Interferometric SAR (InSAR) is a promising tool for monitoring seasonal snow and for retrieving of Snow Water Equivalent (SWE) as the interferometric phase can be related to changes in SWE (ΔSWE). The boreal forest is a challenging landscape for the InSAR retrieval of SWE since it contributes to the signal by adding an undesired component originating from the vegetation. Although the technique has been validated extensively, most of these works are limited to discrete points. For comparison, we used snowpack simulations from the SnowModel, a high-resolution spatially distributed snow evolution model. This enables a better understanding of the limitations of L-band InSAR for SWE retrieval since it allows to evaluate its performance under different conditions. We analyzed the impact on coherence of snow melt between acquisitions and analyzed pairs with wet snow presence. The interferometric phase was inverted and compared to the simulated ΔSWEfrom the SnowModel distributions for three interferometric pairs. The results indicate a good spatial match between SnowModel and InSAR estimations. However, an increased difference was observed over densely forested areas when the air temperature was close to zero in at least one of the interferometric pairs. We hypothesize that the increase in permittivity of the forest for close to zero temperatures also increases the contribution from the canopy, consequently inducing errors in the retrieval. Both ALOS2 and SnowModel ΔSWEestimates were compared with in-situ data including a snow scale, snow depth from an Automatic Weather Station (AWS), a snow pit, and manual courses.

In this study, we evaluate the potential of deep learning models in predicting forest tree height in boreal forest zone using ESA Sentinel-1 and Sentinel-2 images. The performance of studied deep learning models is compared to several popular conventional machine learning approaches. The study area is located near Hyytiala forestry station in Finland, and represents a conifer-dominated mixed boreal forestland. Improved predictions were obtained when using combined optical and SAR data for all studied models. Our results indicate that UNet based models can achieve better accuracy in predicting forest tree heights (RMSE of$1.90m,\ \mathrm{R}^{2}$of 0.69), compared to traditional parametric and machine learning models with RMSE range of$2.27-2.41m$and$\mathrm{R}^{2}$range of 0.50-0.56 when satellite optical and radar data are combined.

Accounting for relatively large seasonal and short term peatland surface vertical displacements with Synthetic Aperture Radar Interferometry (InSAR) poses a problem of possible propagation of ambiguity errors. Notwithstanding, the absence of continuous high temporal resolution peatland surface levelling measurements for validation has been something characteristic. Based on the ground levelling from a raised bog, we demonstrate the Sentinel-1 distributed scatterer (DS) time-series InSAR technique underestimates real surface displacements and hereby we question the accuracy of the approach over peatlands. When the relative surface change from 6-day interferograms is used instead of accounting for the absolute change, the estimation accuracy improves (Spearman's rho 0.82, p-value < 0.002) because 6-day in situ surface changes are usually small and do not need InSAR phase unwrapping. Despite a serious unwrapping problem in peatlands, DS time series contain useful signal and differential InSAR (DInSAR) might have potential for assessment of short term peatland surface displacements in favourable conditions.

An investigation of boreal forest attenuation of a radar signal in winter is presented, applying a multifrequency (1–10 GHz) ground-based synthetic aperture radar (GB-SAR). As stable targets, corner reflectors (CRs) with known radar cross section (RCS) were used under the forest canopy. This enabled to relate changes in observed wideband backscattering from the reflectors to attenuation of the radar signal in forest vegetation, eliminating the influence of the background, such as snow and soil. We found that ambient temperature affected the observed attenuation of the radar signal in the entire 1–10-GHz frequency range. For temperatures$T < 0 ~^{\circ }\text{C}$, attenuation was found to decrease by up to 4.3 dB at the lowest observed temperatures of −36 °C, with peak attenuation occurring at$T \approx 0 ~^{\circ }\text{C}$. The overall apparent two-way attenuation increased by up to 18 dB from L- to X-band. The presence of snow on the canopy was found to increase attenuation by 1–4 dB, the effect increasing with frequency while having only negligible effects on vegetation backscatter.

Interferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C- and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.

Accurate measurement of Snow Water Equivalent (SWE) from remote sensors is still an on-going topic with many open challenges. Interferometric Synthetic Aperture Radars (InSAR) offers the possibility of retrieving SWE changes between acquisitions by exploiting the relation between the interferometric phase, the snow depth and the water contained on it. However, it is susceptible to estimation errors due to loss of coherence. Sources of decorrelation in snow have not been exhaustively investigated. Here we present the results from the SWE retrieval during both the 2019–2020 and 2020–2021 winters and an analysis of various environmental parameters on the snow coherence using SodSAR (Sodankylä SAR). SodSAR is a 1-10GHz tower-based fully polarimetric SAR with InSAR capabilities operating in northern Finland. Acquisitions were made every 12 hours for the 2019–2020 winter and every 6 hours for the 2020–2021 winter. In-situ instruments are used for validation and comparison.

