Earth Science Applications Research Articles

Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment

Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.

GENESIS: co-location of geodetic techniques in space

Improving and homogenizing time and space reference systems on Earth and, more specifically, realizing the Terrestrial Reference Frame (TRF) with an accuracy of 1 mm and a long-term stability of 0.1 mm/year are relevant for many scientific and societal endeavors. The knowledge of the TRF is fundamental for Earth and navigation sciences. For instance, quantifying sea level change strongly depends on an accurate determination of the geocenter motion but also of the positions of continental and island reference stations, such as those located at tide gauges, as well as the ground stations of tracking networks. Also, numerous applications in geophysics require absolute millimeter precision from the reference frame, as for example monitoring tectonic motion or crustal deformation, contributing to a better understanding of natural hazards. The TRF accuracy to be achieved represents the consensus of various authorities, including the International Association of Geodesy (IAG), which has enunciated geodesy requirements for Earth sciences. Moreover, the United Nations Resolution 69/266 states that the full societal benefits in developing satellite missions for positioning and Remote Sensing of the Earth are realized only if they are referenced to a common global geodetic reference frame at the national, regional and global levels. Today we are still far from these ambitious accuracy and stability goals for the realization of the TRF. However, a combination and co-location of all four space geodetic techniques on one satellite platform can significantly contribute to achieving these goals. This is the purpose of the GENESIS mission, a component of the FutureNAV program of the European Space Agency. The GENESIS platform will be a dynamic space geodetic observatory carrying all the geodetic instruments referenced to one another through carefully calibrated space ties. The co-location of the techniques in space will solve the inconsistencies and biases between the different geodetic techniques in order to reach the TRF accuracy and stability goals endorsed by the various international authorities and the scientific community. The purpose of this paper is to review the state-of-the-art and explain the benefits of the GENESIS mission in Earth sciences, navigation sciences and metrology. This paper has been written and supported by a large community of scientists from many countries and working in several different fields of science, ranging from geophysics and geodesy to time and frequency metrology, navigation and positioning. As it is explained throughout this paper, there is a very high scientific consensus that the GENESIS mission would deliver exemplary science and societal benefits across a multidisciplinary range of Navigation and Earth sciences applications, constituting a global infrastructure that is internationally agreed to be strongly desirable.Graphical

Aurora Detection From Nighttime Lights for Earth and Space Science Applications

AbstractThis research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer (VIIRS) instrument onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite. We demonstrate the value of mining the VIIRS DNB for aurora and describe our use of unsupervised machine learning to create a binary mask for aurora occurrence. This mask can be used to flag aurora‐contaminated observations for NASA's nighttime lights products for Earth science applications. The identification of auroral regions can also be used for Space Weather applications, for example, for comparison with aurora forecast model and with other satellite‐ or ground‐based aurora observations. The DNB is a broadband channel that is sensitive to wavelengths from 500 to 900 nm, which covers most of the visible light spectrum, and as the name implies, captures light even at night with a sensitivity at the nanowatt level. This band is suitable for aurora observations since the light emitted by the aurora tends to be dominated by emissions from atomic oxygen, resulting in a greenish glow at a wavelength of 557.7 nm, especially at an altitude of 110 km. This study compares the global nighttime derived aurora regions for 17 and 18 March with the NOAA Space Weather Prediction Center's (SWPC) probability product for the St. Patrick's Day geomagnetic storm in 2015. VIIRS sensors are slated to be added to the next generation of polar‐orbiting operational satellites. Our novel automated approach to aurora identification opens up an efficient way to leverage this unique data source.

A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

Abstract. High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020)

Terrestrial water storage (TWS) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) mission have been useful for several earth science applications, ranging from global earth system science studies to regional water management. However, the relatively short record of GRACE has limited its use in understanding the climate-driven interannual-to-decadal variability in TWS. Targeting these timescales, we used the novel method of cyclostationary empirical orthogonal functions (CSEOFs) and the common modes of variability of TWS with precipitation and temperature to reconstruct the TWS record of 1979–2020. Using the same common modes of variability, we also provide a realistic, time-varying uncertainty estimate of the reconstructed TWS. The interannual variability in the resulting TWS record is consistent in space and time, and links the global variations in TWS to the regional ones. In particular, we highlight improvements in the representation of ENSO variability when compared to other available TWS reconstructions.

