Estimation of forest parameters using airborne laser scanning data

Methods for the estimation of forest characteristics by airborne laser scanning (ALS) data have been introduced by several authors. Tree height (TH) and canopy closure (CC) describing the forest properties can be used in forest, construction and industry applications, as well as research and decision making. The National Land Survey has been collecting ALS data from Finland since 2008 to generate a nationwide high resolution digital elevation model. Although this data has been collected in leaf-off conditions, it still has the potential to be utilized in forest mapping. A method where this data is used for the estimation of CC and TH in the boreal forest region is presented in this paper. Evaluation was conducted in eight test areas across Finland by comparing the results with corresponding Multi-Source National Forest Inventory (MS-NFI) datasets. The ALS based CC and TH maps were generally in a good agreement with the MS-NFI data. As expected, deciduous forests caused some underestimation in CC and TH, but the effect was not major in any of the test areas. The processing chain has been fully automated enabling fast generation of forest maps for different areas.

Weather model verification using Sodankylä mast measurements

Sodankylä, in the heart of Arctic Research Centre of the Finnish Meteorological Institute (FMI ARC) in northern Finland, is an ideal site for atmospheric and environmental research in the boreal and sub-arctic zone. With temperatures ranging from −50 to +30 °C, it provides a challenging testing ground for numerical weather forecasting (NWP) models as well as weather forecasting in general. An extensive set of measurements has been carried out in Sodankylä for more than 100 years. In 2000, a 48 m high micrometeorological mast was erected in the area. In this article, the use of Sodankylä mast measurements in NWP model verification is described. Started in 2000 with NWP model HIRLAM and Sodankylä measurements, the verification system has now been expanded to include comparisons between 12 NWP models and seven measurement masts. A case study, comparing forecasted and observed radiation fluxes, is also presented. It was found that three different radiation schemes, applicable in NWP model HARMONIE-AROME, produced during cloudy days somewhat different downwelling long-wave radiation fluxes, which however did not change the overall cold bias of the predicted screen-level temperature.

Comparison between manual scaling and Autoscala automatic scaling applied to Sodankylä Geophysical Observatory ionograms

This paper presents a comparison between standard ionospheric parameters manually and automatically scaled from ionograms recorded at the high-latitude Sodankylä Geophysical Observatory (SGO, ionosonde SO166, 64.1° geomagnetic latitude), located in the vicinity of the auroral oval. The study is based on 2610 ionograms recorded during the period June–December 2013. The automatic scaling was made by means of the Autoscala software. A few typical examples are shown to outline the method, and statistics are presented of the differences between manually and automatically scaled values of F2, F1, E and sporadic E (Es) layer parameters. We draw the conclusions that: The F2 parameters scaled by Autoscala, foF2 and M(3000)F2, are reliable. F1 is identified by Autoscala in significantly fewer cases (about 50 %) than in the manual routine, but if identified the values of foF1 are reliable. Autoscala E layer parameters are close to those manually scaled when identified; however, Autoscala detects an E layer in many cases when none is identified by the manual scaler. Es and parameters of Es identified by Autoscala are in many cases different from those of the manual scaling. Scaling of Es at auroral latitudes is often a difficult task.

The Sodankylä in-situ soil moisture observation network: an example application to Earth Observation data product evaluation

Soil moisture is one of the main drivers in water, energy, and carbon cycles. Both latent and sensible heat fluxes, governing the air temperature and humidity boundary layer over land, are affected by variations in soil moisture. During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data needs to be validated by means of in-situ observations in different climatic regions. In that context, a comprehensive, distributed network of in-situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL-VAL reference site soil moisture observation network. The procedures for choosing the representative sites for individual soil moisture network stations are discussed, as well as the development of a weighted average of top layer (5–10 cm) soil moisture over the study area. Comparisons of top layer soil moisture around the Sodankylä CAL-VAL site between the years 2012 and 2014 using ESA CCI soil moisture data against in-situ network observations were conducted. The comparisons were made against a single CCI data product pixel encapsulating the Sodankylä observation sites. Comparisons have been made against both daily CCI soil moisture estimates and against weekly running average values. Soil moisture comparisons are only conducted during snow free and thawed periods, as the presence of snow and soil frost interfere with Earth Observation (EO) data based soil moisture retrievals. While the overall achieved correlation between the CCI data product and in-situ observations was low (0.479), this was largely the result of a single year of observations (2014) with poor correlation metrics. The best values were achieved in 2012 and 2013 at 0.551 and 0.621. All years exhibit a negative (dry) bias ranging from 0.0346 to 0.046. Averaging CCI soil moisture data from daily to weekly estimates significantly improves both correlation and RMSE, but has little effect on bias. The average correlation between the CCI data product and weighted average in-situ observations improves from 0.479 to 0.637. The improvements in correlation are most pronounced in 2012 and 2013, with an improvement from 0.551 to 0.840 and from 0.621 to 0.813 respectively.

