It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.

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The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle

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Remote sensing of the environment

. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980–2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003–2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011–2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land–atmosphere feedbacks.

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GLEAM v3: satellite-based land evaporation and root-zone soil moisture

[1] Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer-EOS (AMSR-E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85-GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002–2006) between ASI and NASA-Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.

Microwave Remote Sensing

A set of Mie functions has been developed in MATLAB to compute the four Mie coefficients an, bn, cn and dn, efficiencies of extinction, scattering, backscattering and absorption, the asymmetry parameter, and the two angular scattering functions S1 and S2. In addition to the scattered field, also the absolute-square of the internal field is computed and used to get the absorption efficiency in a way independent from the scattered field. This allows to test the computational accuracy. This first version of MATLAB Mie Functions is limited to homogeneous dielectric spheres without change in the magnetic permeability between the inside and outside of the particle. Required input parameters are the complex refractive index, m= m’+ im”, of the sphere (relative to the ambient medium) and the size parameter, x=ka, where a is the sphere radius and k the wave number in the ambient medium. 1 Equation on p. 16 corrected, April 2006

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MATLAB Functions for Mie Scattering and Absorption

(1987). Applications of the interaction of microwaves with the natural snow cover. Remote Sensing Reviews: Vol. 2, No. 2, pp. 259-387.

Applications of the interaction of microwaves with the natural snow cover

Microwave Emission Model of Layered Snowpacks

The relative dielectric constant, or relative permittivity, /spl epsiv/ of dry snow, is independent of frequency from about 1 MHz up to the microwave range of at least 10 GHz. New measurements of with improved accuracy were made with a specially designed resonator operating near 1 GHz. The coaxial sensor accurately defines the sample volume whose actual mass can be determined to give the density of the snow sample. A special electronic instrument, called a resometer, enabled accurate and rapid measurements under field conditions. Some 90 measurements of different kinds of dry snow (fresh, old, wind-pressed snow, depth hear, and refrozen crusts) were made at test sites in the Swiss and Austrian Alps. The data indicate that /spl epsiv/ is a function of snow density only, given that the standard deviation of 0.006 from the fitted curve is just due to the expected measurement errors. The interpretation of these data in terms of physical mixing theory favors the effective medium formula of Polder and van Santen (1946). The data allow to relate the average axial ratio X as a function of ice volume fraction. Both prolate and oblate spheroids can explain the data. Independent reasoning gives preference to oblate particles. In both cases, the axial ratio increases with increasing fraction up to a critical value of 0.33, followed by a decrease at still higher fractions. The destructive metamorphism of slowly compacting snow explains the increase of X, while the following decrease might be due to sintering. So far, no effect on /spl epsiv/ by a liquid-like surface layer on the ice grains at temperatures between -10/spl deg/C and 0/spl deg/C has been observed.

Microwave permittivity of dry sand

So far, knowledge about the dielectric properties, especially the loss factor, of ice at microwave frequencies has been unsatisfactory. In this work the authors report on new measurements made over the frequency range from 2 to 100 GHz by a resonator method (2-10 GHz) and a radiometer method (10-100 GHz). Measurements were made with pure and with slightly saline (10 to 13 p.p.m.) ice. The results agree with the assumption of a single minimum of the dielectric loss at 2 to 4 GHz. For pure ice the data are a natural link between the measurements of Westphal made below 1 GHz and the far-infrared spectrum. The influence of small impurities on the dielectric loss is compared with the behaviour of sea ice, and it is found that the same linear relationship with salinity can be applied to both cases.

https://iopscience.iop.org/article/10.1088/0022-3727/21/11/522/pdf

Christian Mätzler

Papers

MATLAB Functions for Mie Scattering and Absorption

Applications of the interaction of microwaves with the natural snow cover

Microwave Emission Model of Layered Snowpacks

Microwave permittivity of dry sand

Dielectric properties of freshwater ice at microwave frequencies