertheless, the denoising of weak rovibrational spectral data has long been a computational and experimental challenge. Here, we develop a new semi-supervised machine learning (SSML) denoising method combining unsupervised spectral eigenvector space dimensionality reduction of the spectral data matrix exploiting principal component analysis with supervised Fourier domain residual analysis for enhancing high-resolution rovibrational spectral signal quality. We discussed the detailed implementation of the SSML algorithm on the real experimental mid-infrared rovibrational spectral features of nitrogen dioxide (NO2) in the gas-phase acquired from a quantum cascade laser-coupled cavity ring-down spectroscopic method. Our results confirm the robustness and feasibility of the SSML approach and could lead to a wide range of pertinent applications in infrared spectroscopy including precise spectral signal analysis, concentration retrieval and performance enhancement of laser-coupled spectroscopic sensors.

Measurements of laser-plasma instabilities in double-cone ignition experiments relevant to the direct-drive conditions at Shenguang-II Upgrade laser facility

Experiments have been performed to investigate laser-plasma instabilities (LPIs) relevant to the direct-drive inertial confinement fusion (ICF) conditions at the Shenguang-II Upgrade (SG-II UP) laser facility during the double-cone ignition (DCI) experimental campaigns, with the overlapped laser intensity ranging from 6×1014Wcm−2 to 1.8×1015Wcm−2 . An overall LPI scenario has been built from the collection and investigation of angularly distributed scattered light. Across broad ranges of laser and plasma parameters, relevant to the direct-drive ICF conditions, the dominance of the stimulated Raman side scattering (SRSS) was confirmed, and its coexistence with the two-plasmon decay (TPD) was also observed. Significant SRSS scattered light was observed across an extremely wide range of emission angles, concentrated at large angles, and demonstrated to be robust throughout the parameter space. Time-resolved spectral measurements show distinctly different SRSS behaviors along different angles and a slightly higher threshold compared to the TPD. The overall SRSS energy loss was measured, indicating a scattered light energy fraction of up to 6%. These results are of crucial importance for the precise assessment of the LPIs in the DCI as well as other laser directly driven ICF schemes.

First principles insight into the study of the structural, stability, and optoelectronic properties of alkali-based single halide perovskite ZSnCl3 (Z = Na/K) materials for photovoltaic applications.

Metal halide perovskites are crystalline materials with a sharp increase in popularity and rapidly becoming a major contender for optoelectronic device applications. In this work, we provide the optoelectronic features of a possible novel candidate, ZSnCl3 (Z = Na/K) Sn-based on a detailed numerical simulation. The output of the current computations is compared to the results that are currently available, and a respectable agreement is noted. The studied compounds were cubic in nature and structurally stabe. The mechanical properties reflectthe mechanical stability and ductility of the proposed materials. The Sn-based single perovskite compounds proposed in this study are mechanically stable and ductile. The narrow direct band gap for NaSnCl3 and KSnCl3 are 1.36 eV and 1.47 eV, respectively, using the HSE06 hybrid function with the Boltztrp2 integrated in Quantum ESPRESSO (QE) software. The effective use of these compounds in perovskite solar cells and other optoelectronic applications was confirmed by optical absorption spectral measurements conducted in the photon energy range of 0-20 eV.

Thermal radiation at high-temperature and high-pressure conditions: Validation of HITEMP-2010 for carbon dioxide

Line-by-line models based on spectroscopic databases are powerful tools for the generation of accurate gas absorption spectra but still require validation for high-temperature and high-pressure conditions. Therefore, this study carried out spectral transmissivity measurements of CO2/N2 mixtures at temperatures of approximately 773K and 1273K and pressures between 1bar and 60bar. The measurement data was obtained in a spectral range from 1900cm−1 to 6600cm−1 at a spectral resolution of 1cm−1 and was compared with line-by-line predictions. The comparisons show that the absorption spectra of CO2 can be predicted for high-temperature and high-pressure conditions with good accuracy if the line-by-line calculations are performed using modified line profiles and the HITEMP-2010 database. In comparison with standard line-shape functions, both the Voigt line-shape function combined with the cut-off criterion of Alberti et al. (Combust. Flame 162 (2015) 597–612) and the Price line-shape function modified with one of the two ξ corrections of Westlye et al. (J. Quant. Spectrosc. Radiat. Transfer 302 (2023) 108555; J. Quant. Spectrosc. Radiat. Transfer 280 (2022) 108089) provided significantly superior predictions. However, deficiencies of the cut-off criterion of Alberti et al. become clear in the transmissivity predictions of the right wings of the 4.3µm band and the 2.7µm band. The Price line-shape function combined with the first ξ correction of Westlye et al. was able to overcome these erroneous predictions of the wings, while the Price line-shape function modified with the second ξ correction of Westlye et al. only proved superior for gas mixtures with 20% CO2 and 80% N2 (in mole fractions).