Here, we examine a deep learning approach to perform land cover classification using country-wide SAR mosaics compiled using multitemporal Sentinel-1 imagery. We capitalize on our earlier study [1], demonstrating the suitability of deep learning models for land cover mapping using satellite C-band SAR images. A set of SAR mosaics compiled from consecutive Sentinel-1 IW mode acquisitions covering the whole territory of Finland was used in production of the whole-country land cover map. The imagery were used as an input to the state-of-the-art deep-learning model for semantic segmentation called FC-DenseNet. This model was pre-trained on the ImageNet dataset and further fine-tuned in this study. CORINE land cover map was used as a reference, and the model was trained to distinguish between 5 Level-1 CORINE classes. Upon the evaluation and benchmarking, we found that the FC-DenseNet model is able to achieve nearly 90% overall classification accuracy. These results indicate the suitability of deep learning approaches to support efficient operational wide-area mapping using satellite SAR imagery.

In this study, we assess the potential of long time series of Sentinel-1 SAR data in forest growing stock volume (GSV) estimation. The study site with 17,762 forest stands is located near the Hyytiälä forestry field station in Finland and represents the boreal coniferous forest. Altogether 96 images spanning more than three years of observations have been studied using linear and random forest regression approaches. Our analysis demonstrates considerable decrease in the prediction errors of GSV as the the number of input scenes increases. The use of feature extraction and dimensionality reduction techniques allows to achieve to nearly optimal performance already with 10 scenes. While the GSV prediction errors using individual Sentinel-1 scenes varied considerably from 86 to 93 m3/ha, the prediction accuracy with combined scenes improved to 76 m3/ha (44.9%) RMSE.

Northern peatlands are significant pools of stored carbon. Understanding of seasonal dynamics of peat water table (WT) is the key to improve spatial models of material flows and gas exchange in those landscapes. Since synthetic aperture radar interferometric (InSAR) observables are influenced by soil moisture variations, the objective of this study is to examine the ability of Sentinel-1 long term InSAR coherence magnitude (calculated using a single master image) to characterize seasonal dynamics of peatland WT over 3 years of different climatological conditions. We show how in the open bog the coherence can be preserved more than 6 months and is related to peat WT difference in favourable weather condition. The second degree polynomial regression model can describes the relationship between coherence and WT with Root Mean Squared Error RMSE 0.047 (p <; 0.001). These findings suggest long term InSAR coherence has potential to characterize seasonal bog WT fluctuations, especially during dry summers or in intensive drainage affected sites with high discharges.

To date, the potential of multitemporal interferometric SAR (InSAR) data in land-cover mapping has not been fully explored despite suitable time series increasingly acquired from SAR sensors. Here, we suggest to use an LSTM (Long Short Term Memory) based land-cover classifier to address this problem. Spatial context is preserved by using grey-level spatial dependencies and morphological profiles. Further, a 4-LSTM-based model was trained to capture the temporal dynamics of InSAR coherence. Altogether 39 Sentinel-1 interferometric coherence pairs acquired over Donana in Spain were used to evaluate the method performance. Achieved more than 90% overall accuracy indicates the strong potential of developed InSAR recurrent approach in improving differentiation between various land cover classes.

In this study, a set of bistatic interferometric SAR images acquired by the TanDEM-X mission are used along with fully polarimetric ALOS PALSAR data for the assessment of tropical forest properties in Nepal. Research to be presented at the conference concentrates on several scientific goals. First, location of interferometric scattering phase centre inside forest canopy is investigated using reference ALS measured canopy height model. Means for forest tree height extraction using both model based approaches (similar to Random Volume over Ground) and semi-empirical models are investigated and reported. It is shown, that forest tree height retrieval is possible with RMSE around 2.8 meters (R2=0.68). Secondly, correlations between ALOS PALSAR and tropical forest data are analysed, and AGB estimation using fully polarimetric SAR features is performed. Several statistical and non-parametric methodologies were tested and compared. Thirdly, forest AGB estimation is done using both TanDEM-X based tree height and L-band PolSAR features using statistical inversion framework.

Here, we investigate multiple ways of assimilating synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance the model performance over forested areas in the boreal zone. Land C-band satellite SAR backscatter data, X -band interferometric SAR coherence, as well as auxiliary data layers from forest authorities are used as a proxy in calculating the forest transmissivity, instead of traditionally used leaf area index (LAI) parameter. Our earlier experiments have shown, that when particularly ALOS PALSAR (L-band) and multitemporal composite Sentinel-l (C-band) data were applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. Here, we extend our analysis and examine data acquired by ALOS PALSAR, ESA Sentinel-l, and TanDEM-X mission of DLR, as well as several other auxiliary datasets on forest parameters. Our proposed model based approach indicates the potential of an SAR-based estimation of forest volume transmissivity and represents a viable way of active-passive microwave satellite data fusion and incorporating readily available reference data.

