Reflection Data Research Articles

Remote Sensing Evaluation of Trophic Status in the Daihai Lake Based on Fuzzy Classification

Trophic state index (TSI) is a critical ecological and environmental issue in water resource management that has garnered significant attention. Given the complexity of optical characteristics in aquatic environments, this study employs fuzzy classification methods (FCM) and composite nutrient status indices to meticulously classify in-situ remote sensing reflectance data, aiming to develop evaluation models for different nutrient status categories to facilitate the assessment of the Daihai River in Inner Mongolia, China. Subsequently, we applied this model to MSI data to analyze the nutrient status of Daihai Lake from 2016 to 2021. Furthermore, a structural equation model (SEM) was utilized to explore the primary driving factors influencing nutrient status. The results indicated that the water bodies in Daihai Lake can be broadly classified into three categories, with the nutrient status models demonstrating robust performance for each category (R2 = 0.80, R2 = 0.83, and R2 = 0.74). Comparisons were made between nutrient status accuracies obtained through the NCM and FCM based on measured data, yielding R2 values of 0.74 and 0.85, respectively. Furthermore, the TSI results derived from MSI inversion were validated, with NCM achieving an R2 of 0.49, RMSE of 6.88, and MAPE of 10.36%, while FCM exhibited an R2 of 0.55, RMSE of 8.89, and MAPE of 13.18%. An SEM–based analysis revealed that over the long term, human activities exerted a more substantial impact on eutrophication in Daihai Lake, while climatic factors played an accelerating and reinforcing role. These results are consistent with prior research in the Daihai area, indicating a state of mild eutrophication and the potential of the fuzzy classification method and comprehensive trophic status index method in eutrophication assessment.

Two unequal shots in the same hole: statistical versus deterministic deconvolution

Determination of the source time function and earth impulse response for explosive source seismic reflection data is traditionally performed using predictive deconvolution, which assumes the reflection seismogram is the convolution of a minimum-phase wavelet with a white, random, stationary sequence of impulses. We compare this statistical method with a new deterministic method that uses two unequal shots close together in the same hole and assumes a seismogram is the convolution of a source time function with a Green's function, plus noise. The seismograms from the two shots share essentially the same Green's function to any receiver, because the shots are close together. The Green's function is estimated by deconvolving the two seismograms at a given receiver for the corresponding source time functions, modelled independently; source parameters are obtained by trial-and-error by minimising the difference between the two estimates of the Green's function. We test these competing theories using seismic reflection data acquired with two charges fired separately in the same hole into a receiver spread of 1178 bunched geophone groups at 10 m intervals: a 1 kg charge at 42 m depth and 2 kg at 40 m depth. The criterion for comparison of the theories is the similarity of the recovered impulse responses at any receiver. Each method recovers a wavelet, or source time function, for each shot and two estimates of the impulse response, or Green's function, at each receiver. The Green's functions recovered from the two shots using the deterministic method are almost identical and are measurably more similar than the impulse responses recovered using predictive deconvolution. That is, the deterministic method is better. The resolution of explosive source seismic reflection data can now be significantly improved with the new method and minimal additional field effort of loading one extra charge per shot hole.

Classification of Citrus Leaf Diseases Using Hyperspectral Reflectance and Fluorescence Imaging and Machine Learning Techniques

Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of a portable reflectance and fluorescence hyperspectral imaging (HSI) system for detecting and classifying a control group and citrus leaf diseases, including canker, Huanglongbing (HLB), greasy spot, melanose, scab, and zinc deficiency. The HSI system was used to simultaneously collect reflectance and fluorescence images from the front and back sides of the leaves. Nine machine learning classifiers were trained using full spectra and spectral bands selected through principal component analysis (PCA) from the HSI with pixel-based and leaf-based spectra. A support vector machine (SVM) classifier achieved the highest overall classification accuracy of 90.7% when employing the full spectra of combined reflectance and fluorescence data and pixel-based analysis from the back side of the leaves, whereas a discriminant analysis classifier yielded the best accuracy of 94.5% with the full spectra of combined reflectance and fluorescence data and leaf-based analysis. Among the diseases, control, scab, and melanose were classified most accurately, each with over 90% accuracy. Therefore, the integration of the reflectance and fluorescence HSI with advanced machine learning techniques demonstrated the capability to accurately detect and classify these citrus leaf diseases with high precision.

Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red-Green-Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = -0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = -0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = -0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = -0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.