Synthesis and structural characterization of supramolecular cocrystals of [(4-cyano-1-methylpyridinium)2-(18-crown-6)] diiodide

Abstract This study focuses on synthesizing and characterizing supramolecular cocrystals derived from 4-cyano-1-methylpyridinium iodide and 18-crown-6. The synthesis employed a solvent evaporation technique, followed by a thorough structural analysis utilizing XRD methods. The compound’s purity is confirmed by powder XRD analysis, while single-crystal XRD confirms its triclinic system packing with a centrosymmetric space group ( P 1 ‾ $P\overline{1}$ ). The findings unveiled the emergence of unique cocrystals characterized by distinct molecular configurations supported by hydrogen bonding and ion-dipole interactions. Through diffuse reflectance spectral measurements, the direct band gap energy was assessed at 2.36 eV. Thermogravimetric analysis was utilized to evaluate the thermal properties of the compound across varying temperatures. The compound’s elemental composition and surface morphology were analyzed through investigations using EDS and SEM.

Research on Spectral Measurement Method for Content of Bicolor Mixed Ink

Research on Spectral Measurement Method for Content of Bicolor Mixed Ink

Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements

Abstract. The increase in greenhouse gas concentrations, particularly CO2, has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges, particularly in terms of computational efficiency in atmospheric CO2 retrieval and analysis. To address these challenges, this study focuses on improving the speed of retrieving the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using spectral data from the Orbiting Carbon Observatory-2 (OCO-2) satellite while still maintaining retrieval accuracy. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrievals. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of the operational optimization model, which is released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion performance and prediction performance of the machine learning model for XCO2 are compared, analyzed, and discussed for the observed region over east Asia. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model not only achieves rapid predictions (<1 ms) with good accuracy (1.8 ppm or approximately 0.45 %) but also effectively captures sudden increases in XCO2 plumes near industrial emission sources. The accuracy of the machine learning model retrieval results is validated against reliable data from Total Carbon Column Observing Network (TCCON) sites, demonstrating its ability to effectively capture CO2 seasonal variations and annual growth trends.

Supercontinuum laser-based gonio-scatterometer for in and out-of-plane spectral BRDF measurements.

The hyperspectral component of bidirectional reflectance measurements, namely from several hundred wavelengths upwards, is attracting growing interest for numerous applications in both optics and computer graphics. In this paper, we present a motorized hyperspectral bidirectional reflectance measurement bench that performs in-plane and out-of-plane measurements for isotropic materials using a supercontinuum laser covering the visible and near infrared range, with a sub-nanometer spectral accuracy. We describe the complete data processing chain, including a method for assessing the alignment error of the measurement bench. From these measurements, we verify the principles of non-negativity, energy conservation and Helmholtz reciprocity. We introduce criteria also to evaluate the validity of the Lambertian hypothesis for the bidirectional reflectance and its deviation from reciprocity, obtained from the measurements directly. We show the need for spectral bidirectional reflectance measurements for certain materials, rejecting the separable function approximation.

Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors.

Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled the detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was commonly used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. In this paper, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo measurements of patients with various brain tumors to show that a Poisson distribution indeed models the measured data 82% better than a Gaussian in terms of the Kullback-Leibler divergence, and that the endmember abundance vectors are sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra, including PpIX (620 nm and 634 nm photostates), NADH, FAD, flavins, lipofuscin, melanin, elastin, and collagen, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances. The new unmixing method was then tested on the human and simulated data and compared to four other candidate methods. It outperforms previous methods with 25% lower error in the computed abundances on the simulated data than NNLS, lower reconstruction error on human data, better sparsity, and 31 times faster runtime than state-of-the-art Poisson regression. This method and library of endmember spectra can enable more accurate spectral unmixing to aid the surgeon better during brain tumor resection.

Broadband color routing with a single element nanoantenna for communication bands

Spectral routing techniques have attracted plenty of research attention for the past decades, as they enable light manipulation in both the frequency domain and the spatial domain, which is crucial for applications in on-chip spectroscopy, optical switching, and modern communications. Here, we demonstrate an ultra-compact asymmetric nanoplasmonic router for communication bands that routes O and C bands to opposite positions. The nanorouter consists of two uneven grooves that create bidirectional scattered optical fields, utilizing the interference between different optical modes inside the grooves. A broadband spectrum exceeding 100 nm and a maximum extinction ratio of 31 dB are achieved, providing new opportunities for nanophotonic color routing solutions and extensions to other areas such as imaging sensors and spectral measurements.