We have conducted a multiyear (2011-2014) airborne microwave radiometer measurement program of snow on lake ice, regularly using our HUTRAD instrument (6.9 to 36.5 GHz, vertical and horizontal polarization). Occasionally, we have also used our interferometric 1.4 GHz HUT-2D radiometer. Our data consist of observations along a 4-km test line over two lakes in southern Finland, and include data also for adjacent land areas, whose land-cover types range from forest to short vegetation. A wide variety of snow and ice conditions were encountered, covering early winter, mid-winter and late winter conditions, and the melting season. In this paper we focus on the comparison of brightness temperatures observed for snow on lake ice and snow on terrain.

A fully polarimetric Airborne Wind Vector Scatterometer (AWVS) is developed to provide independent airborne wind vector measurements for validation of space-borne GNSS-R measurements. The scatterometer is designed to meet 1 m/s wind speed accuracy requirement. In this ad hoc and low-budget project, the instrument development exploited on some already existing subsystems. The development started from scientific system requirement definition and ended to two experiment flights for wind vector retrieval from three areas at the Gulf of Finland, the Baltic Sea. One of the flights was carried out with a simultaneous overpass of the TDS-l satellite, conducting the GNSS-R measurements. The results confirm the measurement capabilities of the GNSS-R technology and the desired 1 m/s wind speed accuracy of the developed scatterometer. Moreover, the fully polarimetric backscattering results support well other recent measurements and models of cross-polarized sea surface scattering.

Single pass radar interferometry (sp-InSAR) is a well established technique for generation of digital elevation models (DEM). Differencing two DEMs acquired at different times can reveal topographic changes. However snow depth estimation by DEM differencing is still an ongoing topic in radar research: in contrast to snow free surfaces, the snow surface elevation is difficult to detect either because of microwave penetration into dry snow or because of the weak backscatter return from wet snow which significantly decorrelates the interferometric signal. In this study we demonstrate first results of wet snow depth estimation by differencing sp-InSAR DEMs acquired by the TanDEM-X satellite mission. The results show, in contrast to dry snow, a clear sensitivity to wet snow. However, additionally to a high vertical sensitivity of a few ten centimeters a very low noise-equivalent-sigma-zero (NESZ) is crucial for successful snow depth estimation.

In this study, bistatic interferometric Synthetic Aperture Radar (InSAR) data acquired by the TanDEM-X mission were used for automated classification of sea ice over the Baltic Sea, in the Bothnic Bay. A scene acquired in March of 2012 was used in the study. Backscatter-intensity, coherence-magnitude and InSAR-phase, as well as their different combinations, were used as informative features in several classification approaches. In order to achieve the best discrimination between open water and several sea ice types (new ice, thin smooth ice, close ice, very close ice, ridged ice, heavily ridged ice and ship-track), Random Forests (RF) and Maximum likelihood (ML) classifiers were employed. The best overall accuracies were achieved using combination of backscatter-intensity & InSAR-phase and backscatter-intensity & coherence-magnitude, and were 76.86% and 75.81% with RF and ML classifiers, respectively. Overall, the combination of backscatter-intensity & InSAR-phase with RF classifier was suggested due to the highest overall accuracy (OA) and smaller computing time in comparison to ML. In contrast to several earlier studies, we were able to discriminate water and the thin smooth ice.

In this study we compare semi-empirical interferometric coherence models, proposed in [1], for tree height estimation from TanDEM-X coherence scenes. The models are derived from Random Volume over Ground model, by applying simplifications and introducing empirical parameters at different complexity levels so that the models can be adapted to available ancillary data. Several different TandDEM-X interferometric scenes from Estonia are used to test the model performance in various conditions. All the results are compared with highly accurate canopy height models measured using airborne laser scanning. We demonstrate that models which are very simple to invert, produce accurate tree height estimates when the conditions are most favorable. Best results can be seen for winter images for frozen and dry snow conditions. Simple parametric sinc model can produce accurate tree height maps over large areas with pixel-wise deviation only few meters.