Caledonian reactivation and reworking of Timanian thrust systems and implications for latest Mesoproterozoic to mid-Paleozoic tectonics and magmatism in northern Baltica

Background The Trollfjorden–Komagelva Fault Zone is the southernmost thrust fault of the Timanian Orogen and extends for thousands of kilometers from northwestern Russia to northern Norway. Though there is little about its location onshore northeastern Norway, where it is mapped as a major fault system dominantly comprised of NNE-dipping thrust faults, its continuation to the west below Caledonian nappes and offshore post-Caledonian sedimentary basins remains a matter of debate. Methods The present study provides a more definitive answer about the continuation of Trollfjorden–Komagelva Fault Zone west of the Varanger Peninsula by using seismic reflection, bathymetric, topographic, and magnetic data onshore Finnmark and offshore on the Finnmark Platform. Results The present study demonstrates that the Sørøya–Ingøya shear zone represents a portion of the Trollfjorden–Komagelva Fault Zone that was folded into a NE–SW orientation and reactivated as a top-southeast thrust during the Caledonian Orogeny, while other portions of the Trollfjorden–Komagelva Fault Zone (e.g., on the Varanger Peninsula) were reactivated as strike-slip faults. The study also documents the presence of another major, NNE-dipping Timanian shear zone with a similar geometry to the Trollfjorden–Komagelva Fault Zone north of the Varanger Peninsula. Conclusions The Trollfjorden–Komagelva Fault Zone may continue offshore as a NE–SW-striking folded structure. This has the following implications: (1) the Seiland Igneous Province likely formed in a backarc setting, (2) metasedimentary rocks of the Kalak Nappe Complex deposited along the Baltican margin of the Iapetus Ocean, possibly in a late–post-Grenvillian collapse basin, (3) the Iapetus Ocean was much narrower than the several thousands of kilometers width commonly proposed, and (4) early Neoproterozoic magmatism in northern Norway is possibly related to the initial breakup of Rodinia.

Petroleum system analysis of Fujairah basin, eastern offshore of the United Arab Emirates

The Fujairah basin, located on the eastern margin of the United Arab Emirates, forms part of the hinterland basin of the Oman-UAE mountains. Despite its geological significance, the hydrocarbon potential of this basin remains unexplored. This study aims to address this knowledge gap by using 2D seismic reflection data, three exploration wells, geochemical data from one well, a 2D velocity section, and two pseudo-wells. The study began with the interpretation of the seismic profiles and then proceeded to create depth maps for the most significant units. We used Rock-Eval pyrolysis plots to identify the primary source rocks and to classify their kerogen types. The seismic interpretation formed a basis for the 1D and 2D basin modeling techniques that are used to determine the petroleum system of the basin. Our results identify the Pliocene, Miocene, and Eocene sequences as potential source rocks in the basin with TOC values less than 1 wt% and low expulsion efficiencies. The Pliocene and Miocene source rocks are mainly type-II kerogen, whereas most Eocene samples are characterized as type-III kerogen. The Pliocene and Miocene source rocks are immature in the basin. The Eocene is mature depending on the burial within the sub-basins. The Eocene started expelling hydrocarbons during the Burdigalian, which was linked to the collision of the Arabian and Central Iran plates along the Zagros suture zone. Structural and stratigraphic traps may have entrapped the generated hydrocarbons. The three drilled wells in the basin lack good reservoir rocks. However, low-velocity anomalies and bright spots indicate possible hydrocarbon accumulations in the basin.

Study on UV-VIS characteristics of Aluminum Zinc Oxide nanoparticles through manganese substitution using Wemple-Di domenico method and diffuse reflectance spectroscopy

In this research Manganese-substitution impact on linear and nonlinear (NL) optical properties of Aluminum-doped ZnO (AlZnO) Nanoparticles (NPs) are investigated. Utilizing measured data of reflectance acquired from the DRS, the higher-order NL optical characteristics of the provided NPs are examined. The maximum quantity of NL susceptibility χ3 is measured to be around 3.945 × 10−10 esu at wavelength λ = 520 nm and 7.467 × 10−11 esu at wavelength λ = 530 nm for AlZnO and Manganese-substituted Aluminum-doped Zinc-Oxide (Mn-also) NPs, for particle sizes 14−67 nm and 11−54 nm, consecutively. Consequently, the synthesis method (sol–gel) which is used in this work concluded a decrease in particle size and different optical behavior especially in UV spectral regions during the substitution with Manganese in comparison with the AlZnO. This decrement assures that a decrease in the size of crystallite induces a decline in the χ3. The obtained optical parameters are red shifted with the Manganese substitution. Also, the NL absorption coefficient (β c(m/W)) is enhanced in the UV range. The maximum of the NL absorption coefficient (β c(m/W)), for AlZnO and Mn-AlZnO NPs, are determined (at λ = 250 nm) 2.24 × 10−18 (m/W) and 5.76 × 10−15 (m/W) respectively.

Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery

AbstractThis study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M‐matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi‐kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M‐matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single‐kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi‐kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single‐kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy.

Flow conditions of the Quaternary Deep-water Current reconstructed by sediment waves in the northeastern South China Sea

The South China Sea (SCS) plays a key role in maintaining the circulation in the Pacific and Indian oceans. After entering the northeast South China Sea from the Luzon Strait, the Pacific Deep Water transforms into the Deep-water Current (DWC) and flows westward. The upwelling of the DWC in the SCS could outflow into both the Indian and Pacific oceans. However, when and how this modern circulation was established in the SCS remains unclear. By using seismic reflection data tied to the Ocean Drilling Program wells in the northeastern SCS margin, we have discovered fields of previously unreported sediment waves, of which the onset dates back to ∼2.6 Ma. The sediment wave heights increased from 2.0 to 7.5 m, in association with spatial extent from 630 km2 to 800 km2 between ∼2.6 Ma and ∼ 0.7 Ma. After that, the wave heights and spatial extent reduced to ∼5.5 m and 700 km2, respectively. Considering the location, morphological features and water depth, we propose that these sediment waves were formed by the DWC. The morphological changes of the sediment waves are linked to energy increase and decrease of the DWC within ∼2.6–0.7 Ma and ∼ 0.7–0 Ma, respectively. We interpret the intensification as caused by the narrowing and uplifting of the Luzon Strait that is the sole deep-water gateway of the SCS, and speculate that the post-0.7 Ma weakening was probably related to the reduced Kuroshio Current intrusion due to the middle Pleistocene climate transition. This study proposes a novel model for the evolution of the Quaternary DWC hydrodynamics, fostering our understanding of the paleo-oceanographic links between the SCS and the Pacific Ocean.

“I think they only study seawater”: Oceanography classroom career panel as a tool to promote geoscience career awareness

The utilization of a professional geoscience career panel as a curricular classroom activity can be essential for promoting career awareness. By interacting with professionals, students can be exposed to diverse geoscientific careers, helping them understand various professional paths and the real-world applications of their studies. Using social constructivism and critical reflection theories of learning, we designed and implemented a two-day career panel attended by ten ocean scientists in an undergraduate oceanography course for majors and non-majors. We used a qualitative, quasi-experimental design involving pre-and-post reflection data collected from the treatment group (n = 22) and control group (n = 19), and five post-treatment interviews to evaluate the effectiveness of the career panel on students’ ocean science career awareness. While the pre-reflections revealed that students in both the treatment and control groups had low awareness and knowledge about ocean science careers and practice, students in the treatment group showed improved awareness about ocean science careers and disciplines, processes of ocean science work, and the social and cultural impact of ocean science work after attending the career panel. Students in the treatment group also showed increased interest in ocean science and geosciences in general. Results indicate classroom career panel that include personal narratives, discussions, and students’ critical reflections can be effective tools to promote career awareness and interest in the geosciences. We provide implications for geoscience education such as supplementing classroom career panel with hands-on activities (e.g., job simulations), advanced organizers (e.g., worksite visits), and experiential learning opportunities (e.g., internships) as instructional tools to promote students’ career awareness.

The Mundeck Salt Unit: A Review of Aptian Depositional Context and Hydrocarbon Potential in the Kribi-Campo Sub-Basin (South Cameroon Atlantic Basin)

The Kribi-Campo sub-basin, located in the Gulf of Guinea, constitutes the southeastern segment of the Cameroon Atlantic Margin. Drilling in the Aptian salt unit revealed a sparse hydrocarbon presence, contrasting with modest finds in its counterparts like the Ezanga Salt in Gabon and the Rio Muni Salt in Equatorial Guinea. This discrepancy prompted a reassessment of the depositional context and hydrocarbon potential of the Mundeck salt unit. By integrating 2D seismic reflection and borehole data analysis, this study established the structural and stratigraphic framework of the area, emphasizing the salt unit’s significance. Borehole data indicate a localized salt unit offshore Kribi, with seismic reflection data revealing distinct forms of diapir and pillow. This salt unit displays a substantial lateral extent with thicknesses ranging from 4000 m to 6000 m. The depositional context is linked to the following two major geological events: a significant sea-level drop due to margin uplift during the Aptian and thermodynamic processes driven by transfer faults related to mid-oceanic ridge formation. These events were crucial in forming and evolving the Mundeck Salt. Regarding hydrocarbon prospects, this study identifies the unit as being associated with potential petroleum plays, supported by direct hydrocarbon indicators and fault-related structures. The findings suggest that untapped hydrocarbon resources may still exist, underscoring the need for further exploration and analysis.

