Saku Anttila

From EO data to snow covered area (SCA) end products using automated processing system

The operative fractional snow mapping system over Finland and cross-border watersheds run by Finnish Environment Institute (SYKE) is presented. The method to estimate the regional fraction of snow covered area (SCA) is applicable to various optical sensors and can be implemented to cover large regions in boreal zone. Since 2003, data provided by SCAmod have been successfully assimilated to the operational hydrological model improving the performance of run-off and river discharge forecasts provided by the model. In addition of using EO data based SCA as input for hydrological modelling, SCA information is also distributed through internet as thematic maps for other end users, such as hydropower industry and citizens. SYKEs snow mapping activities will be complemented with SAR-based SCA-procedure in

ESLab application to a boreal watershed in southern Finland: preparing for a virtual research environment of ecosystem services.

Abstract We report on preparatory work to develop a virtual laboratory for ecosystem services, ESLab, and demonstrate its pilot application in southern Finland. The themes included in the pilot are related to biodiversity conservation, climate mitigation and eutrophication mitigation. ESLab is a research environment for ecosystem services (ES), which considers ES indicators at different landscape scales: habitats, catchments and municipalities and shares the results by a service that utilizes machine readable interfaces. The study area of the pilot application is situated in the boreal region of southern Finland and covers 14 municipalities and ten catchments including forested, agricultural and nature conservation areas. We present case studies including: present carbon budgets of natural ecosystems; future carbon budgets with and without the removal of harvest residues for bioenergy production; and total phosphorus and nitrogen future loads under climate and agricultural yield and price scenarios. The ESLab allows researchers to present and share the results as visual maps, statistics and graphs. Our further aim is to provide a toolbox of easily accessible virtual services for ES researchers, to illustrate the comprehensive societal consequences of multiple decisions (e.g. concerning land use, fertilisation or harvesting) in a changing environment (climate, deposition).

Assimilating spaceborne radar and ground-based weather station data for operational snow-covered area estimation

An enhanced method for snow-covered area (SCA) estimation for boreal forest zone is presented. The method combines TKK developed spaceborne radar-based SCA estimation with ground-based weather station observations. The purpose is to improve the reliability of SCA estimates near and after the end of snow-melt season. The SCA estimates acquired with the enhanced method are compared with optical satellite data-based (MODIS) SCA data. Investigations were carried out for snow-melt seasons of 2004-2006. The results show a significant increase in accuracy when the enhanced SCA method is applied. Correlation between the radar-based and optical reference data increases from 0.919 to 0.937 and RMS-error improves from 0.151 to 0.140 when the new method is employed.

Estimation of snow water equivalent and snow depth in boreal forests by assimilating AMSR-E observations with in situ observations

The retrieval of snow depth (SD) and snow water equivalent (SWE) using a forward brightness temperature model-based assimilation technique to AMSR-E and groundbased data is discussed. The results obtained for Finland in the winter of 2004 are presented. In practise, a time series of passive microwave radiometer data is assimilated to SD estimates interpolated from weather station observations. The applied technique is based on Bayesian (statistical) inversion of an analytical brightness temperature model. The obtained results indicate that the data assimilation improves the performance of SD and SWE estimation when compared with the case of only using SD estimates interpolated from a discrete ground-based observation network. KeywordsSnow, passive microwave radiometry, hydrological processes.

Estimating vegetation phenological trends using MODIS NDVI time series

The method to extract phenological information for different land cover types is presented. Phenological features are two different start dates of growing season, date of maximum growth, end of growing season and two growing season lengths. Also, quality indicators are estimated for some phenological features. The method is based on NDVI-time series extracted from MODIS-images. The errors between extracted dates and in-situ measurements are reasonably small. For example, the residuals of the estimation of the start of Flux Growing Season are on only 2 days for broadleaf forest in one Southern Finland hydrological drainage basin. The method has been tested on Northern Boreal forest zone, where there are freezing temperatures and snow during winter.

Operational snow monitoring using satellite observations

Finnish meteorological institute (FMI) has initiated together with Finnish Environment Institute (SYKE) and Helsinki University of Technology (TKK) the development of operational European wide snow monitoring system that will employ satellite data, models and in situ observations. This will be developed in framework of two international projects: (1) HydroSAF supported by Eumetsat and (2) Polarview GMES service element sponsored by ESA. The goal is to provide following snow services operationally: (1) Snow recognition (SR), (2) Fractional snow covered area (SCA), (3) Snow cover status (ST) and (4) Snow water equivalence (SWE). The first two products are designed to cover Europe and the last two will cover also the Northern Eurasia.

Mapping of Snow Water Equivalent and Snow Coverage from Combined EO and in situ Data for Climatic Studies and Hydrological Forecasting Models

Information on physical snow cover characteristics, such as snow water equivalent (SWE) and the areal coverage fraction of snow covered area (SCA), can be obtained from space-borne remote sensing data. The feasible instruments include optical spectrometers and microwave radars (SCA mapping), and microwave radiometers (SWE mapping). As data assimilation techniques are applied, the EO data-derived information can improve the performance of river discharge forecasting models and the knowledge on snow climatology. The results discussed here indicate that the assimilation of EO data-based SCA estimates to hydrological modeling significantly improves the accuracy of operational river discharge forecasts. The results also indicate that the employment of space-borne microwave radiometer data using the data assimilation technique improves the SWE or snow depth mapping accuracy when compared with the use of values interpolated from synoptic observations.

A novel earth observation based ecological indicator for cyanobacterial blooms

Abstract Cyanobacteria form spectacular mass occurrences almost annually in the Baltic Sea. These harmful algal blooms are the most visible consequences of marine eutrophication, driven by a surplus of nutrients from anthropogenic sources and internal processes of the ecosystem. We present a novel Cyanobacterial Bloom Indicator (CyaBI) targeted for the ecosystem assessment of eutrophication in marine areas. The method measures the current cyanobacterial bloom situation (an average condition of recent 5 years) and compares this to the estimated target level for ‘good environmental status’ (GES). The current status is derived with an index combining indicative bloom event variables. As such we used seasonal information from the duration, volume and severity of algal blooms derived from earth observation (EO) data. The target level for GES was set by using a remote sensing based data set named Fraction with Cyanobacterial Accumulations (FCA; Kahru & Elmgren, 2014) covering years 1979–2014. Here a shift-detection algorithm for time series was applied to detect time-periods in the FCA data where the level of blooms remained low several consecutive years. The average conditions from these time periods were transformed into respective CyaBI target values to represent target level for GES. The indicator is shown to pass the three critical factors set for marine indicator development, namely it measures the current status accurately, the target setting can be scientifically proven and it can be connected to the ecosystem management goal. An advantage of the CyaBI method is that it’s not restricted to the data used in the development work, but can be complemented, or fully applied, by using different types of data sources providing information on cyanobacterial accumulations.

Advances in combining optical citizen observations on water quality with satellite observations as part of an environmental monitoring system

Citizen observations, environmental data gathered by volunteers without professional observation capabilities, have been extensively used for Finnish water quality monitoring tasks. Recently, mobile smartphones and their digital cameras have enabled more direct measurements of transparency related water quality variables with inexpensive technology suitable for volunteers. These “Secchi3000” ideas of measurement technology by viewing known targets through multiple viewing path lengths within measured water were used to develop an iQwtr measurement device for water transparency related citizen observations. Past experiences with crowdsourcing and use of in situ water transparency data with satellite observations are reviewed and future challenges outlined.