This paper provides a summary of Finnish Aalto-1 CubeSat mission results so far, and concentrates especially on the results achieved with it's miniature hyperspectral camera for Earth Observation. The Aalto-1 satellite is a multipayload nanosatellite with total mass of 4 kg. It features three different scientific payloads, all built for in-orbit technology demonstration. The main payload of the mission is a tiny hyperspactral camera AaSI, especially built for CubeSat satellite. The spectral camera is based on Fabry-Perot interferometric spectral filter technology and it is capable to measure freely adjustable spectral channels. The mass of the camera is only 592 grams. Additionally, the satellite features a deorbiting experiment and a radiation monitor instrument. The satellite platform has three-axis stabilization, two channel communication system and powerful on-board computer. The Aalto-1 satellite was launched on 23 June 2017 and the first image with the AaSI camera was acquired on 5 July 2017. By May 2018 the satellite has taken several images and spectral images and achieved the most important mission goals. The Aalto-1 mission results demonstrated for the first time in-orbit that a miniature Fabry-Perot spectral camera for Earth Observation can be successfully operated in space.

Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and subsequently lead to effective estimates of vegetation optical depth and scattering albedo acquired with the novel multi-sensor approach.

This letter proposes a novel way to assimilate synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance model performance over forested areas. L- and C-band satellite SAR data are used in order to characterize the forest transmissivity within the emission model, instead of the optical satellite imagery-based leaf area index (LAI) parameter. Examination of several combinations of satellite SAR data as a substitute for LAI within the L-MEB model showed that when ALOS PALSAR (L-band) and multitemporal composite Sentinel-1 (C-band) data are applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. The root mean squared difference between modeled and measured TBs was reduced from 6.1 to 4.7 K with single PALSAR scene-based transmissivity correction and down to 4.1 K with multitemporal Sentinel-1 composite-based transmissivity correction. Suitability of single Sentinel-1 scenes varied based on seasonal and weather conditions. Overall, this indicates the potential of an SAR-based estimation of forest volume transmissivity and opens a possible way of fruitful active-passive microwave satellite data integration.

A concentric raised bog is a common mire type in the southern boreal zone. The mires play an important role in the hydrological cycle of the environment, as their moisture is usually significantly different from open agricultural land or forest. The hydrological parameters, such as soil moisture (SM), are increasingly assessed by means of remote sensing. However, the microwave radiation properties of bogs have not been widely studied in the scope of L-band radiometry, an important technology for SM observations. In order to address this topic, this letter presents an analysis of measurements of a concentric open bog with a 1.4-GHz airborne radiometer. The series of airborne measurements are collected, along with ground measurements, in southern Finland. A two-layer emission model is used to simulate microwave emission of the bog in different moisture conditions. Results show that 1.4-GHz radiometry is sensitive to bog surface conditions, such as changes in moisture content and distribution. We also show that despite the significant difference in soil moisture, the bog has emissivity characteristics more similar to agricultural rather than forested area.

We present in this paper the work done to test the synoptic capabilities of the GNSS-R interferometric technique as a means towards sea surface altimetry estimation, which is an integrated part of the assessment of the GEROS-ISS mission. For this purpose, a new software receiver has been developed and tested: SPIR. With the lessons learned after a first flight campaign, some system modifications were performed and a second experimental campaign has been recently carried on. Initial results show interferometric waveforms that present the quality required for altimetric analysis.

In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.

In this paper, we propose a new approach for improving boreal forest soil moisture estimation using L-band microwave radiometer. The effect is achieved by introducing improved description of forest canopy contribution from multisensor SAR measurements. Spaceborne L-band radiometer is a valuable tool for providing soil moisture estimates globally. Unfortunately, complex vegetation layer, such as forest, can hamper the accuracy of soil moisture retrieval leading to rather poor results particularly over boreal forest areas. Currently, the L-band Microwave Emission of the Biosphere (L-MEB) model adopted in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm, uses Leaf Area Index (LAI) in order to to account for forest canopy contribution to total emission. However, it can argued that LAI presents poorly the actual structure of the coniferous forest. The LAI is calibrated to represent only the leaves, but at L-band, the main contribution to emission and attenuation is due to branches, while trunks and leaves have smaller effects. Here, we tested several combinations of spaceborne SAR data as a substitute of LAI in temperature brightness models for soil moisture retrieval. Particularly when L-band ALOS PALSAR stripmap data were used, the agreement between modelled and measured TB has improved from 0.46 to 0.55 in the L-MEB model.

In this paper we describe a light on-line documentation approach which was developed for a student satellite project, but is also applicable to wide variety of engineering related student projects. The proposed approach allows flexible schedules in student team, group work and collaboration, assessment of the results, accumulation of the experience gained by previous teams. It integrates also rigorous systems engineering tools to meet the challenges of a complex engineering project. The approach is based loosely in documentation practices in space technology projects combined with best practices from student satellite projects.