Arctic Snow Microstructure Experiment for the development of snow emission modelling

The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0 and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small; with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

Soil moisture sensor calibration for organic soil surface layers

This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology HOBE. For the Decagon 5TE sensor such a function is currently not reported in literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified: for the Decagon 5TE apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger non-linearity in the sensor response and signal saturation in the high level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are based on the here proposed natural log fit. The newly derived ThetaProbe fit functions should be used for hand-held applications only, but in that case proof of value for the acquisition of instantaneous large-scale soil moisture estimates.

Proof of concept: temperature sensing waders for environmental sciences

A prototype temperature sensing pair of waders is introduced and tested. The water temperature at the stream-bed is interesting both for scientist studying the hyporheic zone as well as for, e.g., fishers spotting good fishing locations. A temperature sensor incorporated in waders worn by members of the public can give scientists an additional source of information on streamwater-groundwater interaction. A pair of waders was equipped with a thermistor and calibrated in the lab. Tests with both the waders and a reference thermometer in a deep polder ditch with a known localized groundwater contribution (i.e. boil) showed that the temperature sensing waders are capable of identifying the boil location. However, the temperature sensing waders showed a less pronounced response to changing water temperature compared to the reference thermometer, most likely due to the heat capacity of the person in the waders. This research showed that data from temperature sensing waders worn by the public and shared with scientists can be used by to decide where the most interesting places are to do more detailed and more expensive, research.

Sodankylä manual snow survey program

The manual snow survey program of the Arctic Research Centre of Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (SD) and snow water equivalent (SWE); however some older records of the snow and ice cover exists. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day measurements include observations of SD, SWE, temperature, density, horizontal layers of snow, grain size, specific surface area (SSA), and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

Sodankylä ionospheric tomography dataset 2003–2014

Sodankylä Geophysical Observatory has been operating a tomographic receiver network and collecting the produced data since 2003. The collected dataset consists of phase difference curves measured from Russian COSMOS dual-frequency (150/400 MHz) low-Earth-orbit satellite signals, and tomographic electron density reconstructions obtained from these measurements. In this study vertical total electron content (VTEC) values are integrated from the reconstructed electron densities to make a qualitative and quantitative analysis to validate the long-term performance of the tomographic system. During the observation period, 2003–2014, there were three-to-five operational stations at the Fenno-Scandinavian sector. Altogether the analysis consists of around 66 000 overflights, but to ensure the quality of the reconstructions, the examination is limited to cases with descending (north to south) overflights and maximum elevation over 60°. These constraints limit the number of overflights to around 10 000. Based on this dataset, one solar cycle of ionospheric vertical total electron content estimates is constructed. The measurements are compared against International Reference Ionosphere IRI-2012 model, F10.7 solar flux index and sunspot number data. Qualitatively the tomographic VTEC estimate corresponds to reference data very well, but the IRI-2012 model are on average 40 % higher of that of the tomographic results.

Removing low-frequency artefacts from Datawell DWR-G4 wave buoy measurements

In this study we describe a previously unreported error in the vertical displacement time series made with GPS-based Datawell DWR-G4 wave buoys and introduce a simple method to correct the resulting wave spectra. The artefact in the time series is found to resemble a sawtooth wave, which produces an erroneous trend following an f−2 power law in frequency space. The correction method quantifies the amount of erroneous trend below a certain maximum frequency and removes the spurious energy from all frequencies assuming the above mentioned f−2 power law. The presented correction method is validated against an experimental field test and its impact on the measured significant wave height is quantified. The method's sensitivity to the choice of the maximum frequency is also briefly discussed.