Total and partial cloud amount detection during summer 2005 at Westerland (Sylt, Germany)

The detection of cloudiness is investigated by means of partial and total cloud amount estimations from pyrgeometer radiation measurements and visible all-sky imager observations. The measurements have been performed in Westerland, a seaside resort on the North Sea island of Sylt, Germany, during summer 2005. An improvement to previous studies on this subject resulting in the first time partial cloud amounts (PCAs), defined as cloud amounts without high clouds calculated from longwave downward radiation (LDR) according to the APCADA algorithm (Dürr and Philipona, 2004), are validated against both human observations from the National Meteorological Servive DWD at the nearby airport of Sylt and digital all-sky imaging. The aim is to establish the APCADA scheme at a coastal midlatitude site for longterm observations of cloud cover and to quantify errors resulting from the different methods of detecting cloudiness. Differences between the resulting total cloud amounts (TCAs), defined as cloud amount for all-cloud situations, derived from the camera images and from human observations are within ±1 octa in 72% and within ±2 octa in 85% of the cases. Compared to human observations, PCA measurements, according to APCADA, underestimate the observed cloud cover in 47% of all cases and the differences are within ±1 octa in 60% and ±2 octa in 74% of all cases. Since high cirrus clouds can not be derived from LDR, separate comparisons for all cases without high clouds have been performed showing an agreement within ±1(2) octa in 73(90)% for PCA and also for camera-derived TCA. For this coastal mid-latitude site under investigation, we find similar though slightly smaller agreements to human observations as reported by Dürr and Philipona (2004). Though limited to daytime, the cloud cover retrievals from the sky imager are not really affected by cirrus clouds and provide a more reliable cloud climatology for all-cloud conditions than APCADA

Accuracy of UAV Photogrammetry in Glacial and Periglacial Alpine Terrain: A Comparison With Airborne and Terrestrial Datasets

Unoccupied Aerial Vehicles (UAVs) equipped with optical instruments are increasingly deployed in high mountain environments to investigate and monitor glacial and periglacial processes. The comparison and fusion of UAV data with airborne and terrestrial data offers the opportunity to analyse spatio-temporal changes in the mountains and to upscale findings from local UAV surveys to larger areas. However, due to the lack of gridded high-resolution data in alpine terrain, the specific challenges and uncertainties associated with the comparison and fusion of multi-temporal data from different platforms in this environment are not well known. Here we make use of UAV, airborne, and terrestrial data from four (peri)glacial alpine study sites with different topographic settings. The aim is to assess the accuracy of UAV photogrammetric products in complex terrain, to point out differences to other products, and to discuss best practices regarding the fusion of multi-temporal data. The surface geometry and characteristic geomorphological features of the four alpine sites are well captured by the UAV data, but the positional accuracies vary greatly. They range from 15 cm (root-mean-square error) for the smallest survey area (0.2 km2) with a high ground control point (GCP) density (40 GCPs km−2) to 135 cm for the largest survey area (>2.5 km2) with a lower GCP density (<10 GCPs km−2). Besides a small number and uneven distribution of GCPs, a low contrast, and insufficient lateral image overlap (<50–70%) seem to be the main causes for the distortions and artefacts found in the UAV data. Deficiencies both in the UAV and airborne data are the reason for horizontal deviations observed between the datasets. In steep terrain, horizontal deviations of a few decimetres may result in surface elevation change errors of several metres. An accurate co-registration and evaluation of multi-temporal UAV, airborne, and terrestrial data using tie points in stable terrain is therefore of utmost importance when it comes to the investigation of surface displacements and elevation changes in the mountains. To enhance the accuracy and quality of UAV photogrammetry, the use of UAVs equipped with multi-spectral cameras and high-precision positioning systems is recommended, especially in rugged terrain and snow-covered areas

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Accuracy of UAV Photogrammetry in Glacial and Periglacial Alpine Terrain: A Comparison With Airborne and Terrestrial Datasets

Unoccupied Aerial Vehicles (UAVs) equipped with optical instruments are increasingly deployed in high mountain environments to investigate and monitor glacial and periglacial processes. The comparison and fusion of UAV data with airborne and terrestrial data offers the opportunity to analyse spatio-temporal changes in the mountains and to upscale findings from local UAV surveys to larger areas. However, due to the lack of gridded high-resolution data in alpine terrain, the specific challenges and uncertainties associated with the comparison and fusion of multi-temporal data from different platforms in this environment are not well known. Here we make use of UAV, airborne, and terrestrial data from four (peri)glacial alpine study sites with different topographic settings. The aim is to assess the accuracy of UAV photogrammetric products in complex terrain, to point out differences to other products, and to discuss best practices regarding the fusion of multi-temporal data. The surface geometry and characteristic geomorphological features of the four alpine sites are well captured by the UAV data, but the positional accuracies vary greatly. They range from 15 cm (root-mean-square error) for the smallest survey area (0.2 km2) with a high ground control point (GCP) density (40 GCPs km−2) to 135 cm for the largest survey area (&gt;2.5 km2) with a lower GCP density (&lt;10 GCPs km−2). Besides a small number and uneven distribution of GCPs, a low contrast, and insufficient lateral image overlap (&lt;50–70%) seem to be the main causes for the distortions and artefacts found in the UAV data. Deficiencies both in the UAV and airborne data are the reason for horizontal deviations observed between the datasets. In steep terrain, horizontal deviations of a few decimetres may result in surface elevation change errors of several metres. An accurate co-registration and evaluation of multi-temporal UAV, airborne, and terrestrial data using tie points in stable terrain is therefore of utmost importance when it comes to the investigation of surface displacements and elevation changes in the mountains. To enhance the accuracy and quality of UAV photogrammetry, the use of UAVs equipped with multi-spectral cameras and high-precision positioning systems is recommended, especially in rugged terrain and snow-covered areas