Microwave melt-quenching technique to synthesize of CuO-doped B2O3–ZnO–Na2O–Sm2O3 scintillating glasses

The new series of CuO-doped samarium sodium zinc borate (SNZB) glasses, with the composition (59.5-x)B2O3:30ZnO:10Na2O:0.5Sm2O3 (x = 0.00–0.10 mol%), were prepared using the microwave melt-quenching technique. These glasses were characterized through XRD, absorption, excitation, photoluminescence (PL), and X-ray luminescence (RL) spectral measurements. The optical absorption analysis revealed a significant increase in the visible Cu2+ absorption band at approximately 770 nm with the addition of CuO at concentrations ranging from 0.00 to 0.10 mol%. From the absorption spectra of SNZBCu-0.00, the Judd-Ofelt (J-O) intensity parameters (Ω2, Ω4, Ω6) were determined. Under 402 nm excitation, the PL spectra exhibited a bright reddish-orange emission at 600 nm. The decay spectral profiles of the 4G5/2 → 6H7/2 (600 nm) luminescence transition were used to measure experimental lifetimes (τg).

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

Abstract We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. The resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves the capture of SIF seasonal and interannual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather, the original noisy retrievals are needed as the target for the NN training process. Significance Statement The purpose of this study is to document and demonstrate a machine learning algorithm that is used to effectively reduce noise and artifacts in a satellite data product, solar-induced fluorescence (SIF) from chlorophyll. This is important because SIF retrievals are typically noisy, and the noise limits their ability to be used for diagnosing plant health and productivity. Our results show substantial improvement in SIF retrievals that may lead to new applications. Our approach can be similarly applied to other noisy satellite data products.

Influence of stiffeners on acoustic emission monitoring of ship structures

Naval vessels are valuable assets that are expensive to build and maintain. Predictive maintenance is crucial to enhance efficiency and operability and to extend the service life of these structures. Fatigue is regarded as one of the main damage modes affecting the structural integrity of ship structures. Fatigue cracks can develop at the intersections of stiffeners and other structural elements, without being detected until they substantially grow, introducing a degree of uncertainty in the estimation of the fatigue damage. Acoustic emission (AE) is a passive ultrasound method for the detection and localization of different types of damage. AE is sensitive to crack propagation and can cover large areas, making it suitable for structural health monitoring of ship hulls. However, for accurate fatigue crack detection via AE it is crucial to understand how ultrasound waves interact with structural members i.e. stiffeners and longitudinal frames. Dispersion, scattering, reflection, and multimodality have so far hindered the application of AE in this context. This study aims to assess ultrasound wave behavior in ship structures in the presence of structural elements such as stiffeners and longitudinal/transversal frames. It investigates the ultrasound wave transmission dependence on source frequency and angle of incidence using spectral finite element (SEM) simulations and experimental measurements. The experimental setup includes a 10mm thick steel plate with a stiffener and a longitudinal beam (Figure 1). Pairs of sensors are placed on each side of the stiffener to record the incoming and transmitted waves, and to determine the transmission coefficient. By combining the results of both simulation and experiments, an expression for the transmission coefficient as a function of different frequencies and angles is presented. The results of this study can be used to maximize the coverage of AE monitoring systems and enhance their sensitivity for detection of fatigue cracks on ship structures.

Absolute energy-dependent scintillating screen calibration for real-time detection of laser-accelerated proton bunches.

Laser-plasma accelerators (LPAs) can deliver pico- to nanosecond long proton bunches with ≳100 nC of charge dispersed over a broad energy spectrum. Increasing the repetition rates of today's LPAs is a necessity for their practical application. This, however, creates a need for real-time proton bunch diagnostics. Scintillating screens are one detector solution commonly applied in the field of electron LPAs for spatially resolved particle and radiation detection. Yet their establishment for LPA proton detection is only slowly taking off, also due to the lack of available calibrations. In this paper, we present an absolute proton number calibration for the scintillating screen type DRZ High (Mitsubishi Chemical Corporation, Düsseldorf, Germany), one of the most sensitive screens according to calibrations for relativistic electrons and x rays. The presented absolute light yield calibration shows an uncertainty of the proton number of 10% and can seamlessly be applied at other LPA facilities. For proton irradiation of the DRZ High screen, we find an increase in light yield of >60% compared to reference calibration data for relativistic electrons. Moreover, we investigate the scintillating screen light yield dependence on proton energy since many types of scintillators (e.g., plastic, liquid, and inorganic) show a reduced light yield for increased local energy deposition densities, an effect termed ionization quenching. The ionization quenching can reduce the light yield for low-energy protons by up to ∼20%. This work provides all necessary data for absolute spectral measurements of LPA protons with DRZ High scintillating screens, e.g., when used in the commonly applied Thomson parabola spectrometers.

