Forest fires in Canada’s boreal forest can cause a great deal of concern for populations, environment and infrastructures. One of the tools developed to predict potential fire activity related to these events is the Canadian Fire Weather Index System (FWICAN). This study aims to analyse the potential of Soil Moisture and Ocean Salinity (SMOS) satellite products (Soil Moisture (SM), Vegetation Optical Depth (VOD) and Root Zone Soil Moisture (RZSM)) to provide additional information on pre-fire soil and vegetion conditions for forest fire danger rating system. Using Random Forest algorithm, we show that adding SMOS data to the FWICANsystem indices slightly increases the accuracy of the predictions of potential fire activity. The RZSM from SMOS data was the variable that best improved the model performance, whereas the VOD provided no additional information. In the aim to evaluate the method for regions with limited in situ meteorological data, we used FWI system indices calculated from ERA5 available over the globe (FWIERA5). Taking into account FWIERA5, SMOS data allow to improve substantially the ability to predict forest fire.

We present a unique open-ended coaxial probe (OECP) that can accurately measure the permittivity of heterogeneous materials due to its large aperture. The probe works at frequencies ranging from 0.5 to 18 GHz, which is an important range for microwave remote sensing applications, and more precisely the Terrestrial Snow Mass Mission (TSMM). TSMM aims at launching a new satellite equipped with a dual Ku-band radar (13.5 and 17.2 GHz) for snow monitoring. At Ku-band frequencies, the backscattered radar signal contains information not only about the snow but also about the soil underneath. Knowing the soil permittivity will allow models to better account for the soil contribution to the total signal and to obtain more accurate snow data. To demonstrate the accuracy of the probe and the repeatability of the measures, calibration solutions of known permittivity were measured, and the results were compared to their theoretical values. Then, we conducted tests for approximating the penetration depth of the probe signal with dry and wet papers. Afterward, a protocol to use the probe to compute the permittivity of soil samples in freeze/thaw cycles was developed. The permittivities of commercial sand and organic soil from Iqaluktuuttiaq (Cambridge Bay, Nunavut, Canada) are presented for different relevant frequencies for microwave remote sensing applications and different temperatures (−20 °C to 20 °C) according to the new protocol. This article aims to present the probe and its potential use in microwave remote sensing applications, especially for the active Ku-band, where little research exists.

Spatial and temporal patterns of landscape Freeze/Thaw (F/T) state transitions within the cryosphere are highly variable with impacts to climate, hydrological, ecological, and biogeochemical processes. F/T state has a strong impact on the seasonal amplitude and partitioning of surface energy exchange, while ecosystem responses to seasonal thaw are rapid, with evapotranspiration, soil respiration, and plant photosynthetic activity accelerating with warmer temperatures and the availability of liquid water. The annual thaw period also influences the vegetation growing season, while variability in F/T timing rules vegetation net primary production and Net Ecosystem CO2 Exchange (NEE) with the atmosphere. More than one third of the Earth’s land surface is covered by seasonal or permanent soil frost. Many agricultural, engineering, and environmental issues and applications are affected by F/T state. A frozen land surface tends to reduce or even to impede water infiltration, which may promote flooding, surface runoff, and soil erosion.In this work, Spire data through the NASA Commercial Smallsat Data Acquisition (CSDA) Program are used to develop novel Global Navigation Satellite Systems Reflectometry (GNSS-R) methods to determine the F/T state over the Arctic-Boreal region. The generated theoretical and experimental capabilities will be applied to analyze the spatial patterns and temporal dynamics with an improved spatio-temporal sampling as compared to Synthetic Aperture Radar (SAR) missions.

The Fine Resolution Explorer for Salinity, Carbon and Hydrology (FRESCH) is presented. The science case and the mission objectives are discussed before presenting the mission concept. FRESCH is an L-band antenna array operated in beamforming mode providing data at a spatial resolution of 10-15 km to study the biogeochemical and physical phenomena taking place at the interfaces of ocean, land and ice. FRESCH has been submitted to the European Space Agency Earth Explorer 12 program.