Investigations on color quality improvement through laser-induced surface oxidation for coloration

This study focuses on laser-induced oxidation of TC4 alloy surfaces and achieves precise color control on TC4 surfaces at room temperature using laser post-processing. The current research lacks an in-depth understanding of the mechanism responsible for color changes due to the number of processing cycles. This study aims to improve the quality of coloration through secondary processing and establish a color control mechanism based on accumulated energy density. Statistical analyses revealed notable reductions in ΔE, ΔC∗, and Δh∗, indicating enhanced color uniformity. Surface morphology and reflectance data further confirmed these findings, demonstrating the effectiveness of secondary processing in achieving consistent and uniform coloration.

Mapping stem volume in fast-growing eucalypt plantations: integrating spectral, textural, and temporal remote sensing information with forest inventories and spatial models

Key messageAccurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points.ContextForest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery.AimsOur study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil.MethodsThe components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date.ResultsThe best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models.ConclusionThe linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions.

Seismic reflection data interpretation and petrophysical evaluation of the Meyal area, the Potwar Basin, Pakistan

Despite extensive exploration efforts in the Potwar Basin, a thorough understanding of the structural framework and reservoir characteristics within the Meyal Field remains critical for pinpointing potential hydrocarbon zones. This study aims to integrate geology and geophysics to emphasize the structural and stratigraphic interpretation of the Meyal Field, including reservoir characterization using seismic and well-log data. The seismic interpretation of six seismic lines focuses on four key reflectors: the Kamlial, Chorgali, Sakesar, and Salt Range Formations. The Eocene Chorgali and Sakesar Formations are of primary importance for exploration and production. Structural analysis revealed that the Meyal anticline is a plunging structure bounded by a back thrust to the north and a fore thrust to the south, suggesting a favorable location for hydrocarbon accumulation. Time-depth contour maps derived from seismic data further delineate potential sites for future investigations. The well correlation indicated an uplift toward the Meyal-2 compared with Meyal-5, signifying a shallowing trend attributed to thrust tectonics. This tectonic regime has rendered the area highly prospective for hydrocarbon exploration, with thrusting and oblique-slip faulting enhancing the reservoir qualities of Eocene Formations. The petrophysical evaluation revealed favorable reservoir characteristics, including an average porosity ranging from 0% to 12% with an effective porosity of approximately 7.5%, water saturation up to 42%, and hydrocarbon saturation reaching 58% within the pay zone of the reservoir. These findings suggest that the Sakesar and Chorgali Formations within the Meyal oil field hold promise as productive hydrocarbon reservoirs. The integrated study provides valuable insights into the structural framework and reservoir characterization, highlighting the potential of the Eocene Formations as productive hydrocarbon reservoirs supported by favorable structural configurations and petrophysical properties.

A Radar-Based Methodology for Aerosol Plume Identification and Characterisation on the South African Highveld

Biomass burning on the South African Highveld annually injects substantial amounts of aerosols and trace gases into the atmosphere, impacting the global radiative balance, cloud microphysics, and regional air quality. These aerosols are transported as plumes over long distances, posing challenges to existing in situ and satellite-based monitoring techniques because of their limited spatial and temporal resolution, particularly in environments with low-level sources. This study aims to develop and validate a novel radar-based methodology to detect, track, and characterise aerosol plumes, addressing the limitations of existing in situ and satellite monitoring techniques. Using high-resolution volumetric reflectivity data from an S-band radar in Pretoria, South Africa, a traditional storm tracking algorithm is adapted to improve plume identification. Case studies of plume events in June and August 2013 demonstrate the radar’s effectiveness in distinguishing lower vertical profiles and reduced reflectivity of plumes compared with storm echoes. The adapted algorithm successfully tracked the spatial and temporal evolution of the plumes, revealing their short-lived nature. Results indicate that radar-derived geospatial characteristics have the potential to contribute significantly to understanding the impacts of plumes on local air quality. These findings underscore the critical need for high spatio-temporal resolution data to support effective air quality management and inform policy development in regions affected by biomass burning.

A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas

National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM2.5 and O3 concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM2.5 and O3 concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM2.5 and O3 concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R2 values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM2.5 and O3 concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM2.5 concentrations.

