Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value)

Appendix for Master's thesis: LAI development, Reflectance Curves

Appendix for Master's thesis. Additional graphs for the ones exemplary printed in the thesi

Hydrology under change: long-term annual and seasonal changes in small agricultural catchments in Norway

Abstract In agricultural catchments, hydrological processes are highly linked to particle and nutrient loss and can lead to a degradation of the ecological status of the water. Global warming and land use changes influence the hydrological regime. This effect is especially strong in cold regions. In this study, we used long-term hydrological monitoring data (22–26 years) from small agricultural catchments in Norway. We applied a Mann–Kendall trend and wavelet coherence analysis to detect annual and seasonal changes and to evaluate the coupling between runoff, climate, and water sources. The trend analysis showed a significant increase in the annual and seasonal mean air temperature. In all sites, hydrological changes were more difficult to detect. Discharge increased in autumn and winter, but this trend did not hold for all catchments. We found a strong coherence between discharge and precipitation, between discharge and snow water equivalent and discharge and soil water storage capacity. We detected different hydrological regimes of rain and snow-dominated catchments. The catchments responded differently to changes due to their location and inherent characteristics. Our results highlight the importance of studying local annual and seasonal changes in hydrological regimes to understand the effect of climate and the importance for site-specific management plans

Sediment transport dynamics in small agricultural catchments in a cold climate:a case study from Norway

Abstract Increased nutrient and soil losses from agricultural areas into water bodies constitute a global problem. Phosphorus is one of the main nutrients causing eutrophication in surface waters. In arable land, phosphorus losses are closely linked to sediment losses. Therefore, a better understanding of the sediment-runoff processes in agricultural areas is a key to reduce the eutrophication impacts and to implement mitigation measures. The objectives of this study were to identify dominant sediment runoff processes in cultivated grain-dominated catchments in a cold climate. We assessed continuous high-resolution turbidity data, temporal and spatial catchment properties and agricultural management data to describe and get a better understanding of the cause-relationship of sediment transfer in two small agricultural dominated catchments in southern Norway. The concentration-discharge pattern, index of connectivity and agricultural activities were considered with the wider aim to establish a link between field and catchment scale. The results showed that the dominant concentration-discharge pattern was a clockwise concentration-discharge (c-q) hysteresis in both catchments indicating that areas close to or in the stream gave the highest contribution to turbidity. The main driver for turbidity was discharge, though soil water storage capacity, rain intensity and former discharge events also played a role. Intensity of soil tillage and index of connectivity (likelihood of water and particles to be transported to the stream) impacted the c-q hysteresis index. Little vegetation cover and high intensity of soil tillage led to a high hysteresis index, which indicates a quick increase in turbidity following increased discharge. Other links between agricultural management and in stream data were difficult to interpret. The findings of this study provide information about discharge, field operations and vegetational status as drivers for turbidity and about the spatial distribution of sediment sources in two agricultural catchments in a cold climate. The understanding of sediment runoff processes is important, when implementing management actions to combat agricultural emissions to water most efficiently

Comparing in situ turbidity sensor measurements as a proxy for suspended sediments in North-Western European streams

Abstract Climate change in combination with land use alterations may lead to significant changes in soil erosion and sediment fluxes in streams. Optical turbidity sensors can monitor with high frequency and can be used as a proxy for suspended sediment concentration (SSC) provided there is an acceptable calibration curve for turbidity measured by sensors and SSC from water samples. This study used such calibration data from 31 streams in 11 different research projects or monitoring programmes in six Northern European countries. The aim was to find patterns in the turbidity-SSC correlations based on stream characteristics such as mean and maximum turbidity and SSC, catchment area, land use, hydrology, soil type, topography, and the number and representativeness of the data that are used for the calibration. There were large variations, but the best correlations between turbidity and SSC were found in streams with a mean and maximum SSC of >30–200 mg/l, and a mean and maximum turbidity above 60–200 NTU/FNU, respectively. Streams draining agricultural areas with fine-grained soils had better correlations than forested streams draining more coarse-grained soils. However, the study also revealed considerable differences in methodological approaches, including analytical methods to determine SSC, water sampling strategies, quality control procedures, and the use of sensors based on different measuring principles. Relatively few national monitoring programmes in the six countries involved in the study included optical turbidity sensors, which may partly explain this lack of methodological harmonisation. Given the risk of future changes in soil erosion and sediment fluxes, increased harmonisation is highly recommended, so that turbidity data from optical sensors can be better evaluated and intercalibrated across streams in comparable geographical regions