Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 10⁶ km² of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

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 14 days, 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

Large scale mapping of snow water equivalent (SWE) is a long-lasting request in many scientific and economical fields. Active and passive microwave remote sensing methods are explored, as local methods cannot be generalized due to the spatial inhomogeneity of the snow pack. Microwaves interact with snow by absorption, scattering, and refraction. For dry snow of a few meters depth and frequencies below 20 GHz, absorption and scattering in the snow volume are negligible compared with the backscattered energy from the underlying ground. The signal delay caused by refraction can be measured with differential radar interferometry, but phase wrapping errors and temporal decorrelation must be considered. We demonstrate that large ΔSWE can be accurately determined from dense time series of differential interferograms at Xand Ku-band by temporal integration. Lost phase cycles are reconstructed with a two-frequency approach. Temporal decorrelation is minimized by a temporal resolution of 4 h. A linear function between ΔSWE and phase difference is derived, which deviates only a few percent from the exact solution and which depends negligibly on snow density and stratigraphy. ΔSWE retrieved from observations of the SnowScat instrument (SSI) were validated against observed SWE from different reference instruments, installed at a test site near the town of Sodankylä, Finland. An accuracy below ±6 mm SWE was achieved at frequencies of 10 and 16 GHz for up to 200 mm of ΔSWE. An exceptionally high temporal coherence was observed for up to 30 days for dry snow, whereas for wet snow it decayed within hours.

Significance: We quantified a 36-y trend of advanced spring recovery of carbon uptake across the northern hemisphere boreal evergreen forest zone. From this trend, we estimated the corresponding change in global gross primary production (GPP) and further quantified the magnitude and spatiotemporal variability of spring GPP, that is, the cross-photosynthetic carbon uptake by forest. Our main findings are the following: ( i ) We developed a proxy indicator for spring recovery from in situ flux data on CO ₂ exchange and recent satellite snowmelt products and ( ii ) we established a relation between spring recovery and carbon uptake to assess changes in springtime carbon exchange showing a major advance in the CO ₂ sink.