The Arctic snowpack, characterized mainly by a dense wind slab (WS) layer overlaying less dense and porous depth hoar (DH), generates large uncertainties in microwave radiative transfer models (RTM) used to interpret satellite observations. In this work, we tested two improvements recently implemented in the snow microwave radiative transfer (SMRT) model. First, an improvement of the snow microstructure parametrizations introduces a polydispersity geometrical parameter (K) related to the grain shape and microstructural arrangement. Second, the new electromagnetic model (EM) based on the strong contrast expansion (SCE) allows a continuous formulation of the scattering coefficient as a function of the density between low-density snow and hard and icy snow. The SCE model was compared with in situ observations to the commonly used improve Born approximation (IBA) EM. Results show improved brightness temperature simulations at 19, 37, and 89 GHz compared to surface-based and satellite microwave radiometric measurements using polydispersity values ($K_{\text {WS}} = 0.80$and$K_{\text {DH}} = 1.33$) for scaling the measured optical grain size of the different snow layers with IBA. The finding is that the polydispersity values found in this study are of general applicability for Polar-layered snowpack. The SCE model yields results similar to IBA for snow densities up to$500 \; \text {kg}\,\text {m}^{-3}$but no analysis was investigated for the range of 500–700 kgm3 (firn). These improvements in snow microstructure and the new SCE RTM allow better simulations of Polar snow and therefore better Polar snowpack monitoring by satellite.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for April 2022 and the other one for July 2022. The IOPs will entail a deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

Monitoring properties of soil from microwave remote sensing is a very effective tool. In this context, the knowledge of soil permittivity values is important in order to guarantee the accuracy of the monitoring. Previous studies have showed the impact of temperature and moisture gradients in the top soil layer permittivity profile on remote sensing monitoring of soil. To increase our knowledge of these phenomena, we developed a new low cost equipment for continuous in-situ measurements of microwave [100 MHz-3GHz] permittivity soil profiles.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2021 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in April 2021, and a second one in July 2021. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

Seasonal snow cover plays a key role in freshwater resources, water security, natural hazards, and weather and climate. However, accurate estimation of snow-water equivalent (SWE) with remote sensing observations remains a significant challenge. NASA Terrestrial Hydrology Program launched its multi-year SnowEx mission whose primary goal is to develop and test techniques for estimating how much water is stored in some complex Earth's terrestrial snow-covered regions (e.g. forested areas). The first year of the 5-year campaign took place in Colorado during the winter 2016–2017, during which in situ measurements and ground-based remote sensing observations were collected by the scientific community. Throughout February 2017, about 100 people were deployed and over 30 remote sensing instruments were used. This required an exceptional coordination effort, which resulted in collocated in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements) with ground-based remote sensing observations (microwave radiometers, radar, scatterometers, lidars, etc.). The public release of all these datasets has started (nsidc.org/data/snowex).

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

Snow-covered surface passive microwave surface-based radiometer (SBR) observations were taken over 50 locations during the NASA SnowEx 2017 campaign. Several black body measurements done during the campaign show that the mean absolute errors (MAE: absolute difference between the radiometer measurements and measured black body physical temperature) of all measurements are about 1 K and the bias are lower than 1 K. Continuous observations during surface melt episodes show a strong increase in TB at higher frequencies (37 and 89 GHz) due to liquid water formation in the top snow layer. The increase also significant at lower frequencies (11 and 19) but slower due to the higher emission depth.

Two L-Band radiometers were deployed at a boreal forest site (one above the forest canopy and one below the overstory canopy), to characterize the microwave contributions of the ground and forest canopy on the FreezelThaw (F/T) microwave signal observed by spaceborne L-Band sensors. Preliminary results show that the F/T processes of the ground rather than the overstory canopy primarily contribute to the F/T signal at L-band. However, the radiometer above the canopy did show some sensitivity to F/T of the canopy (5 K), but this sensitivity was much lower compared to the signal induced by the ground F/T (12 K).

NASA SnowEx's goal is estimating how much water is stored in Earth's terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

The spatial and temporal distribution of ROS across the Canadian Arctic Archipelago (CAA) remains poorly understood owing to their sporadic nature in time and space. In this study, we highlight the distribution and evolution of ROS occurrences inventoried since 1984 at 14 Environment and Climate Change Canada (ECCC) weather stations in the CAA. We introduce an adaptation of the detection algorithm proposed by Dolant et al., (2016), to investigate spatio-temporal patterns in occurrence. Across the 14 weather stations, more than 600 ROS events were identified since 1984, 80% of which occurred during the Spring season.

