A Novel Approach to Map the Intensity of Surface Melting on the Antarctica Ice Sheet using SMAP L-Band Microwave Radiometry

The polar ice sheets have undergone unprecedented melt events in the recent past, which have consequences for ice sheet mass balance, stability, and sea level. In this study, we employed L-band (1.4 GHz) brightness temperature observations collected by NASA's Soil Moisture Active Passive (SMAP) mission to investigate the extent, duration and intensity of melt events on the Antarctic Ice Sheet from 2015 to 2020. Satellite microwave measurements have long been used to detect melt events because of their sensitivity to the presence of liquid water in snow and ice. The observed microwave response depends on the sensor measurement frequency. Our hypothesis for this study is that the relatively long wavelength SMAP observations can detect a wider range of surface wetness conditions relative to shorter wavelength microwave observations that attain signal saturation at relatively lower wetness levels and within shallower surface layers. SMAP provides nearly all-weather surface monitoring over all of Antarctica twice daily with morning and evening overpasses at about 40 km spatial resolution. We applied an empirical threshold algorithm using horizontally and vertically polarized microwave brightness temperature differences to detect surface melt events over Antarctica from 2015 through 2020. The results show that the SMAP empirical algorithm can be used to detect melt extent and duration, and the geophysical model-based algorithm can be used to detect snow wetness, which can be used as an indicator of melt intensity. Analysis of the melt seasons between 2015 and 2020 show that the even though the melt extent in 2019–2020 was not as large as during the 2015–2016 melt season, it was significantly more intense, particularity on the West Antarctic Ice Sheet.

Hourly soil moisture mapping over West Africa using AMSR-E observations and a satellite-based rainfall product

This paper provides an original and simple methodology to map surface soil moisture with a fine temporal and spatial resolution over large areas based on a satellite rainfall accumulation product and soil microwave emission measurements at C-band. The first motivation of this study was to obtain high temporal frequency (~1 h) in order to study the possible feedback mechanisms between soil moisture and convection in West Africa. The use of soil moisture maps derived from satellite microwave measurements was not possible due to the low (at best daily) temporal resolution. Thus, a rainfall accumulation product based on Meteosat geostationary satellite measurements was used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture map at the 10×10 km2 and 30 min resolution. Due to uncertainties on the satellite-based rainfall accumulation product, derived soil moisture maps were found to be erroneous. An assimilation technique based on AMSR-E C-band measurements into a microwave emission model was developed. The assimilation technique described in this study consists of modulating the rainfall accumulation estimate between two successive AMSR-E brightness temperatures (TB) measurements in order to match simulated and observed TB. When a rainfall event happens, the initial rainfall accumulation estimate is modulated using a multiplicative factor ranging from 0 to 7. The best solution is given by the rainfall rate which minimizes the difference between observed and simulated TB. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the methodology which was found to improve the soil moisture estimates over the three sites.

Daily Microwave-Derived Surface Temperature over Canada/Alaska

The land surface temperature variation over northern high latitudes in response to the increase in greenhouse gases is challenging because of the lack of meteorological stations. A new method to derive the surface temperature from satellite microwave measurements that improves the frequency of measurements relative to that of infrared data is presented. The daily Special Sensor Microwave Imager 25 km × 25 km Equal-Area Scalable Earth Grid (EASE-Grid) dataset provided by the National Snow and Ice Data Center in Boulder, Colorado, is processed to derive the surface temperature using the method proposed by Fily et al. A normalization approach based on the 40-yr ECMWF reanalysis (ERA-40; 2.5°) temperature diurnal cycle fitted for each pixel is applied to overcome the time acquisition variation of measurements as well as to interpolate missing data. An adaptive mask for discriminating between ice-free pixels and snow-free pixels is also applied. The resulting database is thus a new consistent hourly series of near-surface air temperatures during the summer (without snow). The mean accuracy is on the order of 2.5–3 K when compared with the synchronous in situ air temperature and different gridded datasets over Canada and Alaska. The trend over the last 10 yr confirms observed climate evolution: an increase in summer surface temperature of +0.09° ± 0.04°C yr−1, at the 90% confidence level, for Canada between 1992 and 2002, whereas a decrease of −0.15° ± 0.05°C yr−1, at the 95% confidence level, is observed for Alaska. Spatial and temporal anomalies show regional impacts of meteorological phenomena such as the El Niño extreme warm summer episode of 1998, the decrease in temperatures in 1992 in Canada following the volcanic eruption of Mount Pinatubo in June 1991, and the strong drought in the prairies in 2001. The annual sum of positive degree-days (thawing index) has been related to the permafrost distribution. The lower values of the derived thawing index (<1400 degree-days) are related well to the presence of continuous and dense discontinuous permafrost. The observed increase in the thawing index during the 1992–2002 period represents a decrease of classified permafrost area of 7%.