In this study, we assess the potential of combining forest tree height derived from interferometric SAR data with satellite optical data for improving accuracy of forest stem volume mapping. Study site was located near the Hyytiälä forestry station in central Finland, with terrain representative of the boreal coniferous forest. As a primary interferometric data, several data takes of TanDEM-X data are used. Satellite optical data were represented by Landsat 8 image. The ground reference data were information on stand level from forest management plans. Firstly, forest tree height is estimated from TanDEM-X interfer ometric SAR coherency. Further, retrieved tree heights are combined with optical data for predicting forest stem volume using linear regression framework. Results of regression analysis performed demonstrate considerable improvement in terms of obtained accuracy figures of the combined stem volume estimation (RMSE = 34%, R2=0.57) compared with use of optical satellite data only (RMSE=40%, R2=0.43).

We have conducted airborne multi-frequency radiometer measurements over two lakes and adjacent land areas in southern Finland over a period of several winters using a frequency range of 1.4 to 36.5 GHz. Data have been collected under a variety of snow, ice, and weather conditions in order to determine the behavior of the snow-ice-water system. This paper presents an overview of the airborne campaigns and results confirming that the brightness temperature behavior of the snow/lake ice/water system is different from that of the snow/terrain system. This needs to be taken into account in algorithms for retrieval of snow characteristics from space-borne radiometer data for northern lake-rich areas. Comparisons between experimental brightness temperatures and theoretical results show that the HUT snow emission model performs well for lake ice.

This study provides a comparison of built-up area detection methods from Sentinel-1A SAR images. The selected methods are based on local area statistics (speckle divergence and difference between the local mean and local median) and repeat-pass interferometric coherence. The dataset comprises VV and VH polarization SAR images acquired during ascending and descending orbits over two study areas in Estonia, and ancillary map information. Our results show that combining measurements from both orbits and both polarisations yielded the highest accuracy for built-up area detection. In the case of methods based on local statistics, classification accuracy increased twofold, up to 72% (user accuracy, mean-median method). For all methods, the results differed between the study areas, indicating the existence of site specific effects. The methods yielded lower classification accuracy when applied to a more rural study area.

Rapid development of Earth Observation technology and increasing awareness of global challenges has increased the need for lean and agile space missions and calls for a new generation of engineers to design them. Miniaturization of sensor technology and electronics has decreased the weight of a satellite, which has brought launch prices to the reach of educational institutes and smaller countries. This is recognized in many universities where nanosatellite projects are used for education. A standard, called CubeSat, has been driving the development and become the most popular development platform for university space projects [1].

This work investigates applicability of spaceborne repeat-pass interferometric SAR images for detecting mowing events on agricultural fields. Four pairs of one-day repeat-pass COSMO-SkyMed acquisitions were analysed and compared to in situ measurements of 11 agricultural grasslands to study the potential of X-band temporal interferometric coherence for detecting mowing events. Field works covered 11 test plots in Central Estonia with varying species composition and homogeneity. Temporal decorrelation due to changes in the grass height and wet biomass was analysed. A nonlinear relationship was observed between the wet biomass and temporal coherence, as well as between the grass height and the temporal coherence. Our results show that one-day temporal coherence decreases as the grass height and the wet biomass increases, until reaching a noise level at 25 cm and 400 g, respectively. The current study shows that detecting mowing event from multitemporal interferometric SAR images is a feasible technique and could be used for monitoring applications on the European level.

This paper evaluates performance of fully polarimetric SAR (PolSAR) data in several land cover mapping studies in the boreal forest environment, taking advantage of the high canopy penetration capability at L-band. The studies included multiclass land cover mapping, forest-nonforest delineation, and classification of soil type under vegetation. PolSAR data used in the study were collected by the ALOS PALSAR sensor in 2006-2007 over a managed boreal forest site in Finland. A supervised classification approach using selected polarimetric features in the framework of probabilistic neural network (PNN) was adopted in the study. It has no assumptions about statistics of the polarimetric features, using nonparametric estimation of probability distribution functions instead. The PNN-based method improved classification accuracy compared with standard maximum-likelihood approach. The improvement was considerably strong for soil type mapping under vegetation, indicating notable non-Gaussian effects in the PolSAR data even at L-band. The classification performance was strongly dependent on seasonal conditions. The PolSAR feature data set was further modified to include a number of recently proposed polarimetric parameters (surface scattering fraction and scattering diversity), reducing the computational complexity at practically no loss in the classification accuracy. The best obtained accuracies of up to 82.6% in five-class land cover mapping and more than 90% in forest-nonforest mapping in wall-to-wall validation indicate suitability of PolSAR data for wide-area land cover and forest mapping.

