Matias Takala

Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to 2007

Determining the date of snowmelt clearance is an important issue for hydrological and climate research. Spaceborne radiometers are ideally suited for global snowmelt monitoring. In this paper, four different algorithms are used to determine the snowmelt date from Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager data for a nearly 30-year period. Algorithms are based on thresholding channel differences, on applying neural networks, and on time series analysis. The results are compared with ground-based observations of snow depth and snowmelt status available through the Russian INTAS-SSCONE observation database. Analysis based on Moderate Resolution Imaging Spectroradiometer data indicates that these pointwise observations are applicable as reference data. The obtained error estimates indicate that the algorithm based on time series analysis has the highest performance. Using this algorithm, a time series of the snowmelt from 1979 to 2007 is calculated for the whole Eurasia showing a trend of an earlier snow clearance. The trend is statistically significant. The results agree with earlier research. The novelty here is the demonstration and validation of estimates for a large continental scale (for areas dominated by boreal forests) using extensive reference data sets.

Monitoring of Snow-Cover Properties During the Spring Melting Period in Forested Areas

As space-borne C-band SAR observations are used for monitoring the snow cover during the spring melt period, temporal changes m backscatter from forest cover disturb the mapping of snow cover. This paper presents an analysis of snow backscattering properties in eight test areas situated around weather stations. Test areas represent open and forested landscapes in Northern Finland. Analyses are carried out using an extensive multitemporal ERS-2 C-band SAR data set from the snow melt period. We validate the 1) forest backscattering model for forest compensation, 2) TKK fractional snow-covered area (SCA) method with m situ observations and 3) inversion of a combined forest/snow/ground backscattering model in an application to yield estimates of the relative changes of snow wetness during full snow cover conditions. The results show that the semi-empirical TKK backscattering model describes the average C-band backscattering properties of all test regions well as a function of forest stem volume. Comparison of SCA estimation results with available ground truth data also shows a good performance. The retrieved relative snow wetness values agree well with temperature observations.

Investigating the feasibility of the globsnow snow water equivalent data for climate research purposes

This paper presents the efforts for creating two global scale snow dataset covering 15 and 30 years of satellite-based observations, one describing the extent of snow cover (SE) the other describing the snow water equivalent (SWE) characteristics. The main emphasis of the paper is describing the validation work carried out for the SWE product that will cover the non-mountainous regions of Northern Hemisphere on a daily basis starting from 1979. The work has been carried out within the ESA Globsnow project.

New Snow Water Equivalent Processing System With Improved Resolution Over Europe and its Applications in Hydrology

The presence and amount of snow, given in terms of snow water equivalent (SWE), is an essential physical characteristic influencing climate and hydrological processes. For the recent decades, remote sensing has proven to be a valuable tool for deriving regional and global scale information on SWE. However, determining SWE reliably from remote sensing data for many local-scale applications remains a challenge. Microwave radiometers are currently the best option to determine SWE since they respond to snow depth and density. Further, weather phenomena and solar illumination are not of concern. However, for some purposes the typical spatial resolution of space-borne radiometers (in the order of tens of kilometers) is not sufficient. In this study, the spatial resolution of existing operational SWE products (GlobSnow and H-SAF product portfolios) is improved by performing assimilation of ground truth observations of snow depth and space borne derived SWE estimates in a resolution grid of 0.05° × 0.05° (approximately 5 km × 5 km). Some modifications to the SWE algorithm and the applied auxiliary data (such as an improved forest stem volume map) are introduced. We will present how the improved resolution enhances spatial details in the retrieved SWE, while the validation results show that in terms of accuracy, the new product is on similar level than the existing operational products. Finally, the gained new SWE estimates are ingested into the HOPS hydrological model in the Ounasjoki river basin. The results indicate that simulation of snow melt driven river discharge can be improved by ingesting the retrieved SWE data into a hydrological model.

Investigating hemispherical trends in snow accumulation using GlobSnow snow water equivalent data

This paper presents the evaluation of the 30-years GlobSnow SWE data record, spanning Northern Hemisphere, for climate research purposes. It includes a brief validation of the SWE data record with ground-based reference data and evaluation of the hemispherical scale SWE trends.