Compact staring-type underwater spectral imaging system utilizing k-Nearest neighbor-based interpolation for spectral reconstruction

Besides the challenges posed by the complex optical properties of water, underwater spectral imaging face significant issues, including system bulkiness, optical complexity, and high spectral resolution requirement. This study introduces a compact staring-type underwater spectral imaging system designed to overcome these limitations. Featuring a 6-channel rotary spectral camera, the system is optimized for optical simplicity, and spectral efficiency. It employs a k-Nearest Neighbor (KNN)-based spectral reconstruction method to substantially enhance spectral resolution. Auto-focusing and image registration techniques, utilizing the Tenengrad function and Scale-Invariant Feature Transform (SIFT) feature points, ensure sharp and aligned multi-channel imagery. Experimental validation demonstrates the system’s capability to accurately reconstruct spectral reflectance data across the 400 nm to 700 nm band, significantly increasing spectral resolution from 6 to 31 channels. The spectral reflectance reconstruction results, achieving an average Root Mean Square Error (RMSE) of 0.0651 and Goodness of Fit Coefficient (GFC) of 0.9871, highlight the system’s efficiency in precise spectral analysis. This advancement in spectral resolution and adaptability to diverse underwater applications marks a significant leap forward in underwater observation and monitoring through spectral imaging.

Major tunnel valleys and sedimentation changes document extensive Early Pleistocene glaciations of the Barents Sea

Sedimentary records of Early Pleistocene (~2.6–0.8 Ma) glaciations are sparse on shelves, yet trough mouth fans on adjacent continental slopes provide a continuous record of ice-sheet and climate development throughout the Quaternary. Here, we interpret high-quality 3D seismic reflection data combined with borehole and chronostratigraphic information from a shelf-slope setting in the southwestern Barents Sea to study meltwater and sediment inputs to the deep ocean, focusing on the onset of Pleistocene glaciations. Sandy deposits were brought to the slopes of the high-latitude Bear Island Fan by a preglacial contourite-turbidite system from ⁓2.6–2.4 Ma. Muddy glacigenic debris flows document the first shelf-edge glaciation at ⁓2.4 Ma. From 1.78–0.78 Ma, muddy turbidity-current- and debris-flow-derived sediments were delivered from shelf to slope via six tunnel valleys measuring up to 12 km in width and 200 m in depth. These tunnel valleys and associated downslope deposits formed during the 41-kyr climate cycles of the Early Pleistocene, and evidence abundant channelized, meltwater discharges from these glaciations. Following the mid-Pleistocene transition to 100-kyr cycles, a change in the style of glaciation is suggested by a change in landform and facies associations consistent with a reduced meltwater contribution. This study shows that the Norwegian-Barents shelf was extensively glaciated in the Early Pleistocene, with a first shelf-edge glaciation from ~2.4 Ma.

Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application

ABSTRACT The current quantitative retrieval of Aerosol Optical Depth (AOD) typically uses Top-of-atmosphere (TOA) reflectance data obtained by the scale and offset terms of radiometric calibration. Errors can be introduced during the conversion of Digital Number (DN) values to TOA reflectance, restraining the accurate retrieval of AOD. Particularly when surface reflectance is high, conversion errors can significantly distort the retrieval of Aerosol Optical Depth (AOD), given the minimal impact of aerosols on the radiation detected by satellite sensors. To counteract this, we introduce an innovative retrieval algorithm that harnesses a Generative Adversarial Network (GANN), a deep learning (DL) model, to accurately derive AOD from the scaled digital number (DN) values of multi-spectral satellite imagery. The DL technology enables the use of scaled DN values for AOD retrieval, bypassing the conversion errors associated with TOA reflectance since it relies on a sample learning approach instead of radiative transfer calculations. To ensure the number and representativeness of training samples, a total of 37,103 samples from different underlying surfaces from 2014 to 2018 were obtained using a space-time matching strategy. Our K-cross validation demonstrates that employing DN values for AOD retrieval promises to yield higher accuracy compared to using TOA reflectance. Independent 2019 data were used to estimate GANN, MCD19A2 and MOD04_3K aerosol products. Our model showed superior performance compared to the other products in terms of a higher correlation (R = 0.8184) with AERONET AOD and obtaining more reliable retrievals (7601). The sensitivity experiment suggests that GANN exhibits limitations in areas characterized by high aerosol loads and heterogeneity. Our study can give a novel insight into AOD retrieval based on multi-spectral satellite and DL models.

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.