Colombian soil texture: building a spatial ensemble model

Abstract. Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km2 of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, Varón-Ramírez and Araujo-Carrillo, 2022, https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, Varón-Ramírez et al., 2022) provide new insights to solve some of the aforementioned issues.

Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.

Kaniadakis Functions beyond Statistical Mechanics: Weakest-Link Scaling, Power-Law Tails, and Modified Lognormal Distribution

Probabilistic models with flexible tail behavior have important applications in engineering and earth science. We introduce a nonlinear normalizing transformation and its inverse based on the deformed lognormal and exponential functions proposed by Kaniadakis. The deformed exponential transform can be used to generate skewed data from normal variates. We apply this transform to a censored autoregressive model for the generation of precipitation time series. We also highlight the connection between the heavy-tailed -Weibull distribution and weakest-link scaling theory, which makes the -Weibull suitable for modeling the mechanical strength distribution of materials. Finally, we introduce the -lognormal probability distribution and calculate the generalized (power) mean of -lognormal variables. The -lognormal distribution is a suitable candidate for the permeability of random porous media. In summary, the -deformations allow for the modification of tails of classical distribution models (e.g., Weibull, lognormal), thus enabling new directions of research in the analysis of spatiotemporal data with skewed distributions.

Geostationary operational environmental satellite-R advanced baseline imager reflective solar band absolute validation using Sonoran desert scenes

The advanced baseline imagers (ABIs) on board the geostationary operational environmental satellite (GOES)-R series, GOES-16 and GOES-17 satellites, have accumulated long data series that have been updated throughout their missions to enhance their utility for imaging the Earth’s weather, oceans, and environment. In preparing for a reprocessing effort aimed at improving the consistency and image quality and reducing radiometric uncertainties necessary for some Earth science applications, we developed a vicarious validation technique for five of the six reflective solar bands. This technique uses Sonoran desert surface reflectance data derived from the moderate resolution imaging spectrometer bidirectional reflectance distribution function parameters product along with other atmospheric products to generate a predicted at-sensor radiance for both GOES-16 and GOES-17 for comparison with ABI observations and to enable ABI intercomparisons. This technique identified known instrument biases and demonstrated absolute validation over the course of a year, showing its capability to detect changes, monitor stability, and assess radiometric uncertainties for operational data applications and future reprocessed data sets.

A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation

Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information.

230Th dating of gypsum from lacustrine, brackish-marine and terrestrial environments

Gypsum is an important environmental archive in hyperarid settings, but its application in earth sciences has been hampered by the limited availability of suitable dating methods. Here we re-evaluate the potential of 230Th/U dating for sedimentary gypsum as well as anhydrite samples in different depositional environments. We provide a robust analytical protocol based on a simple dissolution using nitric acid followed by a two-column separation procedure using anion exchange resin. Isotope analyses were performed using MC-ICPMS and a mixed 229Th-233U-236U tracer. We applied the method to a suite of samples that likely reached secular equilibrium as well as to three suites of younger (<300 ka old) samples, two from the Atacama Desert in Chile and one from a drill core of a saline playa lake in southern Spain. We employed a selective sampling approach targeting the least visually altered and clearest parts of the samples largely devoid of detritus and analysed multiple subsamples.The results demonstrate the general applicability of the 230Th/U method to gypsum as well as anhydrite, and recrystallization of gypsum to anhydrite does not significantly affect the U-series isotope system. However, there is evidence for open-system behaviour, which needs to be carefully assessed. Our results suggest that α-recoil processes as well as uptake or loss of uranium are important processes biasing the results of gypsum dating. Furthermore, we see signs of multiple-phase contamination in our case studies involving detrital components and authigenic phases. Based on these results we propose geochemical criteria in to identify biased 230Th/U gypsum ages.The application of our protocol to a suite of selenite samples deposited by brine outflows on the lowest terrace of Cerro Soledad (Atacama Desert, Chile) provides a mean age of 212 ± 8 ka, indicating a relatively humid climate interval with local rainfall in this area that is coinciding with marine isotope stage 7C. Selenites deposited in the paleo-lake Soledad system (Atacama Desert, Chile) yield 230Th/U ages ranging from 250 to 320 ka, in agreement with previous age estimates for the drainage of this paleo-lake obtained by cosmogenic nuclide dating. Application to a suite of gypsum crystals recovered from a drill core in the Laguna de Fuente de Piedra (South Spain) shows good agreement with their stratigraphic context, however, multiple-phase contamination produces larger age uncertainties for these ages.

STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention

Spatiotemporal fusion in remote sensing plays an important role in Earth science applications by using information complementarity between different remote sensing data to improve image performance. However, several problems still exist, such as edge contour blurring and uneven pixels between the predicted image and the real ground image, in the extraction of salient features by convolutional neural networks (CNNs). We propose a spatiotemporal fusion method with edge-guided feature attention based on remote sensing, called STF-EGFA. First, an edge extraction module is used to maintain edge details, which effectively solves the boundary blurring problem. Second, a feature fusion attention module is used to make adaptive adjustments to the extracted features. Among them, the spatial attention mechanism is used to solve the problem of weight variation in different channels of the network. Additionally, the problem of uneven pixel distribution is addressed with a pixel attention (PA) mechanism to highlight the salient features. We transmit the different features extracted by the edge module and the encoder to the feature attention (FA) module at the same time after the union. Furthermore, the weights of edges, pixels, channels and other features are adaptively learned. Finally, three remote sensing spatiotemporal fusion datasets, Ar Horqin Banner (AHB), Daxing and Tianjin, are used to verify the method. Experiments proved that the proposed method outperformed three typical comparison methods in terms of the overall visual effect and five objective evaluation indexes: spectral angle mapper (SAM), peak signal-to-noise ratio (PSNR), spatial correlation coefficient (SCC), structural similarity (SSIM) and root mean square error (RMSE). Thus, the proposed spatiotemporal fusion algorithm is feasible for remote sensing analysis.

Planning Satellite Swarm Measurements for Earth Science Models: Comparing Constraint Processing and MILP Methods

We compare two planner solutions for a challenging Earth science application to plan coordinated measurements (observations) for a constellation of satellites. This problem is combinatorially explosive, involving many degrees of freedom for planner choices. Each satellite carries two different sensors and is maneuverable to 61 pointing angle options. The sensors collect data to update the predictions made by a high-fidelity global soil moisture prediction model. Soil moisture is an important geophysical variable whose knowledge is used in applications such as crop health monitoring and predictions of floods, droughts, and fires. The global soil-moisture model produces soil-moisture predictions with associated prediction errors over the globe represented by a grid of 1.67 million Ground Positions (GPs). The prediction error varies over space and time and can change drastically with events like rain/fire. The planner's goal is to select measurements which reduce prediction errors to improve future predictions. This is done by targeting high-quality observations at locations of high prediction-error. Observations can be made in multiple ways, such as by using one or more instruments or different pointing angles; the planner seeks to select the way with the least measurement-error (higher observation quality). In this paper we compare two planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. DCP uses domain heuristics to find solutions within a reasonable time for our application but cannot be proven optimal, while the MILP produces provably optimal solutions. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.

An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation

AbstractIt is crucially important to estimate unknown parameters in process‐based models by integrating observation and numerical simulation. For many applications in earth system sciences, a parameter estimation method which allows parameters to temporally change is required. In the present paper, an efficient and practical method to estimate time‐varying parameters of relatively low dimensional models is presented. In the newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE‐PF), an inflation method to maintain the spread of ensemble members in a Sampling‐Importance‐Resampling Particle Filter (SIRPF) is improved using a non‐parametric posterior probabilistic distribution of time‐invariant parameters obtained by comparing simulated and observed climatology. HOOPE‐PF outperforms the original SIRPF in synthetic experiments with toy models and a real‐data experiment with a conceptual hydrological model when an ensemble size is small. The advantage of HOOPE‐PF is that its performance is not greatly affected by the size of perturbation to be added to ensemble members to maintain their spread while it is important to get the optimal performance in the original particle filter. Since HOOPE‐PF is the extension of the existing particle filter which has been extensively applied to many models in earth system sciences such as land, ecosystem, hydrology, and paleoclimate reconstruction, HOOPE‐PF can be applied to improve the simulation of these process‐based models by considering time‐varying model parameters.

Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective

AbstractMachine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Representation Uncertainty in the Earth Sciences

AbstractThe first Joint Workshop on Representation Uncertainty in the Earth Sciences was held in March 2021. This brought together the Earth observation, data assimilation and forecast verification and post‐processing communities, alongside metrologists to discuss the definition and quantification of representation uncertainty within the Earth sciences. The aim of the workshop was to facilitate cross‐disciplinary discussion, establishing where existing methodologies could be shared and to foster future collaboration. A key outcome of the workshop was a working definition of representation uncertainty applicable across all the Earth science communities, which is presented in this white paper. The cross‐disciplinary discussions at the workshop highlighted the need for scientists to work with metrologists to establish a common vocabulary for uncertainties, accessible to Earth science applications. Further recommendations included regular workshops to discuss progress in defining and quantifying representation uncertainty and awareness of cross‐disciplinary funding opportunities to further address representation uncertainty issues.

A HYPERSPECTRAL REMOTE SENSING FUSION TECHNOLOGY BASED ON SPECTRAL NORMALIZATION OF GF AND ZY SERIES SATELLITES

Abstract. Globalized surface coverage, environmental monitoring and other earth system science and high-quality global surface coverage monitoring applications urgently need basic hyperspectral remote sensing reflectance data to support. Spectral reflectance is the core data product of hyperspectral satellite remote sensing data, which is directly related to the application efficiency and quality of hyperspectral satellite. Due to the differences in spectral range, resolution width, central wavelength and other factors between multispectral and hyperspectral remote sensing images, the fusion processing of hyperspectral and multispectral remote sensing images is extremely difficult. In this study, the idea of normalization of physical parameters is adopted. Based on the remote sensing data of multi-source satellites such as GF5, GF1WFV and ZY3, the physical parameters of GF series satellites are fused to improve the spatial resolution of hyperspectral remote sensing images while the physical parameters of surface parameters are maintained. According to the fusion quality evaluation results of noise, information entropy, clarity, spectral angle cosine, correlation coefficient and root mean square error, the fusion results are consistent with the original numerical performance. The noise level, information entropy and clarity index of GF5 and ZY3 fusion are better. The spectral angle cosine, correlation coefficient and root mean square error index of GF5 and GF1 fusion are better. This study provides a new way and method for high-precision and quantitative processing of hyperspectral remote sensing data.

Aurora Detection from Nighttime Lights for Earth and Space Science Applications

Aurora Detection from Nighttime Lights for Earth and Space Science Applications

Recent advances in MC-ICP-MS applications in Earth and environmental sciences: Challenges and solutions

• History, instrumentation of MC-ICP-MS and its up-to-date developments. • Introduction of various sampling methods including femtosecond laser ablation. • Role of CRMs in obtaining precise isotope ratios and elemental concentrations. • Technique with high potential for applications in Earth sciences and environment. The development of multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) in 1992 opened up new opportunities for geochemical, mineral exploration, and environmental studies. Recent developments in MC-ICP-MS and its hyphenation to techniques such as laser ablation including femtosecond laser sampling, advances in detection system and several other developments made MC-ICP-MS, one of the most versatile analytical techniques for the determination of isotopic compositions in several types of earth and environmental materials. Different studies in recent times indicated that MC-ICP-MS can routinely produce isotopic data that is similar in precision to those obtainable by secondary ion mass spectrometry (SIMS), sensitive high resolution ion microprobe (SHRIMP), and thermal ionization mass spectrometry (TIMS), with striking advantages such as relatively higher throughputs, ease of analysis as the source operates at atmospheric pressure, and possibility of sample introduction by other means such as laser ablation and chromatographic techniques. Recent studies demonstrate the decisive benefits of MC-ICP-MS analysis (both solution and laser ablation modes) not only isotopic ratio determinations, but also absolute element concentration determinations of several elements in the periodic table in different types of geological and environmental materials in terms of versatility, accuracy, precision and for improved spatial resolution. Laser ablation split stream (LASS) technique with ability to do dating, and capability to obtain isotopic and elemental information simultaneously in rocks and minerals on a single sampling event, when coupled to a second ICP-MS system (typically a quadrupole ICP-MS or a high resolution-ICP-MS) demonstrated its potential to become an important analytical tool in solving important problems, especially in geochemistry. The performance characteristics of MC-ICP-MS are compared with those of other similar analytical techniques such as TIMS, SHRIMP, and SIMS with recent examples. This review contains detailed descriptions of the development of MC-ICP-MS instrumentation, analytical procedures, reference materials, and focused applications in the area of geochemistry, mineral exploration, and environmental areas.

A Novel Machine Learning–Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data by Combining Downscaling and Regression

Abstract Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.