Implementing hemispherical snow water equivalent product assimilating weather station observations and spaceborne microwave data

Snow water equivalent (SWE) is one of the key parameters describing seasonal snow cover. Traditional methods such as interpolating ground-based measurements or estimating SWE from spaceborne measurements have their shortcomings. In this paper an assimilation approach has been used to estimate a time series of SWE in hemispherical scale for 30 years. The behaviour of the algorithm is analyzed and scatterplot of validation results is presented. Results show an improvement over using traditional algorithms.

Radiometric performance of interferometric synthetic aperture radiometer HUT-2D

This paper describes a test campaign carried out with synthetic aperture radiometer HUT-2D to establish understanding to the instruments radiometric performance. The test campaign consists of measurements of the radiation from the sky, which is considered as a well-known target, and measurements of a pure sea water scene, which brightness temperature is possible to model. Results of the tests are used to address instruments radiometric sensitivity and radiometric resolution. The experimental results are discussed and compared to the theoretical values, where applicable.

Estimating the snow melt onset using AMSR-E data in Eurasia

Knowing the onset of snow melt is an important factor in climatological and weather forecasting models. The carbon cycle in the atmosphere is directly related to melting of snow and thus is a key information understanding global climate change. Microwave radiometers are sensitive to liquid water and thus well suited for melt detection. The rather coarse resolution is ideal for monitoring the snow melt globally. However, many snow melt detection algorithms are applicable only on arctic tundra or snow covered glaciers. The authors of this paper have earlier developed melt detection algorithms for boreal forest zone using SSM/I-data. In this paper AMSR-E data is used and the algorithm is slightly modified to operate without using additional data such as ground based measurements. The algorithm is applied over the whole Northern Eurasia and the results obtained are reliable and valuable for further development.

Validation of microwave emission models by simulating AMSR-E brightness temperature data from ground-based observations

For several applications, spaceborne microwave measurements are used to get large scale information of snow- covered terrain. Emission models for soil, vegetation and snow are needed in extraction of snow parameters from satellite measurements. In this paper space-observed brightness temperature of snow-covered terrain is simulated from in situ measurements using HUT snow model, rough bare soil reflectivity model and boreal forest emission model. The results are compared with AMSR-E data. Correlations of time series between simulated and measured brightness temperatures were best on the highest frequencies being better than 0.7 on frequencies above 18 GHz.

Early snowmelt significantly enhances boreal springtime carbon uptake

Significance We quantified a 36-y trend of advanced spring recovery of carbon uptake across the northern hemisphere boreal evergreen forest zone. From this trend, we estimated the corresponding change in global gross primary production (GPP) and further quantified the magnitude and spatiotemporal variability of spring GPP, that is, the cross-photosynthetic carbon uptake by forest. Our main findings are the following: (i) We developed a proxy indicator for spring recovery from in situ flux data on CO2 exchange and recent satellite snowmelt products and (ii) we established a relation between spring recovery and carbon uptake to assess changes in springtime carbon exchange showing a major advance in the CO2 sink. We determine the annual timing of spring recovery from space-borne microwave radiometer observations across northern hemisphere boreal evergreen forests for 1979–2014. We find a trend of advanced spring recovery of carbon uptake for this period, with a total average shift of 8.1 d (2.3 d/decade). We use this trend to estimate the corresponding changes in gross primary production (GPP) by applying in situ carbon flux observations. Micrometeorological CO2 measurements at four sites in northern Europe and North America indicate that such an advance in spring recovery would have increased the January–June GPP sum by 29 g⋅C⋅m−2 [8.4 g⋅C⋅m−2 (3.7%)/decade]. We find this sensitivity of the measured springtime GPP to the spring recovery to be in accordance with the corresponding sensitivity derived from simulations with a land ecosystem model coupled to a global circulation model. The model-predicted increase in springtime cumulative GPP was 0.035 Pg/decade [15.5 g⋅C⋅m−2 (6.8%)/decade] for Eurasian forests and 0.017 Pg/decade for forests in North America [9.8 g⋅C⋅m−2 (4.4%)/decade]. This change in the springtime sum of GPP related to the timing of spring snowmelt is quantified here for boreal evergreen forests.