Wildfires are a concerning issue in Canada due to their immediate impact on people’s lives, local economy, climate, and environment. Studies have shown that the number of wildfires and affected areas in Canada has increased during recent decades and is a result of a warming and drying climate. Therefore, identifying potential wildfire risk areas is increasingly an important aspect of wildfire management. The purpose of this study is to investigate if remotely sensed soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to identify potential wildfire risk areas for better wildfire management. We used the National Fire Database (NFDB) fire points and polygons to group the wildfires according to ecozone classifications, as well as to analyze the SMOS soil moisture data over the wildfire areas, between 2010–2017, across fourteen ecozones in Canada. Timeseries of 3-day, 5-day, and 7-day soil moisture anomalies prior to the onset of each wildfire occurrence were examined over the ecozones individually. Overall, the results suggest, despite the coarse-resolution, SMOS soil moisture products are potentially useful in identifying soil moisture anomalies where wildfire hot-spots may occur.

Estimating soil moisture (SM) over the circumpolar boreal forest would have numerous applications including wildfire risk detection, and weather prediction. Evaluation of satellite derived SM retrievals in boreal ecoregions is hindered by available in situ SM observation networks. To address this, an SM monitoring network was established in a boreal forest region in Saskatchewan, Canada. The network is unique as there are no other SM network of similar size in the boreal forest. The network consisted of 17 SM stations within a single Soil Moisture Active Passive (SMAP) satellite observation pixel ( $33\times 33$ km). We present an analysis of the sensitivity and accuracy of SMAP SM products in a boreal forest environment over a two-year period in 2018 and 2019. Results show current SMAP radiometer-based L2 SM products have higher correlation with the in situ lower mineral layer SM than with the top organic layer, although the overall correlation is low. Correlations between in situ mineral layer SM and SMAP brightness-temperature (TB) products are higher than those observed with the SMAP SM product, suggesting current SMAP SM retrieval from the TB using the $\tau $ – $\omega $ model introduces large uncertainties in the SM estimation, possibly from uncertain vegetation and surface parameters in the retrieval model. Results show SM can be retrieved using the $\tau $ – $\omega $ model with reasonable accuracy over the boreal forest provided the vegetation and soil parameters are optimized. The SM retrieval using a dual channel $\tau $ – $\omega $ model, which utilize both horizontally and vertically polarized SMAP TB, performs better than that with a single channel algorithm (SCA), using optimized parameters.

The soil moisture active passive (SMAP) validation experiment in the boreal forest took place near Candle Lake, Saskatchewan within an area previously studied as a part of the experiments conducted at the Boreal Ecosystem Research and Monitoring Sites (BERMS). This study specifically focuses on the data collected within a single SMAP radiometer footprint, covering an area of approximately 30 × 30 km2 within a boreal forest environment. The land cover classes and soil types in the boreal forest area of the BERMS region are dominated by coniferous trees and soils represented by an often-thick, organic-rich layer of moss and leaf litter above a mineral soil layer. The completed field experiment focused on accurately representing both soils and vegetation of the region based on comprehensive sampling campaigns completed over two sampling periods between 16–30 June and 6–18 August 2022. Numerous datasets were collected from sampling campaigns and through the installation of in situ networks. Comparisons among observed soil moisture (SM) and those retrieved from the SMAP and soil moisture and ocean salinity mission satellite platforms show a significant correlation to SM collected in situ. It is anticipated that the data collected as a part of this experiment (vegetation biomass and soil and vegetation structures) will be important for modeling efforts to improve SM retrievals in these environments.

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.