Recent theoretical and experimental studies have indicated the feasibility of passive microwave L-band observationsfor observing dry snow cover characteristics, namely snow density in the lower approx.. 10 cm of the snowpack. The sensitivity of L-band emission to snow density is based on the dual influence of refraction and impedance matching on observed brightness temperature with changing effective snow permittivity. The permittivity of pure, dry snow, on the other hand, depends largely on snow density. In this study, we expand the theoretical and experimental results of retrieving dry snow density to passive L-band satellite observations. Such retrievals could be appealing in the context of improving satellite based retrievals of e.g. Snow Water Equivalent (SWE) using other sensors. Retrievals are applied to both multi-angular observations from the ESA SMOS mission, and observations of the NASA SMAP radiometer on a single angle of observation. While in theory the multi-angular approach is preferable, improved RFI mitigation in SMAP provides more spatially and temporally more stable retrievals. The applied dual-parameter retrieval scheme produces also an estimate of ground permittivity; experimental data showed dry snow cover to have a clear influence on ground permittivity retrievals, implicating that even dry snow cover is non-negligible also in retrievals of soil moisture from L-band observations.

The database helps to better understand and quantify the effect of F/T and snow on the L-Band signal. The information will be useful for the validation and calibration of satellite based products. The database will also be used to validate and calibrate different L-Band snow emission models [3–4–5].

Dry snow is conventionally considered as having minimal effect on microwave radiation at long wavelengths (such as L-band). However, dry snow affects observed microwave signatures even at these wavelengths through changes in impedance matching between soil and the overlying media, as well as through changes in the refraction angle at the soil interface. Exploiting these effects, the multi-angular, dual-polarized L-band observations of e.g. the European Space Agency's SMOS (Soil Moisture and Ocean Salinity) mission have the potential to derive snow properties, such as the density of the lowest layers of the snowpack in contact with the ground. This in turn, would have the potential to inform retrieval schemes of snow cover based on EO-data from other sensors. In addition, the theoretical studies demonstrate that the effect of dry snow on retrieval of other geophysical variables, such as soil moisture, is not negligible. In this study, we demonstrate the simultaneous retrieval of snow density and ground permittivity in dry snow conditions, using a multi-year dataset of tower-based L-band observations. We show that following predictions of the theoretical studies, the retrieved snow density matches that of the density measured for the lowest snow layers; dry snow cover is also shown to affect retrievals of ground permittivity by up to 40 %.

This study focuses on the coupling of the Canadian land surface scheme (CLASS) and a radiative transfer model (RTM) to simulate brightness temperature (TB) in boreal forest environments. A parameterization of winter vegetation emission and transmission is proposed to improve the winter TB simulations. When compared to AMSR-E observations, RMSE values of 8K at 37 GHz, 5 K at 19 GHz and 3 K at 10 GHz were obtained. It is also shown that ice crusts have an important impact on the TB in H-pol at the three frequencies. Despite the low sensitivity of microwaves in dense forest, the study provides an interesting tool for the assimilation of TB for SWE retrieval in CLASS.

Downwelling radiative fluxes from inhomogeneous environments are not easy to quantify from the total measured signal contribution. We propose the use of a Lambertian reflector to assess this issue. Using ray tracing to extrapolate the bidirectional reflectance distribution function of a surface with high reflectivity in the microwave spectrum, we characterized a surface that acts as a near-Lambertian reflector at 37 GHz. After implementing such a plate using molded aluminum and getting its emissivity, we validated its Lambertian reflectance properties based on microwave surface-based radiometer measurements. This analysis also gives guidelines on how to make a Lambertian surface for different wavelengths in the microwave spectrum. Field experiments show the usefulness of such a Lambertian plate to estimate the downwelling contribution in ground-based radiometric measurements.

