Bojan Bojkov

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.

A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data

The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series.

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.

Assessing global satellite-based snow water equivalent datasets in ESA SnowPEx project

There is a significant difference in SWE retrieval performance between the different satellite-based products. The assessment using the Russian and Finnish snow transect data covers an extremely large and varied geographical region and spans a total of ten years (2002-2011). Additionally, the reference data are well suited for assessing coarse resolution data, as they are not point-wise measurements but distributed measurements from the snow transects or snow courses.

Cross-calibration of ERS-1 and ERS-2 wind scatterometers; Towards a homogeneous 20-year-long wind vector monitoring of the earth

The importance of long-term, continuous, and homogenous time-series of satellite data is widely accepted and strongly fostered by the international scientific community. The various global projects and initiatives undertaken in the last few years are evidences of that effort. Among those are: the Long Term Data Preservation Working Group [1], the Permanent Access to the Records of Science in Europe (PARSE) [2], or the Global Climate Observing System (GCOS) [3]. One of the examples of long-term monitored variable is the wind vector. Since the European Remote-sensing Satellite (ERS)-1 launch in July 1991 and until ERS-2 decommissioning in July 2011, a continuous and consistent database of backscattering signal from the Earth surface has been built, and is now available. The Active Microwave Instrument (AMI) [4], which was one of the ERS-1 and ERS-2 payloads, provided radar backscattering coefficient measurements during the last 20 years by using its three nominal operational acquisition modes: Synthetic Aperture mode (SAR mode), Scatterometer mode (wind mode) and a special combination of the two over ocean where SAR and Scatterometer mode are interleaved (wind/wave mode). The main applications for data acquired in Scatterometer mode is related to the estimation of the wind vector over the sea surface. In that field the ERS-2 Scatterometer measurements give a very valuable contribution to the accuracy of the numerical weather forecast models, being assimilated in several meteorological weather forecast centers since the beginning of the mission. After the decommissioning of ERS-2, effort has been devoted to achieve a complete reprocessed database, including both ERS-1 and ERS-2 acquisitions [5]. The cross-calibration between these two satellites is a crucial task to obtain the homogeneousness of the wind vector database, and allow its long-term characterization. The approach followed by ESA in term of team organization, cross-calibration strategy and validation methodology towards this goal is presented in this paper as well as the preliminary results of the long-term characterization of the wind vector.