Geochemical evolution of uraniferous soda lakes in Eastern Mongolia

Extremely high concentrations of U were discovered in hypersaline soda lakes in eastern Mongolia. The lakes are small, shallow (<1km², <1m), and terminal with variable salinity. The origin and fate of U in these lakes was investigated using geochemical analyses and modeling, using samples collected from lakes and lake pore waters, wells and a stream. Samples were analyzed for Sr and U isotopes, cations, trace metals, anions, total inorganic carbon (TIC), and unstable field parameters. A representative groundwater in the field area is dilute and alkaline, with pH=7.9, 10 mmol L⁻¹ of TIC and 5 mmol L⁻¹ Cl⁻. In contrast, a representative lake water is pH(similar to)10 with TIC and Cl⁻ each more than 1000 mmol L⁻¹. Uranium concentrations in lake waters range from 0.24 to more than 62.5 (Greek small letter mu)mol L⁻¹, possibly making these lakes the highest naturally occurring U concentrations ever reported in a natural water. Groundwater concentrations of U range from 0.03 and 0.43 (Greek small letter mu)mol L⁻¹. The U is natural and derived from groundwater discharging to stable closed basin lakes. Waters are concentrated by evaporation and U(VI) is chelated by CO₃⁻² to form the highly soluble UO₂(CO₃)₃⁻⁴. Two sets of well waters with corresponding lake discharge waters were analyzed for U isotopes. Unnatural concentrations of ²³⁵U were tested for, the presence of which would indicate fallout-derived U. The average of four samples was ²³⁸U/²³⁵U=136(plus or minus)2 indicating that the U is naturally derived. (Greek small letter delta)²³⁴U between one well and its corresponding discharge lake had similar (Greek small letter delta)²³⁴U values ([Greek small letter delta]²³⁴U=837.6- 858.5). The other sample pair however, revealed significant differences between the well and its discharge lake ([Greek small letter delta] ²³⁴U=303.6-1530). This suggests nuclide recoil is significantly enriching one of the lakes with ²³⁴U beyond secular equilibrium during alpha-decay of ²³⁸U in lake sediments or along the groundwater flow path. Modeled evaporation of lakes demonstrates that a U-mineral phase is likely to precipitate during evaporation. Strontium isotopes varied in groundwaters between ⁸⁷Sr/⁸⁶Sr= 0.706192-0.709776 and in lakes ⁸⁷Sr/⁸⁶Sr=0.708702-0.709432. Cretaceous mafic rocks likely account for low ⁸⁷Sr/⁸⁶Sr values while Cretaceous alkaline rhyolites account for the high ⁸⁷Sr/⁸⁶Sr values. High concentrations of U, Na, Cl⁻, and K correlate to radiogenic Sr in lake waters indicating that U is sourced from local Cretaceous alkaline rhyolites.Geological Science

Development of an Integrated Soil Properties Mapping System

One of the main goals of precision agriculture (PA) is to define spatial variability in soil properties within an agricultural field to make decisions that can maximize profitability and reduce negative environmental impact. Various soil sensor systems have been developed over the years to map soil properties on-the-go. In this study, an Integrated Soil Mapping System (ISMS) was developed to predict soil water content, soil organic matter, and soil mechanical resistance on-the-go using a capacitance moisture sensor, an optical sensor, and a load cell sensor respectively. These sensors were mounted on the ISMS for acquiring three different data layers at the same time. Each sensor was calibrated under laboratory conditions and the ISMS was also tested in fields. For example, volumetric soil water content estimated from the two-sided capacitance moisture sensor was compared with volumetric water content measured by the oven-drying method which produced R2 = 0.94 in laboratory conditions with a standard error of 0.017 cm3/cm3. Soil index calculated as the sum of individual soil reflectance measurements by the optical sensor in red (660 nm) and blue (480 nm) parts of the spectrum predicted soil organic matter with R2 = 0.73 and with standard error of 0.47 OM%. The load cell sensor was tested by applying different loads on the hitch for simulating field conditions and measuring value of known weights with an R squared = 0.99 and standard error of 0.032 kN. Then ISMS was tested in field conditions for mapping the three data layers simultaneously. High sampling density data collected by the ISMS was compared with data collected at the same time using conventional laboratory methods by developing the maps of soil properties and R2 of 0.74, 0.67 and 0.28 were obtained for the moisture, optical and load cell sensors, respectively when regressed with standard laboratory methods

Morphological characterization of ponds and tidal courses in coastal wetlands using Google Earth imagery

Ponds and tidal courses are significant landforms that frequently arise in marshes and tidal flats environments. An understanding of their development and permanence is relevant to determine future dynamic processes that alter tidal flats and salt marshes environments, such as changes in the sea level, increase in the wave activity, and some other variations associated to the climate change. Direct access for monitoring in these regions is complex, extremely expensive and not always feasible. Remote sensing imagery represents a monitoring alternative, but requires the research of specific image processing procedures to extract the information concerning to these environmental studies. In this work, we developed a methodology for assessing the relevant morphological parameters of ponds and tidal courses using Google Earth imagery. An automatic classifier identifies these landforms as such (accuracy over 86%), producing a shape descriptors dataset. Then, ponds and tidal courses in tidal flats are morphologically characterized, and their behavior is compared to the surrounding environment. Subsequent analysis found significant differences in morphological characteristics that arise independently of the marsh environmental conditions. The evidence suggests that the evolution processes of the depressions in salt flat environments are clearly different in comparison with salt marshes environments. In salt marshes, the permanence and evolution of the depressions is related to the age of marshes, whereas in tidal flats the dynamic processes and sediment input have influence on depressions evolution.Fil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; Argentin