This letter proposes relationships between boreal forest microwave transmissivity and four forest structural parameters: summer and winter Leaf Area Index (LAI) from MODIS, biomass (t ha-1), and total volume (m3ha-1) for northern Québec, Canada. These relationships were derived for summer AMSR-E data sets that took into account the effects of canopy emission and scattering. Root mean square error results between brightness temperature simulations and ASMR-E observations are approximately 5 K. Results reported in this study can be used as forest correction equations for key surface parameter retrievals under the boreal forest canopy, such as soil moisture or snow depth/water equivalent.

Passive microwave remote sensing is extensively used in polar regions to study the cryosphere. To better understand the measured signal above continental ice-covered areas, our objective is to estimate the microwave emission of bubbly-ice surfaces using a physically based multilayer electromagnetic model, i.e., the dense media radiative transfer-multilayer model (DMRT-ML). This model accounts for ice layers with variable amounts of bubbles. Each layer is fully described by its temperature, density, thickness, and air bubble radius. Simulations are performed using in situ data from two distinct sites: one in Antarctica on a coastal Blue Ice Area near the Cap Prud'Homme (CPH) station in Adelie Land and the other on the Barnes Ice Cap (BIC) located on Baffin Island in the Arctic. On this ice cap, superimposed ice with seasonal snow cover about 1 m thick was observed. In both cases, several ice parameters were measured or estimated, and the others were optimized. Results of the DMRT-ML simulations are compared with in situ surface-based radiometer (SBR) measurements at 11, 19, and 37 GHz at both horizontal and vertical polarizations. Results show that DMRT-ML is able to reproduce the microwave emission of different ice types with good accuracy when accounting for ice bubbles: final RMSE = 7.37 K and 8.42 K, for CPH and BIC, respectively, compared with RMSE ranging from 15 K to 40 K without bubbles. Comparisons between SBR measurements and satellite data for the BIC also show good agreement (RMSE = 4.1 K for 19 and 37 GHz, both polarizations).

Ice lens formation, which follows rain on snow events or melt-refreeze cycles in winter and spring, is likely to become more frequent as a result of increasing mean winter temperatures at high latitudes. These ice lenses significantly affect the microwave scattering and emission properties, and hence snow brightness temperatures that are widely used to monitor snow cover properties from space. To understand and interpret the spaceborne microwave signal, the modeling of these phenomena needs improvement. This paper shows the effects and sensitivity of ice lenses on simulated brightness temperatures using the microwave emission model of layered snowpacks coupled to a soil emission model at 19 and 37 GHz in both horizontal and vertical polarizations. Results when considering pure ice lenses show an improvement of 20.5 K of the root mean square error between the simulated and measured brightness temperature (Tb) using several in situ data sets acquired during field campaigns across Canada. The modeled Tbs are found to be highly sensitive to the vertical location of ice lenses within the snowpack.

Snow grain size is the snowpack parameter that most affects the microwave snow emission. The specific surface area (SSA) of snow is a metric that allows rapid and reproducible field measurements and that well represents the grain size. However, this metric cannot be used directly in microwave snow emission models (MSEMs). The aim of this paper is to evaluate the suitability and the adaptations required for using the SSA in two MSEMs, i.e., the Dense Media Radiative Theory-Multilayer model (DMRT-ML) and the Helsinki University of Technology model (HUT n-layer), based on in situ radiometric measurements. Measurements of the SSA, using snow reflectance in the short-wave infrared, were taken at 20 snowpits in various environments (e.g., grass, tundra, and dry fen). The results show that both models required a scaling factor for the SSA values to minimize the root-mean-square error between the measured and simulated brightness temperatures. For DMRT-ML, the need for a scaling factor is likely due to the oversimplified representation of snow as spheres of ice with a uniform radius. We hypothesize that the need for a scaling factor is related to the grain size distribution of snow and the stickiness between grains. For HUT n-layer, using the SSA underestimates the attenuation by snow, particularly for snowpacks with a significant amount of depth hoar. This paper provides a reliable description of the grain size for DMRT-ML, which is of particular interest for the assimilation of satellite passive microwave data in snow models.

Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50°N to 60 °N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gradient in fraction and stem volume. A large database (>; 2000 points with a stem volume ranging between 0 and 700 m3·ha-1) showed that brightness temperatures (Tb) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore,Tbvalues saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polarization), respectively. These relationships were used to estimate snowTbfrom the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas.