No abstract

To map the aquatic vegetation of Bavarian (Germany) freshwater lakes in a large-scaled and quick way, remote sensing is a helpful tool. For interpretation of the data, a spectral library of different macrophyte and sediment reflectances is under development. Therefore, multi-temporal in situ remote sensing reflectances were sampled from May to October 2011 with hyperspectral RAMSES spectroradiometers. Occurring spectral variations during the growing season could be linked to biometric and phenological data of the particulate species. Principal component analyses showed that, by applying the presented method, differentiation of the macrophytes from sediment and among each other is possible and can be improved by multi-temporal data.

Abstract:In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440) CDOM ) and absorption slope (S 300-500 ) in lakes using field sampling and optical remote sensing data for an area of 350 km 2 in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a(λ) CDOM data from 18 lakes sampled in the field to 356 lakes in the study area (model R 2 = 0.79). Values of a(440) CDOM in 356 lakes varied from 0.48 to 8.35 m −1 with a median of 1.43 m −1 . This a(λ) CDOM dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R 2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440) CDOM = 5.3 m −1 ). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440) CDOM = 3.8 m −1 ) compared to lakes located on higher terraces.

No abstract

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

As it can strongly influence the availability of light and thus primary production, coloured dissolved organic matter (CDOM) affects the function of lake ecosystems. Therefore reliable methods are required for the monitoring of CDOM concentration. A new method using downwelling irradiance was tested for applicability in four selected lakes of the Bavarian Osterseen Lake District, which consists of 19 naturally connected freshwater lakes of different trophic level. The method separates between the direct and diffuse part of the incident light in order to handle the strong variability of the underwater light field. It is implemented in the software WASI, which is capable to retrieve water constituents by inverse modeling. During field campaigns downwelling irradiance measurements using RAMSES sensors were made in different depths. Simultaneously, water samples were taken in three depths (0.5 m, 2 m and Secchi disk depth), from which the absorption coefficient of CDOM, a Y , was derived in the range from 190 to 900 nm using photometric absorption measurements. Concentration (defined as a Y at 440 nm) ranged from 0.33 to 1.55 m -1 with a mean of 0.71 m -1 ± 0.04 m -1 , the spectral slope at 440 nm from 0.0120 to 0.0184 nm -1 with a mean of 0.0145 ± 0.0008 nm -1 . These laboratory measurements from water samples were compared to CDOM concentration obtained by inverse modeling of downwelling irradiance measurements using WASI. For sensor depths lower than 1 to 1.5 m large uncertainties were observed. The measurements in 2 m depth and at Secchi disk depth yielded good correlation between water sample and WASI derived data (R 2 = 0.87) with a mean standard deviation of 0.06 m -1 for the determined CDOM concentrations. This new method is an alternative to laboratory analysis of water samples from in situ measurements of CDOM concentration.

Global snow cover forms the largest and most transient part of the cryosphere in terms of area. On the local and regional scale, small changes can have drastic effects such as floods and droughts, and on the global scale is the planetary albedo. Daily imagery of snow cover forms the basis of long-term observation and analysis, and only optical sensors offer the necessary spatial and temporal resolution to address decadal developments and the impact of climate change on snow availability. The MODIS sensors have been providing this daily information since 2000; before that, only the Advanced Very High-Resolution Radiometer (AVHRR) from the National Oceanographic and Atmospheric Administration (NOAA) was suitable. In the TIMELINE project of the German Aerospace Center, the historic AVHRR archive in HRPT (High Resolution Picture Transmission) format is processed for the European area and, among other processors, one output is the thematic product ‘snow cover’ that will be made available in 1 km resolution since 1981. The snow detection is based on the Normalized Difference Snow Index (NDSI), which enables a direct comparison with the MODIS snow product. In addition to the NDSI, ERA5 re-analysis data on the skin temperature and other level 2 TIMELINE products are included in the generation of the binary snow mask. The AVHRR orbit segments are projected from the swath projection into LAEA Europe, aggregated into daily coverages, and from this, the 10-day and monthly snow covers are finally calculated. In this publication, the snow cover algorithm is presented, as well as the results of the first validations and possible applications of the final product.