Andreas J. Dietz

Remote sensing of snow – a review of available methods

The use of satellite remote sensing for the mapping of snow-cover characteristics has a long-lasting history reaching back until the 1960s. Because snow cover plays an important role in the Earths climate system, it is necessary to map snow-cover extent and snow mass in both high temporal and high spatial resolutions. This task can only be achieved by the use of remotely sensed data. Many different sensors have been used in the past decades with various algorithms and respective accuracies. This article provides an overview of the most common methods. The limitations, advantages and drawbacks will be illustrated while error sources and strategies on how to ease their impact will be reviewed. Beginning with a short summary of the physical and spectral properties of snow, methods to map snow extent from the reflective part of the spectrum, algorithms to estimate snow water equivalent (SWE) from passive microwave (PM) data and the combination of both spectra will be delineated. At the end, the reader should have an overarching overview of what is currently possible and the difficulties that can occur in the context of snow-cover mapping from the reflective and microwave parts of the spectrum.

Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data

In this study medium resolution remote sensing data of the AVHRR and MODIS sensors were used for derivation of inland water bodies extents over a period from 1986 till 2012 for the region of Central Asia. Daily near-infrared (NIR) spectra from the AVHRR sensor with 1.1 km spatial resolution and 8-day NIR composites from the MODIS sensor with 250 m spatial resolution for the months April, July and September were used as input data. The methodological approach uses temporal dynamic thresholds for individual data sets, which allows detection of water pixel independent from differing conditions or sensor differences. The individual results are summed up and combined to monthly composites of areal extent of water bodies. The presented water masks for the months April, July, and September were chosen to detect seasonal patterns as well as inter-annual dynamics and show diverse behaviour of static, decreasing, or dynamic water bodies in the study region. The size of the Southern Aral Sea, as the most popular example for an ecologic catastrophe, is decreasing significantly throughout all seasons (R2 0.96 for April; 0.97 for July; 0.96 for September). Same is true for shallow natural lakes in the northern Kazakhstan, exemplary the Tengiz-Korgalzhyn lake system, which have been shrinking in the last two decades due to drier conditions (R2 0.91 for July; 0.90 for September). On the contrary, water reservoirs show high seasonality and are very dynamic within one year in their areal extent with maximum before growing season and minimum after growing season. Furthermore, there are water bodies such as Alakol-Sasykol lake system and natural mountainous lakes which have been stable in their areal extent throughout the entire time period. Validation was performed based on several Landsat images with 30 m resolution and reveals an overall accuracy of 83% for AVHRR and 91% for MODIS monthly water masks. The results should assist for climatological and ecological studies, land and water management, and as input data for different modelling applications.

European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products

Mean snow cover duration was derived for the entire continent of Europe based on the MODIS daily snow cover products MOD10A1 and MYD10A1 for the period from 2000 to 2011. Dates of snow cover start and snow cover melt were also estimated. Polar darkness north of ~62°N and extensive cloud coverage affected the daily snow cover, preventing a direct derivation of the desired parameters. Combining sensor data from both MODIS platforms and applying a temporal cloud filter, cloud coverage and polar darkness were removed from the input data and accuracy remained above 90% for 87% of the area. The typical snow cover characteristics of the whole continent are illustrated and constitute a unique dataset with respect to spatial and temporal resolution. Abnormal events, glacier inventories or studies on possible impacts of climate change on snow cover characteristics are only some examples for applications where the presented results may be utilized.

Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables

Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km2 large study area in western Burkina Faso and southern Mali (West Africa). A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class “Wetland”) is 9,350 km2, corresponding to 4.3% of the total study area size. The classes “No wetland”/“Wetland” could be separated with very high certainty; the overall agreement (KHAT) was 84.2% (0.67) and 97.9% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments.

Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products

In this study, the daily snow-cover time series has been analysed for the whole of central Asia after cloud coverage was removed. Snow-cover duration (SCD), snow-cover start (SCS), and snow-cover melt (SCM) have been derived for each hydrological year from 2000/2001 to 2010/2011 and mean conditions were extracted that identify a distinct north–south gradient of these parameters. The snow-cover index (SCI), which depicts a moderate variability with maximum deviations of ∼20%, has been included for major hydrological catchments. The hydrological year 2001/2002 stands out due to minimum SCD caused by late SCS and early SCM while 2002/2003 constitutes maximum SCD initiated by late SCM. Although the time series of 11 years of data is too short to calculate possible trends of snow-cover characteristics, the results can be used to describe the average snow-cover conditions and compare single years against these values. Large divergences can indicate deficits or excesses of snow, which may lead to abnormal run-off situations, including natural disasters such as floods, landslides, or droughts. The latter, especially, can have severe negative economic impacts in a region.

Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis

The understanding and assessment of surface water variability of inland water bodies, for example, due to climate variability and human impact, requires steady and continuous information about its inter- and intra-annual dynamics. In this letter, we present an approach using dynamic threshold techniques and utilizing time series to generate a data set containing detected surface water bodies on a global scale with daily temporal resolution. Exemplary results for the year 2013 that were based on moderate resolution imaging spectroradiometer products are presented in this letter. The main input data sets for the presented product were MOD09GQ/MYD09GQ and MOD10A1/MYD10A1 with a spatial resolution of 250 m and 500 m, respectively. Using the static water mask MOD44W, we extracted training pixels to generate dynamic thresholds for individual data sets on daily basis. In a second processing step, the generated sequences of water masks were utilized to interpolate the results for any missing observations, either due to cloud coverage or missing data, as well as to reduce misclassification due to cloud shadow. The product provides an opportunity for further research and for assessing the drivers of changes of inland water bodies at a global scale.

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

Currently there is a lack of quantitative information regarding the driving factors of vegetation dynamics in post-Soviet Central Asia. Insufficient knowledge also exists concerning vegetation variability across sub-humid to arid climatic gradients as well as vegetation response to different land uses, from natural rangelands to intensively irrigated croplands. In this study, we analyzed the environmental drivers of vegetation dynamics in five Central Asian countries by coupling key vegetation parameter “overall greenness” derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI time series data, with its possible factors across various management and climatic gradients. We developed nine generalized least-squares random effect (GLS-RE) models to analyze the relative impact of environmental factors on vegetation dynamics. The obtained results quantitatively indicated the extensive control of climatic factors on managed and unmanaged vegetation cover across Central Asia. The most diverse vegetation dynamics response to climatic variables was observed for “intensively managed irrigated croplands”. Almost no differences in response to these variables were detected for managed non-irrigated vegetation and unmanaged (natural) vegetation across all countries. Natural vegetation and rainfed non-irrigated crop dynamics were principally associated with temperature and precipitation parameters. Variables related to temperature had the greatest relative effect on irrigated croplands and on vegetation cover within the mountainous zone. Further research should focus on incorporating the socio-economic factors discussed here in a similar analysis.

Winter color polymorphisms identify global hot spots for evolutionary rescue from climate change

Changing coats with the season Many species of mammals and birds molt from summer brown to winter white coats to facilitate camouflage. Mills et al. mapped global patterns of seasonal coat color change across eight species including hares, weasels, and foxes. They found regions where individuals molt to white, brown, and both white and brown winter coats. Greater proportions of the populations molted to white in higher latitudes. Regions where seasonal coat changes are the most variable (molting to both brown and white) may provide resilience against the warming climate. Science, this issue p. 1033 Variation in winter coat color may protect species with winter camouflage—such as hares, weasels, and foxes—when the climate warms. Maintenance of biodiversity in a rapidly changing climate will depend on the efficacy of evolutionary rescue, whereby population declines due to abrupt environmental change are reversed by shifts in genetically driven adaptive traits. However, a lack of traits known to be under direct selection by anthropogenic climate change has limited the incorporation of evolutionary processes into global conservation efforts. In 21 vertebrate species, some individuals undergo a seasonal color molt from summer brown to winter white as camouflage against snow, whereas other individuals remain brown. Seasonal snow duration is decreasing globally, and fitness is lower for winter white animals on snowless backgrounds. Based on 2713 georeferenced samples of known winter coat color—from eight species across trophic levels—we identify environmentally driven clinal gradients in winter coat color, including polymorphic zones where winter brown and white morphs co-occur. These polymorphic zones, underrepresented by existing global protected area networks, indicate hot spots for evolutionary rescue in a changing climate.

Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent

With the Global SnowPack, we present a set of global snow cover parameters – for the first time in medium resolution for the full globe and without the compromising effects of cloud coverage or polar darkness. Over 1.2 million single data sets were processed to prepare the Global SnowPack between September 2000 and 2015 – with around 246 more being added every day. Snow cover duration (SCD), early and late season SCD, and statistical products are the main components of the Global SnowPack which can be used to analyse shifts and trends of global snow cover characteristics as well as extreme events. The 500 m resolution allows for applications on a subcatchment scale. One example for a possible application is included, focusing on a detailed view of the California and Volga Basin snow cover characteristics. The Global SnowPack reveals areas with extremely low SCD in 2013/2014 and 2014/2015 which is one reason for the severe droughts in California.