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ElectroMagnetic (EM) reasons resulting in temperature dependence of L-band Vegetation Optical Depth (L-VOD) are currently overlooked in remote sensing products. Discrepancies in retrievals of geophysical surface properties over vegetated areas can result from this incompleteness. This perception motivated to explore EM considerations in how temperature drives L-VOD of a boreal forest. Thereto, a novel physics-based model is developed and evaluated to assess L-VOD sensitivities to canopy temperature and some other model parameters. The L-VOD model is compared to L-VOD derived from close-range L-band brightness temperatures measured through the tree canopy at the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC) in Sodankyla (Finland) during a 4-week and a 1-day period in 2019. Furthermore, the model's ability to explain L-VOD retrieved from brightness temperatures of the “Soil Moisture and Ocean Salinity” (SMOS) satellite over the “Sodankyla grid cell” is investigated. Experimental L-VOD are maximal at around 0 °C and decrease when canopy temperature is moving away from zero degree Celsius. This temperature response, observed at different temporal- and spatial scales, is captured by the proposed L-VOD model and explained by freezing tree sap-water and the dependence of water permittivity on temperature. The demonstrated EM-induced temperature dependence suggest caution with interpreting satellite-based L-VOD, because increased L-VOD around the freezing point is not solely due to increased biomass or rehydration of the vegetation. Further, our study can find future application to compensate L-VOD for EM-induced temperature sensitivity. This potentially leads to improved explanatory power of temperature normalized L-VOD for characterization of forest phenology. Furthermore, we suggest examining the presence and strength of the demonstrated L-VOD temperature response as a practical L-VOD retrieval quality assessment method under steady forest phenology.

Land surface freeze/thaw (F/T) dynamics impact the surface energy balance, carbon fluxes, and hydrologic processes. Recent and on-going L-Band (≈ 1.4 GHz) spaceborne missions have the potential to provide enhanced information on F/T state over large geographic regions with rapid revisit time. However, the low spatial resolution of these spaceborne observations (≈ 45 km) makes it difficult to isolate the primary contributions to the F/T signal, including the soil, snow, and vegetation states. A ground-based L-Band radiometer measurement campaign was conducted in Saskatchewan, Canada during the winter of 2014–2015 to evaluate brightness temperature sensitivity to F/T processes, snow, liquid water in snow and assess theoretical retrievals of soil permittivity (eG), and snow density from experimental data. The ground-based radiometer was run in multiple configurations. First, temporally continuous measurements were conducted through the winter over an agricultural field, with a comprehensive network of reference snow and soil observations characterizing the F/T state of the soils within or adjacent to the radiometer footprint. Secondly, weekly multi-angular L-Band measurements were made at an undisturbed site of naturally accumulating snow cover, over a site that was kept snow free, and a site with artificially compacted snow. Results from the assessment of the land surface F/T retrieval algorithm showed that L-Band measurements are sensitive to the near surface F/T state of the soil, with the highest level of agreement found between the near surface (2.5 cm) F/T reference measurements of soil temperature and eG (accuracies of 91.1% and 92.9%, respectively). Several mid-winter melt events with air temperatures (Tair) above 0°, and soil temperatures below 0 °C, illustrated that liquid water within the snow dramatically increase the TB, resulting in false retrievals of soil thaw events using existing L-Band F/T retrieval algorithms. However, Tair was also shown to have a high commission errors compared to radiometer observations in detecting snow melt, because of the delay between Tair > 0 °C and the onset of melt resulting in a measurable wet snow signal at L-Band. The retrieval of snow density (ρs), of the bottom 10 cm of the snowpack tended to underestimate high ρs (> 400 kg m− 3), and agreed well for lower ρs (

A method for the retrieval of soil freeze/thaw (F/T) state in the boreal forest region using SAR is presented in this paper. The method utilizes Sentinel-1 data and is thus suitable for continuous near real-time monitoring. The main challenge with the C-band VV-polarization signal is the sensitivity to vegetation and especially to forest canopies. A relatively simple zeroth-order model is used for the retrieval of the ground and the canopy backscatter contributions in 1 km cell size. These backscatter components are then used to identify the F/T state of the soil by comparing them to corresponding reference values representing frozen and thawed conditions. The classification algorithm is based on threshold values applied on the Euclidian distances between the retrieved backscatter and the reference values. The method is tested for three test areas across Finland, having different forest properties: Sodankyla, Nurmes and Tampere, located in northern, central and southern Finland, respectively. We first evaluated whether the use of canopy cover (CC) or stem volume (SV) as the parameter describing the forest conditions provide better model accuracy. We then assessed the Sentinel-1 based soil F/T estimates by comparing them to automatic in situ observations and the SMOS (Soil Moisture and Ocean Salinity) based soil F/T product. The model performance was generally better when SV was used as the forest parameter. Nevertheless, for both CC and SV, the RMSE between the modeled and the observed backscatter was considerably lower than the seasonal variation of the backscatter. In Sodankyla and Nurmes, the Sentinel-1 based F/T estimates were well in line with the in situ observations and the SMOS F/T product. The Sentinel-1 retrievals measuring the top soil layer were fast to react to air temperature changes between negative and positive Celsius degrees, showing similarity of 94–99% with the air temperature measurements. In Tampere the method showed weaker results; the similarity with the air temperature observations was 64%. Overall, a correct vertical freezing pattern of the soil was demonstrated in this study, with Sentinel-1 sensitive to the top soil layer, in situ sensors measuring at 5 cm depth, and SMOS reaching to 5–15 cm soil depth. Additional assessment should be conducted in southern Finland.

Synthetic aperture radar (SAR) enables the mapping of flooding over large areas, regardless of cloud and weather conditions. Simplified classification approaches based on threshold levels of backscattering intensity are typical in operational flood monitoring. Backscattering intensity from floods over open non-forested areas is typically lower, whereas backscatter from forest floods is higher compared to non-flooded areas. However, distinction of flooded areas from non-flooded surface in semi-forested areas with low canopy closure (CC) or low tree height (TH) is expected to be difficult due to confounding effects of the different scattering mechanisms. The aim of this study was to investigate X-band SAR backscattering in flooded boreal forests with varying TH and CC, and to quantify in which cases floods would be difficult to detect with typical threshold-based classification methods. To further understand the SAR signal behavior in flooded forests, the total backscatter was modeled using the HUT (Helsinki University of Technology) semi-empirical forest backscattering model. HH-polarized Cosmo Sky-Med acquisitions from four different locations in Finland were analyzed against airborne LiDAR based forest data, ground observations and a high resolution digital elevation model. Floods were well detected in open areas and dense forests. However, as hypothesized, when TH was higher than zero but lower than 4–5 m, or when CC was higher than zero but lower than 15–20%, the detection was less successful. TH was found to have slightly more influence on the capability of X-band SAR to detect floods than CC. In our four test areas, namely Kittila, Kolari, Pudasjarvi and Evo, 85.3%, 89.1%, 82.7% and 73.9% of the total floods were detected, respectively, by a simple threshold value method. The model was able to successfully estimate the different backscattering components of the flooded forests in Kittila and Pudasjarvi, where the number of observations from all forest conditions was sufficient.

The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between − 6 kg m − 3 and 15 kg m − 3 for the forest clearing site, and between 37 kg m − 3 and 90 kg m − 3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈ 45 km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.

A prototype algorithm for hemispheric scale detection of autumn soil freezing using space-borne L-band passive microwave observations is presented. The methodology is based on earlier empirical and theoretical studies of L-band emission properties of freezing and thawing soils. We expand a method originally developed for soil freeze–thaw (F/T) state detection from L-band tower based observations to satellite scale, applying observations from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The developed algorithm is based on first establishing spatially variable thresholds for L-band brightness temperatures representing frozen and thawed states of soil, and comparing these to current values of different indicators of soil freezing, calculated based on observed brightness temperature at different polarizations and incidence angles. An exponential relation between the freezing indicators and the depth of soil frost is developed based on a large amount of manual soil frost tube observations across Finland. An additional processing filter based on observed physical temperature and snow cover information is used to flag obvious F/T detection errors. The estimated soil F/T-states provided in this study are limited to the autumn freezing period, as melting snow in spring effectively prevents acquisition of information from the soil surface using microwaves for large areas in Northern latitudes. The F/T estimate is produced as daily information and provided in the equal-area scalable Earth (EASE) grid. Soil F/T-state is categorized into three discrete levels: ‘frozen’, ‘partially frozen’, and ‘thawed’, and accompanied with a quality data matrix estimating the data reliability for each freezing season separately. Comparisons to in situ data were conducted at 10 different locations in Finland, Northern America and Siberia. These comparison results indicate that the onset of autumn soil freezing can be estimated from SMOS observations to within 1 to 14days, depending on the freezing indicator applied and the in situ data used in comparison. Although the initial results are encouraging, more comprehensive assessment of SMOS based soil F/T estimates still requires further comparison to other reference sites, particularly to sites with measurements available for all locally representative land cover types, as well as other satellite-based soil freezing products.

A key variable describing the mass of seasonal snow cover is snow water equivalent (SWE), which plays an important role in hydrological applications, weather forecasting and land surface process simulations. In this paper, the accuracy of an SWE product, GlobSnow-2, which combines microwave satellite data and in situ measurements, is assessed using three reference evaluation datasets north of 35°N in China. The GlobSnow-2 estimates are also compared with stand-alone satellite products (AMSR2, Chang and FY-3D SWE). The overall unbiased root mean square error (RMSE) and bias of the GlobSnow-2 SWE product validated with three reference datasets are 17.4 mm and 11.2 mm, respectively, which outperforms the AMSR2 SWE (39.3 mm and 37.3 mm, respectively) and Chang SWE (57.5 mm and 46.2 mm, respectively) products. The FY-3D SWE product performs better than the GlobSnow-2 estimate for shallow snow (SWE

The Helsinki University of Technology (HUT) snow emission model forms the basis of the European Space Agency's GlobSnow snow water equivalent (SWE) product (Takala et al., 2011). The model applies a semi-empirical radiative transfer calculation to account for the interaction of the snow medium with microwaves; separate components are applied to account for vegetation, the atmosphere, and emission from the ground surface. For the retrieval of SWE, an innovative method is used to account for spatial and temporal variability in snow conditions by retrieving an effective parameter describing the scattering behavior of microwaves in the snow (a proxy indicator of the microwave effective snow grain size). In this study, the influence of differing snow conditions, as well as varying land cover, on the retrieved microwave effective snow grain size was analyzed. Passive microwave measurements were acquired using tower-based, mobile sled-based and airborne radiometers in a mixed forest environment near Sodankyla, Finland. Forward simulations at the tower site over an entire winter period showed that the use of an empirical relation to modify the classical in situ measured grain size produced HUT model bias errors less than 6 K on average at 19 and 37 GHz from a ~ 20–80 cm deep boreal snowpack. Model simulations for airborne and sled-based observations showed that using a simplified 2-layer representation of the snowpack improves simulation biases and RMSE, although modification of the measured grain size was again necessary to achieve these results, regardless of the layering configuration. The microwave effective grain size retrieved from HUT predictions was closely related to a simple average grain size measured in situ, both in terms of magnitude and temporal trend. This is an important finding as the retrieval scheme of Takala et al. (2011) relies on the microwave effective grain size to retain a degree of physical basis for values to be generally applicable over larger areas, which is challenging because this free parameter also accounts for errors unrelated to the effective snow grain size (i.e. vegetation, soil conditions, scene heterogeneity, etc.).

Passive L-band (1–2 GHz) observables are sensitive to surface soil moisture and ocean salinity, which is the core of the “soil moisture and ocean salinity” (SMOS) mission of the European Space Agency (ESA). This work investigates microwave emission processes that are important to link L-band brightness temperatures with soil freeze/thaw states and the presence and the state of snow. To this end, a ground snow radiative transfer (GS RT) model has been developed on the basis of the “Microwave Emission Model of Layered Snowpacks” (MEMLS). Our model sensitivity study revealed that L-band emission of a freezing ground can be affected significantly by dry snow, which has been mostly disregarded in previous studies. Simulations suggest that even dry snow with mostly negligible absorption at the L-band can impact L-band emission of winter landscapes significantly. We found that impedance matching and refraction caused by a dry snowpack can increase or decrease L-band emission depending on the polarization and the observation angle. Based on the performed sensitivity study, these RT processes can be compensatory at vertical polarization and the observation angle of 50°. This suggests the use of vertical polarized brightness temperatures measured at around 50° in order to achieve segregated information on soil-frost. Furthermore, our simulations demonstrate a significant sensitivity of L-band emission at horizontal polarization with respect to the density of the lowest snow layer as the result of refraction and impedance matching by the snowpack.

An ice thickness retrieval algorithm utilizing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) was developed and applied to Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada, for the period 2002–2009. The temporal evolution of vertically polarized AMSR-E brightness temperature (TB) at 18.7 GHz was explored to estimate ice thickness between the late freeze-up (ice-on) and early break-up (melt-onset) periods. The sensitivity of AMSR-E TB at H and V polarizations to the seasonal evolution of ice thickness was examined statistically and with forward simulations of TB using the most recent version of the Helsinki University of Technology (HUT) model, which incorporates a freshwater ice layer. The strong relation found between TB at 18.7 GHz V-pol and ice thickness was exploited for the development of a regression-based ice thickness retrieval algorithm. Simple linear regression equations allow for the estimation of ice thickness on a monthly basis from January to April; one, the Global equation, combines TB data for GBL and GSL, and two others, the Regional equations, use TB data for each lake alone. Estimated late-winter ice thicknesses on GBL were determined to be on average 5–10 cm thicker than on GSL with the exception of ice season 2005–2006 when it was estimated to be 10 cm thicker on GSL. For both lakes the 2004–2005 and 2008–2009 ice seasons experienced the thickest end-of-winter ice thicknesses, ranging from 130 to 134 cm on GBL and GSL. The thinnest end-of-winter ice thicknesses were on average 120 cm on GBL (2005–2006) and 123 cm on GSL (2007–2008). Variability in air temperature, snowfall and subsequent redistribution by wind, and lake depth explain ice thickness variations within and between lakes over the seven winter seasons analyzed. Estimated ice thicknesses from AMSR-E compare well with coincident in situ measurements collected on GBL and GSL over a limited number of ice seasons and sites within the large passive microwave footprints (Mean Bias Error, MBE = 6 cm; Root Mean Square Error, RMSE = 19 cm).

We present a novel algorithm for detecting seasonal soil freezing processes using L-band microwave radiometry. L-band is the optimal choice of frequency for the monitoring of soil freezing, due to the inherent high contrast of the microwave signature between the frozen and thawed states of the soil medium. Dual-polarized observations of L-band brightness temperature at a range of observation angles were collected from a tower-based instrument, and evaluated against ancillary information on soil and snow properties over four winter seasons. During the first three winter periods the measurement site was located over mineral soil on a forest clearing, for the fourth winter the instrument was moved to a wetland site. Both sites are located in Sodankyla, Northern Finland. The test sites represent two environments typical for the northern boreal forest zone. The data were applied to derive an empirical relation between the onset and progress of soil freezing and the observed passive L-band signature. A retrieval algorithm was developed using the observations at the forest opening site. The algorithm exploits the perceived change in brightness temperature and the change in the relative difference between the signatures at horizontal and vertical polarization. With the collected experimental dataset, these features were linked optimally to the progress of soil freezing by choice of observation angle, polarization and temporal averaging. The wetland site observations provided the first opportunity for demonstrating the developed algorithm over a different soil type, giving a first estimate of the algorithm performance over larger heterogeneous targets. The future objective is to adapt the algorithm to L-band satellite observations. The present study is highly relevant for the development of freeze–thaw algorithms from current and future L-band satellite missions such as SMOS and SMAP.

Plot-scale brightness temperature (T B ) measurements at 6.9, 19, 37, and 89 GHz were acquired in forest, open, and lake environments near Churchill, Manitoba, Canada with mobile sled-based microwave radiometers during the 2009–2010 winter season. Detailed physical snow measurements within the radiometer footprints were made to relate the microwave signatures to the seasonal evolution of the snowpack, and provide inputs for model simulations with the Helsinki University of Technology (HUT) snow emission model. Large differences in depth, density, and grain size were observed between the three land cover types. Plot-scale simulations with the HUT model showed a wide range in simulation accuracy between sites and frequencies. In general, model performance degraded when the effective grain size exceeded 2 mm and/or there was an ice lens present in the pack. HUT model performance improved when simulations were run regionally at the satellite scale (using three proportional land cover tiles: open, forest, and lake) and compared to Advanced Microwave Scanning Radiometer (AMSR-E) measurements. Root mean square error (RMSE) values ranged from approximately 4 to 16 K depending on the frequency, polarization, and land cover composition of the grid cell. Snow water equivalent (SWE) retrievals produced using forward T B simulations with the HUT model in combination with AMSR-E measurements produced RMSE values below 25 mm for the intensive study area. Retrieval errors exceeded 50 mm when the scheme was applied regionally.

The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80 × 100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of −8.4 K and −8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (−5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R = −0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (−4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

Immediately before an April 2007 snow survey and passive microwave radiometer field campaign in the Northwest Territories, Canada, a rain-on-snow event deposited a thin (~ 3 mm) continuous layer of ice on the surface of the snowpack. At eight sites the brightness temperature ( T b ) of the undisturbed snow pack was measured with a multi-frequency dual polarization (6.9, 19, 37, and 89 GHz) ground based radiometer system. The ice lens was then carefully removed and the T b s were measured again. The individual V-pol channels and the 37 V − 19 V difference were largely unaffected by the presence of the ice lens, exhibiting a systematic shift of about 3 K. In comparison, the ice lens had a considerable effect on the H-pol T b at all frequencies, with a mean difference (ice lens present − ice lens removed) of − 9 K (± 5.3 K) at 6.9 GHz, − 40 K (± 11.3 K) at 19 GHz, − 33 K (± 7.6 K) at 37 GHz, and − 19 K (± 8.0 K) at 89 GHz. The effect of the ice lens on H-pol measurements was also observed with spaceborne data from the Advanced Microwave Scanning Radiometer (AMSR-E) satellite data. Simulations of T b were produced for each site using a new two layer formulation of the Helsinki University of Technology (HUT) snow emission model. The ice lens was used as the top layer and the underlying snowpack considered as a homogenous second layer. The agreement between observations and simulations was variable, with agreement strongest at 19 GHz. A comparison with simulations produced using the Microwave Emission Model of Layered Snowpacks (MEMLS) suggests HUT model uncertainty is related not to the ice lens, but to difficulties in simulating emission from deep snow. Overall, the observations and simulations suggest H-pol measurements are capable of detecting new ice layers across the tundra snowpack, while V-pol measurements are more appropriate for snow water equivalent (SWE) retrievals due to their relative insensitivity to ice layers.

Fractionation, size exclusion chromatography, thioacidolysis and 13C NMR of released suspension culture lignin (RSCL)produced by Picea abies sh

Suspension-cultured cells of Picea abies release lignin in the nutrient medium. Chemical analyses, 1 H and 13 C NMR spectroscopy of this material show that they represent carbohydrate-free guaiacyl type lignins with a high proportion of cinnamyl alcohol side chains a somewhat lower methoxyl content than milled wood lignin from spruce. The molecular weight distribution determined by HPSEC gave a weight average M r of 14 200 and a number average weight close to 4000. These are the first conifer lignin samples to be characterized without chemical or mechanical degradation.