The paper describes evaluating the potential of peatland detection under forest canopy with L-band space borne quad-polarization data in the boreal forest zone. Particular emphasis was made on under what seasonal conditions this detection was possible using single SAR data-take. For this purpose multitemporal ALOS PALSAR imagery acquired over Kuortane test site in central Finland during 2007-2008 was used. Supervised classification experiments employing selected polarimetric features were performed using standard maximum likelihood approach and probabilistic neural network (PNN). Strong non-gaussianity effects were noted, with better performance demonstrated by PNN, utilizing non-parametric estimation of probability distributions of the respective polarimetric features. Suitability of several techniques aimed at compensating the presence of forest canopy was studied as well.

The paper describes algorithm development for tree height retrieval in the boreal forest zone from TanDEM-X interferometric imagery. A set of 8 TanDEM-X pairs was acquired during summer and autumn 2011 over southern Finland in order to evaluate the potential tree height retrieval performance for this space-borne instrument. Another focus of the study was evaluation of seasonal dependence of interferometric signature of boreal forest. The obtained results are compared to our previous studies on tree height retrieval with the airborne DLR E-SAR instrument in the same area. The obtained results show good potential of TanDEM-X in forest mapping when external terrain elevation model is available, though accuracy seems to be somewhat lower compared to airborne instruments due to increased noise.

Scattering phase center (SPC) location in boreal forests was studied in order to assist forest inventory with single- and quad-pol synthetic aperture radar (SAR) interferometry. Airborne X- and L-band interferometric SAR data collected by the DLR E-SAR instrument in southern Finland during the FINSAR campaign was used in the study. A simple Random Volume over Ground (RVoG) model was employed as the theoretical framework for inversion of forest parameters and interpretation of the obtained results. LIDAR measurements of the canopy height and terrain elevation were used as reference and auxiliary data. The RVoG model was found to satisfactorily explain the SPC location inside the canopy in boreal forests. We show that when using X-band, the height of the SPC is typically about 75% of the canopy height, as predicted by the RVoG model; however, the retrieved extinction was found to be rather low. The feasibility of highly accurate tree height inversion using single-polarization X-band interferometry (with RMSE approaching 1.5 m) is demonstrated using a digital terrain model. For this purpose, the traditional polarimetric interferometry SAR technique for phase center retrieval is modified to include a complementary LIDAR measured terrain model. At L-band, the phase center height was determined to be around 50% of the canopy height and even lower, indicating that the ground contribution is significant. Moreover, several simplified inversion approaches for tree height and extinction coefficient retrieval were considered based on several boundary cases of the RVoG model, describing the canopy frequently encountered in boreal forest environments. These analyses allowed developing a combined approach for simultaneous estimation of both forest height and extinction in the boreal zone when an accurate elevation model of the terrain is available.

In this paper we describe the Finnish Earth Observation nanosatallite project Aalto-1. The Aalto-1 is a 4 kg student satellite, based on CubeSat standards. The satellite is de signed to carry the world's smallest remote sensing imaging spectrometer for Earth Observation and several other pay loads.

The attenuation of spheroidal melting hydrometeors is simulated in C-, Ku- and Ka-band utilizing a microphysical melting layer model. The scattering properties are obtained with Mie scattering solution. In C-band the polarimetric radar parameters are computed utilizing a method based on volume integral equation. Polarization difference is detectable, but reflectivity values are regularly smaller than those calculated with Mie solution. This is dependent on the process of formatting the particle structure according to the change in liquid water mass fraction.

In this study we give short overview of polarimetric SAR image visualization with colors. By studying the color models and polarization visualization models we propose basic principles which should be followed when presenting polarimetric information in color. We show that for different polarimetric parameters, different color models should be used, and give guidelines for color model selection. We present also two visualization schemes which are suitable for interpretation and browsing of large polarimetric SAR images.

In this paper, we present accurate calculations of electromagnetic wave scattering from a high-detail pine tree model, which includes also the needles. We have deployed a volume integral equation (VIE) model, which takes into account high-order reflections between all scatterers, even between single needles. The pine tree is modeled as a realistic collection of dielectric cylinders. With the help of our calculations, we assess the importance of multiple scattering inside the pine canopy and the contribution of the needles to the scattering. We compare our calculations with a much simpler model output to determine the validity conditions for simplified approaches. As the simpler model, we use an infinite cylinder approximation (ICA) model which uses a truncated cylinder approximation and takes into account only the first-order reflections from single cylinders. Both models are bistatic, fully coherent, and fully polarimetric models suitable in calculating the scattering from a large and general collection of dielectric cylinders. Our results show that, for the C-band calculations, the VIE model output differs significantly from the simple ICA model output, indicating the importance of the higher order scattering between the needles and branches. However, for the L-band, the accurate VIE model gives similar results as the ICA model, indicating that, in this case, the higher order scattering between the needles can be neglected.

