Stefan Wunderle

Operational sub-pixel snow mapping over the Alps with NOAA AVHRR data

Abstract This study is part of research activities concentrating on the real-time application of the U.S. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor for snow-cover analysis of the European Alps. For mapping snow cover in heterogeneous terrain, we implement the widely used linear spectral mixture algorithm to estimate snow cover at sub-pixel scale. Principal component analysis, including the reflective part of AVHRR channel 3, is used to estimate fractions of “snow” and “not snow” within a pixel, using linear mixture modeling. The combination of these features leads to a fast, simple solution for operational and near-real-time processing. The presented algorithm is applied on the European Alps on 17 January 2003 and successfully maps snow at sub-pixel scale. The detailed snow-cover information makes it easy to recognize the complex topography of the Alps, more so than with either a classic binary map or a Moderate Resolution Imaging Spectroradiometer (MODIS) snow product. The sub-pixel algorithm reasonably identifies snow-cover fractions in regions and at altitudes where neither the classic binary map nor the MODIS algorithm detects any snow. Differences concerning the snow distribution are found in forested areas as well as in the lowest-elevation zones. The algorithm substantially improves snow mapping over complex topography for operational and near-realtime applications.

Validation of operational AVHRR subpixel snow retrievals over the European Alps based on ASTER data

Snow is of great economic and social importance for the European Alps. Accurate monitoring of the alpine snow cover is a key component in studying regional climate change as well as in daily weather forecasting and snowmelt run‐off modelling. These applications require snow cover information on a high temporal resolution in near‐real time. For the European Alps, operational snow cover fraction maps are generated on a daily basis using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) platforms. Snow cover distribution is inherently discontinuous and heterogeneous in this mountainous region. We have therefore implemented a straightforward multiple endmember unmixing approach to estimate fractional snow cover. Subpixel proportions are difficult to validate because similar products are not available and appropriate ground‐based observations do not exist. In this study, we validate AVHRR subpixel snow retrievals using binary classified data sets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to establish absolute errors of our operational approach at three test sites. Our analysis indicates that the AVHRR subpixel maps compare well with the aggregated ASTER data, showing an overall correlation of 0.78 and providing subpixel estimates with a mean absolute error of 10.4% fractional snow cover. Discrepancies between AVHRR and ASTER snow fraction maps can be attributed to varying snow conditions, terrain effects and density in forest cover.

Hyperspectral imaging spectroscopy: a promising method for the biogeochemical analysis of lake sediments

Abstract. We investigate the potential of hyperspectral imaging spectrometry for the analysis of fresh sediment cores. A sediment-core-scanning system equipped with a camera working in the visual to near-infrared range (400 to 1000 nm) is described and a general methodology for processing and calibrating spectral data from sediments is proposed. We present an application from organic sediments of Lake Jaczno, a freshwater lake with biochemical varves in northern Poland. The sedimentary pigment bacteriopheophytin a (BPhe a) is diagnostic for anoxia in lakes and, therefore, an important ecological indicator. Calibration of the spectral data (BPhe a absorption ∼800 to 900 nm) to absolute BPhe a concentrations, as measured by high-performance-liquid-chromatography, reveals that sedimentary BPhe a concentrations can be estimated from spectral data with a model uncertainty of ∼10%. Based on this calibration model, we use the hyperspectral data from the sediment core to produce high-resolution intensity maps and time series of relative BPhe a concentrations (∼10 to 20 data points per year, pixel resolution 70×70  μm2). We conclude that hyperspectral imaging is a very cost- and time-efficient method for the analysis of lake sediments and provides insight into the spatiotemporal structures of biogeochemical species at a degree of detail that is not possible with wet chemical analyses.

Daytime Low Stratiform Cloud Detection on AVHRR Imagery

The near-real time retrieval of low stratiform cloud (LSC) coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR) data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV). Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS) cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe.

Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations

Perennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree-days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = −0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS-derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC-2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment.

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI), has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%), a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000–2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure.

Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data

Although lake surface water temperature (LSWT) is defined as an essential climate variable (ECV) within the global climate observing system (GCOS), current satellite-based retrieval techniques do not fulfill the GCOS accuracy requirements. The split-window (SW) retrieval method is well-established, and the split-window coefficients (SWC) are the key elements of its accuracy. Performances of SW depends on the degree of SWC customization with respect to its application, where accuracy increases when SWC is tailored for specific situations. In the literature, different SWC customization approaches have been investigated, however, no direct comparisons have been conducted among them. This paper presents the results of a sensitivity analysis to address this gap. We show that the performance of SWC is most sensitive to customizations for specific time-windows (Sensitivity Index SI of 0.85) or spatial extents (SI 0.27). Surprisingly, the study highlights that the use of separated SWC for daytime and night-time situations has limited impact (SI 0.10). The final validation with AVHRR satellite data showed that the subtle differences among different SWC customizations were not traceable to the final uncertainty of the LSWT product. Nevertheless, this study provides a basis to critically evaluate current assumptions regarding SWC generation by directly comparing the performance of multiple customization approaches for the first time.

Retrieval of aerosol optical depth (AOD) using NOAA AVHRR data in an alpine environment

The aim of this study is the retrieval of aerosol optical depth from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor over land. The region of interest covers central Europe ranging from 50°N to 40.5°N and from 0°E to 17°E including the European Alps. On the temporal scale, we limit the data set to afternoon NOAA-16 passes of the entire year 2002. In this region, there are sixteen stations from the Aerosol Robotic Network (AERONET) at which we can compare the ground based versus the space borne measurements. The most crucial parameter in the retrieval procedure is the estimate of a correct surface reflectance since inaccuracies of 0.01 can result in AOD variations of ±0.1. Surface reflectance has been estimated by extracting the minimum reflectance within 10° intervals of the satellite zenith angle within two-month intervals. This method eliminates the varying reflectance with varying satellite zenith angle but the extracted surface reflectance still contains an aerosol signal. Most stations show a clear relationship between the AVHRR and the AERONET data. In case of a weak or non-existing relationship, we were able to identify reasons for this behavior. The standard error of estimate is about 0.18. The largest potential for increasing the accuracy of this product posses an improvement of the cloud mask. We can conclude that aerosol retrieval over land using AVHRR is a challenging task but it is possible to extract some valuable results.

Operational snow cover estimation at subpixel scale using NOAA-AVHRR data

Snow and ice play an important role in the earth`s radiation balance because of the high albedo in comparison to other natural surfaces. Furthermore ice and snow is the largest contributor to rivers and ground water over major parts of the middle and high altitudes. These are reasons why hydrological and climatological studies require estimates of snow covered areas. Most of such snow cover maps generated from satellite data include information of snow or not snow for each image pixel. In this study a linear spectral unmixing algorithm is used to calculate snow cover portions within each data cell. We examine the ability of this algorithm for operational and near-real time snow cover estimation at subpixel scale using medium spatial resolution satellite data from NOAA-AVHRR. The automated methodology is presented which produces snow cover fraction maps showing plausible distribution of snow in comparison to TERRA-ASTER data. The qualitative analysis of the results present how suitable the approach implemented in the preliminary processing chain is. Simplifying assumptions are made to the procedure which explains some variation between derived snow cover fraction map and reference data. Further work should include an accurate quantification of areal snow coverage comparison to traditional approaches.

Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes

Lake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake’s response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate change and lake dynamics. To derive such a dataset, challenges such as orbit drift correction, non-water pixel detection, and homogenization had to be solved. The result is a dataset covering over 3.5 decades of spatial LSWT data for 26 European lakes. The validation against in-situ temperature data at 19 locations showed an uncertainty between ±0.8 K and ±2.0 K (standard deviation), depending on locations of the lakes. The long-term robustness of the dataset was confirmed by comparing in-situ and satellite derived temperature trends, which showed no significant difference. The final trend analysis showed significant LSWT warming trends at all locations (0.2 K/decade to 0.8 K/decade). A gradient of increasing trends from south-west to north-east of Europe was revealed. The strong intra-annual variability of trends indicates that single seasonal trends do not well represent the response of a lake to climate change, e.g., autumn trends are dominant in the north of Europe, whereas winter trends are dominant in the south. Intra-lake variability of trends indicates that trends at single in-situ stations do not necessarily represent the lake’s response. The LSWT dataset generated for this study gives some new and interesting insights into the response of European lakes to climate change during the last 36 years (1981–2016).