Variability in Arctic sea ice optical properties

Melt ponds are usually modeled for light transfer as horizontally infinite water layers on level ice, and the albedo of floe is determined by a linear combination (LC) of melt pond and bare ice albedos weighted by their areal coverages. However, this method does not reflect the actual conditions because ice floes have a limited size. In the present study, an idealized two‐dimensional Monte Carlo (MC) model was employed to investigate the influence of melt ponds and floe size on the apparent optical properties (AOPs) of summer sea ice. The results showed that the albedo and vertical light transmittance of large floes mainly depend on the melt pond fraction and ice thickness, which is consistent to previous results. However, also the floe size plays an important role in the AOPs of small floes. Two parameters were proposed to present the accuracy of the LC method for small floes with lower sea ice concentration: the ratios of sea ice albedo and transmittance determined by the LC (αline, Tline) to the values in the MC model (α, T), Kα = αline/α, and KT = Tline/T, respectively. Due to the lateral transmittance, Kα, KT ≥ 1 and asymptotically approach 1 with floe size increasing to infinity. To reduce the biases in albedo and transmittance due to floe size, new parameterization formulas were provided for Kα and KT with the distance into the marginal ice zone and in different melting stages. The results have potential to be implemented into future sea ice models to correct the AOPs of small sea ice floes obtained via the LC method.

Spatial variability in CaCO3 solubility is an important factor in parameterizing simulation models and designing experiments. The objective of this study was to quantify the spatial variability, both horizontal and vertical, in CaCO3 solubility in a Chihuahuan Desert soil. CaC03 solubilities were estimated in 1: 5 soihwater suspensions. Soil horizon extracts were generally supersaturated with respect to calcite. The mean (± 1 SE) pIAP(CaCO3) for the A, Bk1, and Bk2 horizons were 8.03 (0.055), 8.19 (0.019), and 8.26 (0.015), respectively. The differences in pIAP between the A and B horizons (vertical variability) were statistically significant; these differences could be due to organic matter inhibition of calcite precipitation. Supersaturation with respect to calcite and vertical variability in CaCO3 solubility needs to be explicitly considered in simulation models. The standard errors in pIAP (horizontal variability) were greatest for the A horizons and decreased with increasing soil depth. Given the inherent variability in CaCO3 solubility, a large sample size is necessary to detect small differences in CaCO3 solubility for this Chihuahuan Desert soil.

A knowledge of the reflection of light from a sea ice cover is important for the interpretation of remote sensing imagery at visible and near‐infrared wavelengths, for climatological studies involving the energy balance of the polar regions, and for understanding radiative transfer in sea ice. Spectral measurements of albedo, bidirectional reflectance distribution function, and polarized reflectance were made for Arctic sea ice conditions found during the onset of melt. The wavelength region studied was from the visible to the near infrared (400–1000 nm). Results are presented for the five following surface types: (1) dry snow, (2) dry snow with a glazed surface, (3) bare ice, (4) blue ice, and (5) melt pond. Results indicate that spectral albedos decrease at all wavelengths as the melt season progresses and the surface conditions evolve from (1) through (5) and that the decrease is most pronounced at longer wavelengths. Reflectance data suggest that at most angles, reflectance has the same spectral shape as albedo; at 30° zenith, reflectance is for the most part azimuthally isotropic; and at 60° zenith there is a significant specular component at 0° azimuth, especially for the bare ice cases. Light reflected at 60° zenith angle was strongly polarized. In general, light reflected from snow‐free ice was more strongly polarized, with the degree of polarization increasing with wavelength.

The assessment of spatial variability in the ground through field experiments has many constraints due to non-homogenous ground conditions and lack of site investigations. For this reason, spatial variability has not been considered in typical analyses. Also, few studies have been conducted on ground spatial variability using models in the laboratory. However, it is necessary to evaluate spatial variability in the ground for more precise analysis and design of construction projects. In this study, in order to evaluate spatial variability in the ground, we performed a number of Mini-Cone Penetration Tests (CPTs) in ground models with one layer of silica sand soil and two layers of silica and weathered soils. Through the Mini-CPTs, cone penetration resistances with depth were measured at many points. Based on the data, the coefficient of variation (CV) and the correlation length (CL) were calculated to quantitatively analyze the vertical and horizontal variability in the ground models. The results showed how the spatial variability in the two ground models varied. This implies that considering spatial variability in the ground can significantly enhance the accuracy of the analysis and design of construction projects.