In this paper we summarize the results of FINSAR campaign, which was arranged to evaluate X- and L-band SAR interferometric and polarimetric SAR techniques for Boreal forest. The main emphasis of the work was on L-band polarimetric interferometry and forest height estimation. Also X-band interferometry and coherence tomography for X- and L-band, phase center height, extinction coefficient of forest and several other aspects of polarimetric interferometry were studied with help of ancillary measurements. Our results show that L-band polarimetric SAR interferometry can estimate well Boreal forest height. Also X-band interferometry shows good potential in height estimation. When accurate ground model is available, tree height can be estimated even by using one polarization interferometry. SAR appears to be more accurate in forest height measurement than forest inventory database, but not as accurate as laser measurement.

This paper describes a prototype of an urban morphological database for micro-meteorological and dispersion modelling. The database relies on digital maps and satellite observations including optical images, SAR interferometry and digital maps. The structure of the database is presented and methods to produce thematic layers in an affordable way are shown. A fine resolution model was compiled regarding urban morphological features that cover a rectangular area of 6 × 3 km2in central Paris. The model contains selected thematic layer types on water, parks, trees, streets, buildings, average height of blocks, digital elevation model of the terrain, coherence and the urban heat fluxes. This study is part of an EU-funded project ¿Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation¿ -MEGAPOLI.

In this letter, we study scattering from a scots pine tree by using a realistic cylinder model of the tree and the coherent electric field scattering model. We study the directional variability of bistatic and monostatic scattering at L-band and show that, due to large variations in results, some averaging technique should be used to describe and interpret the model output efficiently. We propose that the needed averaging can be done by multilooking and that multilook data could be easily generated by rotating the tree model randomly around its vertical axis. We show that the resulting scattering data obey generally the multidimensional Gaussian distribution or the more general K-distribution, in a way similar to synthetic aperture radar (SAR) image pixels, and can therefore be represented by a single averaged covariance matrix. The trunk-ground reflection to the backscattering direction adds to the model output non-Gaussian behavior, which can be treated as texture. Covariance matrix formalism allows us to use descriptors which are commonly used to analyze SAR images, like target entropy and alpha angle. The method helps interpretation and comparison between the model output and SAR image.

The purpose of this paper is to discuss two polarimetric parameters which are widely used in synthetic aperture radar (SAR) polarimetry, namely, target entropy and alpha angle. We propose alternative parameters based on our analysis on how they are connected to covariance matrix similarity invariants and how they can be physically interpreted in optical polarimetry. The proposed alternatives can be computed by a fairly simple algorithm and even by the use of software without complex mathematics abilities. As an example, a NASA/Jet Propulsion Laboratory Airborne SAR L-band image of the San Francisco Bay is used to compare the proposed parameter schemes with the original entropy and alpha. A coherent rationale for these alternative parameters is formulated in order to provide insight to polarimetric parameter interpretation.

In this paper we evaluate X- and L-band SAR coherence tomography in boreal forest with the help of detailed digital terrain and canopy height models, produced by laser scanning. Polarimetric coherence tomography (PCT) needs accurate estimates of ground phase and tree height. Supplemental accurate elevation models allow us to evaluate the performance of PCT in normal case when initial values are derived from RVoG model inversion and provides opportunity to use PCT for nonpolarimetric data. The work is based on E-SAR L-band and X-band measurements in Finland. Our results show that with accurate elevation and tree height information single polarization X-band coherence tomography is feasible and works well. Accurate ground elevation information improves also the performance of fully polarimetric repeat pass L-band PCT. The laser DEM provides better ground phase estimate than RVoG model inversion in the presence of temporal decorrelation. Our results show that accurate ground phase estimation is more critical for successful coherence tomography than other parameters.

In this paper we study the extinction coefficient of boreal forest by utilizing airborne E-SAR X-band POLInSAR and HUTSCAT X-band profiling scatterometer measurements. By combining E-SAR VV-pol coherency with HUTSCAT tree height measurements we calculate forest extinction coefficients by RVoG model inversion and compare the results with extinction values obtained from HUTSCAT measurements. For retrieval of the extinction coefficient we propose robust RVoG model inversion procedure and discuss the model inversion conditions. Our results show, that extinction coefficient for boreal forest is quite low even for X-band, especially from nadir looking instruments. The extinction coefficient of forest canopy retrieved from HUTSCAT measurements is 0.15 dB/m and retrieved from E-SAR and HUTSCAT measurements is 0.9 dB/m.