Foam-Mat Freeze Drying of Kiwiberry (Actinidia arguta) Pulp: Drying Kinetics, Main Properties and Microstructure

The kiwiberry is an interesting source of bioactive compounds (micronutrients, polyphenols vitamins and pectins) and enzyme actinidine but has limited durability. The aim of this study was to determine the impact of shelf temperature (10 °C, 25 °C and 40 °C) during freeze drying on the foam-mat kiwiberry pulp drying process and the quality of the obtained material based on analyses such as moisture content, water activity, hygroscopicity, solubility, microstructure and spectral measurement using the FTIR method. The use of higher shelf temperatures during freeze drying positively influenced the drying process, reducing its duration by up to 40.7%. The shelf temperature caused changes in the dry matter content (97.2–99.6%), water activity (0.159–0.221), structure and hygroscopic properties (1.41–4.41 g water/100 g d.m.) of the kiwiberry foam mats. Foam-mat drying at 40 °C exhibited a significantly lower water activity, total porosity and hygroscopicity, providing properties favorable for good microbiological and functional stability during storage. Furthermore, this temperature applied during freeze drying resulted in an increase in the solubility of the obtained material, which indicates its possible use in the matrix of other food products.

An integrated remote sensing, petrology, and field geology analyses for Neoproterozoic basement rocks in some parts of the southern Egyptian-Nubian Shield

The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

Deep learning reconstruction algorithm for frequency-resolved optical gating.

In general, delay operation is the most time-consuming stage in frequency-resolved optical gating (FROG) technology, which limits the use of FROG for high-speed measurement of ultrashort laser pulses. In this work, we propose and demonstrate the reconstruction of ultrashort optical pulses by employing the sequence-to-sequence (Seq2Seq) model with attention, theoretically. To our knowledge, this is the first deep learning framework capable of accurately reconstructing ultrashort pulses using very partial spectrograms. The root mean squared error (RMSE) of the pulse amplitude reconstruction and phase reconstruction on the overall test dataset are 9.5 × 10-4 and 0.20, respectively. Compared with the classic FROG recovery algorithm based on two-dimensional phase retrieval algorithms, the use of our model can shorten the spectral measurement time to 1/8 of the original time or even less. Meanwhile, the time required for pulse reconstruction using our model is roughly 0.2 s. To our knowledge, the pulse reconstruction speed of our model exceeds all current iteration-based FROG recovery algorithms. We believe that this study can greatly facilitate the use of FROG for high-speed measurements of ultrashort pulses.

A novel physics-based cloud retrieval algorithm based on neural networks (CRANN) from hyperspectral measurements in the O2-O2 band

Clouds play a crucial role in the Earth's climate system and their properties can be detected by hyperspectral measurements from space. With the increasing spectral resolution, traditional retrieval methods based on look-up tables (LUT) and optimal estimation are limited in both efficiency and accuracy compared with machine learning methods. However, the machine learning techniques used to establish the relationships between spectral measurements and cloud properties often lack physical explainability and universality. Additionally, traditional physical retrieval methods based on oxygen A-band are not applicable to instruments without the O2-A band like the ozone monitoring instrument (OMI). Therefore, we have proposed a novel physics-based deep neural networks (DNN) retrieval method––the cloud retrieval algorithm based on neural networks (CRANN)––which incorporates a deep neural network model with radiative transfer model to retrieve cloud fraction and cloud-top pressure from the oxygen–oxygen collision-induced (O4) absorption band. Validation using simulated test data supported the superior accuracy of CRANN, with the correlation coefficients for cloud fraction and cloud-top pressure are 0.989 and 0.993, respectively, whereas the correlation coefficients for cloud fraction and cloud-top pressure of the LUT method are 0.928 and 0.865, respectively. In comparison with the OMCLDO2 cloud product from the OMI, the CRANN results retrieved from OMI observations exhibit substantial consistency, boasting correlation coefficients surpassing 0.95 for cloud fraction and 0.83 for cloud pressure. As compared with the tropospheric monitoring instrument (TROPOMI) official products, the CRANN retrieval results from TROPOMI exhibit a high level of consistency with correlation coefficients exceeding 0.8 for cloud fraction and 0.73 for cloud pressure. Additionally, a promising agreement is observed between the CRANN retrievals from TROPOMI and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, yielding RMSEs of 127.3, 134.6 and 106.4 hPa for the validation dataset, respectively.