Accurate quantification and characterization of the Arctic summer ice cover are needed for mass balance, heat flux, and modeling studies in the region. A general assessment of the state and basic characteristics of the ice cover can best be done in summer because it is when the perennial component is fully revealed. The main source of summer ice information has been passive microwave and to a lesser degree active microwave data. However, the emissivity and backscatter of sea ice are abnormal and difficult to resolve during this time period, causing large uncertainties in the interpretation of satellite data. In this study we examined the state of the sea ice cover by using special scanning microwave imager (SSM/I), synthetic aperture radar (SAR), and advanced very high resolution radiometer (AVHRR) satellite data synergistically. The surface and radiative characteristics of the summer ice cover were evaluated in the context of three special events: onset of melt, melt ponding, and freeze‐up. These events affect the emissivity and backscatter and may alter the albedo and ice structure. Onset of melt is readily detectable and is shown to migrate rapidly to the north in June. Melt ponding is not directly observable but is postulated to be the main cause of the decreases in brightness temperatures and large discrepancies between the SSM/I and SAR ice concentration results in many areas. In these areas, SAR and AVHRR results show concentrations near 100%, while the SSM/I data were as low as 70%. During freeze‐up the ice signatures are still quite different from those of midwinter ice, but the ice concentrations from SSM/I generally agree well with those from SAR data. Our results show that, generally, the average ice concentration within the pack is usually greater than 90% during the summer, which is substantially larger than that inferred previously from passive microwave data. The use of combined SAR and SSM/I data may also provide melt‐ponding fraction and first‐order estimate of albedo in the Arctic region.

Internal leachate quality in a municipal solid waste landfill: Vertical, horizontal and temporal variation and impacts of leachate recirculation

Information on the spatial and temporal variability of rain rate is important not only for meteorology and hydrology but also for the design of remote sensing and in situ measuring or of millimeter wave communication systems. The Ocean Topography Experiment (TOPEX)/Jason Tandem Mission (TM) collocated rain dataset is used in this study to determine the small space-scale (5 km) and time-scale (70 s) rain variability. TOPEX and Jason dual-frequency (Ku and C bands) radar altimeter data have been extensively used during the past decade to detect and study oceanic precipitations. During the TM, designed to intercalibrate and validate the two altimeters, the two satellites were put on the same orbit with a 70-s time separation. With combined use of altimeter and passive microwave radiometers (also available on board altimeter missions), rain intensity, rain attenuation, rain layer height, rain event length, and surface winds can also be estimated and provide valuable coincident geophysical contextual information. The size of the TM collocated rain database (140 000 samples) is large enough to allow a meaningful statistical analysis of the time–space variability of rain over the World Ocean. The analysis of the different terms contributing to the variability of rain attenuation, from which rain rate is inferred, shows that the geophysical and/or instrumental noise is small enough to allow a meaningful estimation of the variability of the measured rain rate. The analysis of the time and space variabilities and their relation reveals two well-defined regimes. The first one, corresponding to convective rain cells (i.e., rain rate greater than 3–4 mm h−1, length smaller than 50 km, and freezing level greater than 3.5 km), is characterized by high temporal and spatial variabilities (greater than 2–3 mm h−1) that increase with increasing rain intensity and decreasing cell length. Horizontal variability is significantly larger than the temporal one and surface wind has a very limited impact. The second regime corresponds to stratiform and/or weak rain cells. The temporal and spatial variabilities are relatively low (on the order of 1–2 mm h−1) and vary little with rain intensity and cell length. The temporal variability increases with surface wind and largely exceeds the spatial variability (ratio of 2 or more); the ratio strongly increases with increasing wind speed.

Degradation of pesticides in aquifers has been evaluated based on a number of co-ordinated field and laboratory studies carried out in Danish aquifers. These studies included investigations of vertical and horizontal variability in degradation rates from the vadose zone to an aquifer, the effects of aerobic versus anaerobic conditions, and the importance of concentration on degradation kinetics for a selected range of herbicides. The studies were based on different experimental approaches ranging from simple batch experiments to column studies to field injection experiments and, where appropriate, results were compared. Some herbicides were degraded under aerobic conditions (some phenoxy acids, DNOC and glyphosate) and others under aerobic conditions (other phenoxy acids, DNOC; there was some indication of atrazine transformation). Certain pesticides were not degraded in any investigations (dichlobenil, the dichlobenil metabolite 2,6-dichlorobenzamide (BAM), bentazone, isoproturon, metamitron and metsulfuron-methyl). The spatial variability was substantial, since hardly any of the investigated pesticides were degraded in all comparable samples. This means that it is very difficult to claim that a given pesticide is readily degradable in aquifers. However, the experimental approaches used (with incubations lasting more than a year) may not be sensitive enough to verify the low degradation rates that may be significant as a result of the long retention time of groundwaters.

UE regulations focus on methods of water quality monitoring and their use in rational management practices. This study investigated horizontal and vertical variations of electrical conductivity (EC), pH, dissolved oxygen (DO), and chlorophyll a (Chl-a) in a shallow polymictic lake. Monitoring of short-term variability of physical and chemical lake water parameters is a critical component in lake management, as it influences aquatic life. Based on the field research, maps of spatial distribution of the parameters were drawn. Using two methods: (1) a classical approach to water column measurements, from the top to the bottom (TB), in which the reference point is always a surface layer (SL), and (2) a newly introduced method of lake water quality monitoring based on a nearest neighbor (NN) approach; a comparison of higher and lower layers of the water column. By subtracting partial maps of spatial variability for different depths, final raster images were obtained. The NN method is rather absent in the limnology literature worldwide. Vertical and horizontal variability of the tested parameters in the polymictic, shallow Lake Bikcze (Poland) was presented in the results. In the presented paper, the commonly used TB method emphasized the role of the surface layer in shaping the variability of physicochemical parameters of lake waters. It shows a general trend of parameters’ changes from the top, to the bottom. The newly presented NN method, which has a major advantage in its simplicity and objectivity, emphasized structural differentiation within the range of variability. The nearest neighbor method was more accurate in showing the actual structure of fluctuation of parameters with higher fluctuation in the water column. Its advantage is a detailed recognition of the vertical variability of selected parameters in the water column. The method may be used regardless of the lake depth, its location in climatic zone, and/or region.

Sediment geoacoustic and roughness measurements were made during a mine burial experiment in the North Sea in November 1997. The experiment site was a sand ridge (20.5–32-m water depth) influenced by strong tidal currents and wave action. Stereo photographs indicated that the sediment roughness was characterized by cross-rippled fine sand that transitioned to straight- and sinuous-rippled mixtures of fine quartz sand and mollusk shell fragments. Box core samples were subsampled with core tubes from which measurements of sediment compressional wave velocity and attenuation, density, and grain size distribution were made. Values for sediment velocity ratio, density ratio, slope of the roughness power spectrum, roughness spectral strength, and other measured and calculated parameters were used as input parameters for the composite roughness model to predict bottom backscattering strengths from various regions within the experiment site. Horizontal and vertical variability in sediment properties is high relative to other shallow sites under the influence of strong hydrodynamic forcing. There is a marked interdependence among sediment geoacoustic properties, water depth and bottom roughness features. The influence of hydrodynamic conditions and sediment type on the acoustic model predictions is demonstrated by the spatial variability in predicted scattering of high-frequency sound from the site.

Increasing awareness of the role of spatial variability in the assessment of liquefaction susceptibility has driven the need to characterize the vertical and horizontal spatial variability ...

characterization of the spatial variability of geotechnical properties of river embankment soils is important for the enhanced modelling and assessment of embankment stability. In practice, available data is usually limited. This study presents the results of a quantitative statistical analysis of the spatial variability of cone tip resistance from the results of 16 closely spaced SCPT carried out near a recent breach in a levee on the Panaro river in northern Italy. Two geotechnical homogeneous soil units are preliminary identified in the levee to ensure the meaningfulness of the analysis in terms of soil type. For each unit, the horizontal and vertical spatial variability and spatial correlation structures of cone tip resistance are investigated by a two-step procedure involving the calculation of empirical semivariograms and the subsequent fitting of semivariogram models. Horizontal and vertical scales of fluctuation are estimated based on fitted semivariogram model parameters.

Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery

Cropping patterns, particularly intercropping - cultivating two or more crops within the same field, are prevalent in smallholder farming systems, enabling farmers to optimize land use, improve soil fertility, and increase resilience to environmental pressures such as pests, diseases, and climate extremes. When implemented effectively, intercropping promotes biodiversity and stabilizes yields. However, the diversity of crops and their interactions in intercropped fields make monitoring crop growth and estimating crop yields challenging. The dynamic and diverse nature of these practices often goes unreported in agricultural statistics, making them even more challenging to identify and document accurately. <br/>Traditional methods, such as field surveys and manual crop assessments, often lack the precision needed to capture detailed variations in crop types, growth and health within these cropping patterns. Further, the capturing methods can be time-consuming, labor-intensive, and prone to human-error and prone to missing important aspects of crop growth. While remote sensing offer a more efficient way to map and monitor crops, the mapping of cropping patterns is often generalized. The fields are often classified as broad cropland categories, or identified only by the most dominant crop in the field. This oversimplification result in the loss of critical information regarding the location of various types of cropping patterns, as a result, key data needed for improving yields and managing resources effectively are often overlooked. Therefore, using both remote sensing data and field data to explore and understand the dynamics of these cropping patterns is crucial for eventually optimizing cropping practices and boosting crop productivity. <br/>The small field sizes and variability in cropping practices of diverse agricultural landscapes requires high spatial and high temporal resolution data for discrimination and monitoring of cropping patterns. Remote sensing plays a key role in monitoring the spatial and temporal variability of growth phases in cropping patterns. This thesis demonstrates the spatiotemporal and spectral variability of cropping patterns using imagery data from Sentinel-2, PlanetScope, and DESIS hyperspectral sensors. The aim was to enhance our understanding of how to monitor and distinguish the complex cropping patterns, particularly those involving intercropping. <br/>This thesis addressed three key aspects of monitoring and discriminating cropping patterns using remote sensing data: the temporal, spectral, and spatial variability of cropping patterns, with a focus on intercropping. First, we focused on understanding annual cropping patterns using remote sensing, which highlighted the gaps and challenges in mapping intercropping. We then identified critical crop growth phases for discriminating cropping patterns using Sentinel-2, emphasizing the importance of temporal resolution. Using Sentinel-2 and DESIS hyperspectral data, we also examined key spectral regions for better discrimination of cropping patterns, exploring the role of spectral resolution. Lastly, we assessed the spatial variability of cropping patterns within different intercropping patterns using PlanetScope. Both primary and secondary field data were used in this study. Key challenges included the lack of detailed field data, diverse cropping practices and the small field sizes, which played a role in the classification accuracy. Additional challenges arose from the complexity of intercropped fields and their dynamic interactions during the crop growing phases. The study highlighted the importance of resolution in identifying critical crop growth phases and improving pattern discrimination through the analysis of temporal, spectral, and spatial variability using Sentinel-2, DESIS, and PlanetScope data, further, despite these challenges, the findings contribute valuable insights into the potential of remote sensing for better cropping pattern monitoring and mapping of smallholder farms.<br/>

This paper compiles a large cone penetration test (CPT) database, named CPT3D/3/2577, to investigate the horizontal and vertical autocovariance structures of soil spatial variation. It contains three CPT parameters (cone tip resistance, sleeve friction, and porewater pressure) from 2,577 soundings taken at 186 sites. Nearly all sites are extracted from the New Zealand Geotechnical Database. The database contains sites with multiple CPTs with known horizontal (x–y) coordinates, so not only the vertical autocovariance parameters [such as scale of fluctuation (SOF) and smoothness] but also the horizontal ones can be investigated. The cone tip resistance data from multiple CPTs at each site are analyzed by a random field model whose trend is explicitly modeled, enabling the characterization of the autocovariance parameters for both the spatial variability part (ε) and trend part (t). The identification results of the 186 sites indicate that the vertical SOF for ε varies in a narrow range, but the ratio of horizontal to vertical SOF for ε varies in a wide range (from 1 to 100). The results also indicate that the horizontal SOF for t is large (easily exceeding tens to hundreds of meters), but the vertical SOF for t is much smaller (sometimes only a few meters). The practical implications of these identification results are also discussed.