Niilo Siljamo

The Satellite Application Facility for Land Surface Analysis

Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land–atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors. This article provides an overview of the Land-SAF, with brief descriptions of algorithms and validation results. The set of parameters currently estimated and disseminated by the Land-SAF consists of three main groups: (i) the surface radiation budget, including albedo, land surface temperature, and downward short- and longwave fluxes; (ii) the surface water budget (snow cover and evapotranspiration); and (iii) vegetation and wild-fire parameters.

New Geostationary Satellite-Based Snow-Cover Algorithm

AbstractSnow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface. Reliable observations of the snow cover are difficult to obtain without satellites. This paper introduces a new algorithm for satellite-based snow-cover detection that is in operational use for Meteosat in the European Organisation for the Exploitation of Meteorological Satellites Satellite Application Facility on Land Surface Analysis (LSA SAF). The new version of the product is compared with the old version and the NOAA/National Environmental Satellite, Data, and Information Service Interactive Multisensor Snow and Ice Mapping System (IMS) snow-cover product. The new version of the LSA SAF snow-cover product improves the accuracy of snow detection and is comparable to the IMS product in cloud-free conditions.

SNORTEX (Snow Reflectance Transition Experiment): Remote sensing measurement of the dynamic properties of the boreal snow-forest in support to climate and weather forecast: Report of IOP-2008

Large discrepancies are observed between snow albedo in Numerical Weather Prediction (NWP) models and from satellite observations in the case of high vegetation. Knowledge of the Bidirectional Reflectance Distribution Function (BRDF) of snow-forest system is required to solve the problem. The 3-years SNORTEX (Snow Reflectance Transition Experiment) campaign acquires from 2008 in situ measurements of snow and forest properties in support to the development of modelling tools and to validate coarse resolution satellite products (POLDER, MODIS, MERIS, METOP). The measurement scheme and some first example results are presented from the Intensive Observing Period (IOP) of 2008, which can be decomposed into airborne and ground operations. Multi-temporal BRDF at a metric resolution were acquired from OSIRIS (airPOLDER) onboard a helicopter and from ground with FigiFiGo spectrogoniometer. The same helicopter embarked a pair of UV sensors, pyranometers and a wide-optics camera. Ground component includes exhaustive snow measurements.

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

Seasonal snow surface roughness is an important parameter for remote sensing data analysis since it affects the scattering properties of the snow surface. To understand the phenomenon, snow surface roughness was measured near the town of Sodankyla, in Finnish Lapland, during winters 2009 and 2010 using a photogrammetry-based plate method. The images were automatically processed so that an approximately 1 m long horizontal profile was extracted from each image. The data set consists of 669 plate profiles from different times and canopy types. This large data set was used to study the temporal and spatial variability of seasonal snow surface roughness. The profiles were analyzed using parameters derived from the root mean square height (σ) and correlation length (L) as functions of measured length. Also, the autocorrelation functions were calculated and analyzed. The (σ) and (L) were found to be so strongly correlated (R2 ~ 0.97) that a more detailed analysis was made using only the scaling parameters derived from σ. These parameters are related to the distance dependence of the rms height. The results show that they react to different characteristics of the profiles and are therefore well able to distinguish between different types of snow. They also show a clear difference between midwinter snow and melting snow, and the effects of snowfall events and slower melting in forested areas are evident in the data.

Comparison of Snow Cover from Satellite and Numerical Weather Prediction Models in the Northern Hemisphere and Northern Europe

Abstract Snow cover has a strong effect on the surface and lower atmosphere in NWP models. Because the progress of in situ observations has stalled, satellite-based snow analyses are becoming increasingly important. Currently, there exist several products that operationally map global or continental snow cover. In this study, satellite-based snow cover analyses from NOAA, NASA, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and NWP snow analyses from the High-Resolution Limited-Area Model (HIRLAM) and ECMWF, were compared using data from January to June 2006. Because no analyses were independent and since available in situ measurements were already used in the NWP analyses, no independent ground truth was available and only the consistency between analyses could be compared. Snow analyses from NOAA, NASA, and ECMWF were similar, but the analysis from NASA was greatly hampered by clouds. HIRLAM and EUMETSAT deviated most from other analyses. Even though the anal...

Cloud statistics-based estimation of land surface albedo from AVHRR data

Cloud cover constitutes a major challenge for the surface albedo estimation using AVHRR data. The demand of pixelwise high accuracy cloud masking based on only single AVHRR images without any augmenting information is not realistic in all possible conditions of cloud fraction and cloud type on any land cover type. Another approach to tackle cloudy conditions is presented in this study. In the areas studied (Finland, Switzerland, the Netherlands) cloudy broadband albedo distributions were constructed for AVHRR data of one month and 15 km x 15 km area. The peak of the distribution, which is caused by the clear sky conditions, correlated well with the corresponding surface albedo distribution obtained from pyranometer measurements at Sodankylä, Payerne and Cabauw masts. Using the half and ¾ height points of the peak it was possible to estimate the surface albedo with quite good accuracy using a simple physically motivated formula.

Operational Snowcover Mapping using MSG/SEVIRI Data

EUMETSATs Land Surface Analysis Satellite Applications Facility (LSA SAF) has been producing daily snow cover product for two years with a baseline algorithm for the land areas covered by EUMETSATs MSG satellites SEVIRI instrument. This paper presents a new improved version of the algorithm which is currently used for operational snow cover product generation in the LSA SAF. Some examples of the product are presented and the product is compared to NOAA/NESDIS IMS snow cover product and the previous version of the LSA SAF snow cover product.

The change of seasonal snow surface roughness in Sodankylä finnish lapland during winters 2009 and 2010

This article presents some results from the snow surface roughness measurements that were made during the Snow Reflectance Transition Experiment -campaign in Sodankylä, Finnish Lapland, in 2009 and 2010. The surface roughness was measured with a photogrammetry-based method where a black plate was inserted into the snow and photographed. After field work a 2D profile was automatically extracted from the image. The profiles extracted from the photographs were analyzed using root mean square height and correlation length as function of measured length. 777 profiles were measured during the SORTEX -campaign in different locations with different canopy type, including marshland, pine, birch and mixed forest and lake ice. In addition to this some profiles were measured in February 2010. The results show a clear shift from midwinter to melting season conditions as well as the effect of fresh snow on the surface.