In this letter, we present results from the FinSAR project, where the E-SAR and Helsinki University of Technology Scatterometer (HUTSCAT) instruments were operated together in order to validate tree-height retrieval algorithms for boreal forest. The campaign was carried out in Finland in fall 2003. The main instruments of the campaign were the E-SAR airborne radar (operating at L- and X-band) and the HUTSCAT helicopter-borne profiling scatterometer (operating at X- and C-band). We compare and discuss forest height obtained from the inversion quad-pol polarimetric interferometric synthetic aperture radar (SAR) data sets at L-band and forest height obtained from the inversion of single-pol X-band in SAR data with forest height estimates from HUTSCAT scatterometer data. Our results show that the forest height values, which are estimated by means of two different radar instruments, are in good agreement. The correlation between HUTSCAT and E-SAR height estimates ( at L-band and at X-band) underlines the good agreement between the results obtained by the two approaches.

In this study we concentrate on the application and validation of forest height estimation by polarimetric SAR interferometry for boreal forest. The study material was collected during the FinnSAR campaign, carried out in Finland in fall 2003. The main instruments of the campaign were E-SAR airborne radar (L- and X-band) and HUTSCAT helicopter-borne profiling scatterometer (X- and C-band). The validated forest height estimation algorithm is based on random volume over ground (RVoG) model inversion by using POLinSAR data. We compare POLinSAR-derived forest height with results from profiling HUTSCAT scatterometer measurements and with ground measurements and discuss the results. Our results show that the forest height values, estimated by means of two different instruments, are in good agreement.

This study examines and discusses the usability of airborne spectrometer in investigation of the effect of variable viewing angle in snow cover monitoring. The focus is on the effect of the observation angle to the detected snow spectra and forest transmissivity. Reflectance determination is sensitive to snow anisotropical reflectance properties and, in forested areas, spectra can be very different, observed from variable sensor view angles. The results of this study are used to investigate the anisotropical scattering of snow-covered areas and they are also compared with Envisat MERIS and Terra MODIS observations. With the airborne spectrometer - derived land cover multiangular spectral features the data obtained at wide viewing angles can be corrected. The results suggest that the airborne data are feasible for the modelling and accuracy assessment of satellite data-based snow cover area (SCA) estimation algorithms.

In this work we demonstrate the feasibility of imaging spectrometer for the detection of oil spills on sea ice. We show that optical spectrometer images can be used as an alternative for oil spill mapping in winter when SAR-based detection algorithms fail due to ice. By comparing high-resolution airborne spectrometer image to satellite images, we evaluate the usability of MODIS and Landsat images for oil pollution detection on ice and discuss the limitations, set by image resolution and spectral band availability. We evaluate here several spectral indices and discuss the results. We propose simple algorithms for oil detection on ice. Our study strongly suggests that an imaging spectrometer suits very well to oil detection on sea ice. However usability of satellite instruments like MODIS have serious limitations set by the image resolution and band selection. Landsat ETM has significantly better resolution and it is therefore more suitable for most typical, small-scale pollution detection, but its imaging frequency does not meet the monitoring demands

In this study the usability of optical airborne spectrometer data in snow cover monitoring is examined and discussed. Snow-covered area (SCA) estimation, specifically during the spring melt period, is important both for hydrological forecasting and climatological studies. The results of this study are used in accuracy assessment and further development of the Finnish Environment Institute's satellite data-based SCA-algorithm. The algorithm applies an empirical reflectance model that describes the reflectance from target area as a function of forest, snow and bare ground reflectance, SCA and average forest transmissivity.

In this work a coherent backscattering model for cylinders has been employed to simulate L-band and C-band polarimetric backscattering form a pine forest. Scattering covariance matrix, entropy, alpha angle, polarimetric coherence and temporal coherence are calculated and compared with SAR measurements. The results show that direct backscattering from tree crowns is an important scattering mechanism. Realistic ground reflection modelling was shown to be very important. By simulating the tree growth, general agreement between the known biomass and backscattering parameters was achieved.

Airborne passive microwave signatures collected in Northern Finland during EMAC-95 are analyzed with the emphasis on forested areas and dry snow conditions. The microwave signatures cover the 6.8-18.7-GHz frequency range and were acquired at both vertical and horizontal polarizations. The analysis is carried out with respect to the forest-stem volume data and comprises three different snow-depth situations. Emissivities approach saturation limit with the increasing stem volume. At 10.65 GHz, the saturation level was found to be linearly related to the snow-water equivalent. On the basis of passive-microwave measurements, an empirical forest transmissivity model is developed. The model is valid at vertical polarization 50/spl deg/ incidence angle, and it accounts for microwave frequency and forest-stem volume effects in the range of 6.8-94 GHz and 0-150 m/sup 3//ha, respectively.