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The near-surface soil freeze–thaw (FT) transition is an important factor affecting land-atmosphere exchanges, hydrology and carbon cycles. Thus, effectively monitoring the temporal–spatial changes of soil FT processes is crucial to climate change and environment research. Several approaches have been developed to detect the soil FT state from satellite observations. The discriminant function algorithm (DFA) uses temperature and emissivity information from Advanced Microwave Scanning Radiometer Enhanced (AMSR-E) passive microwave satellite observations. Although it is well validated, it was shown to be insufficiently robust for all land conditions. In this study, we use in-situ observed soil temperature and AMSR-E brightness temperature to parameterize the DFA for soil FT state detection. We use the in-situ soil temperature records at 5 cm selected from available dense networks in the Northern Hemisphere as a reference. Considering the distinction between ascending and descending orbits, two different sets of parameters were acquired for each frequency pair. The validation results indicate that the overall discriminant accuracy of the new function can reach 90%. We further compared the Advanced Microwave Scanning Radiometer 2 discriminant results using the new function to the Soil Moisture Active Passive freeze/thaw product, and a reasonable consistency between them was found. [ABSTRACT FROM AUTHOR]

Based on Argo sea surface salinity (SSS) and the related precipitation (P), evaporation (E), and sea surface height data sets, the climatological annual mean and low-frequency variability in SSS in the global ocean and their relationship with ocean circulation and climate change were analyzed. Meanwhile, together with previous studies, a brief retrospect and prospect of seawater salinity were given in this work. Freshwater flux (E-P) dominated the mean pattern of SSS, while the dynamics of ocean circulation modulated the spatial structure and low-frequency variability in SSS in most regions. Under global warming, the trend in SSS indicated the intensification of the global hydrological cycle, and featured a decreasing trend at low and high latitudes and an increasing trend in subtropical regions. In the most recent two decades, global warming has slowed down, which is called the "global warming hiatus". The trend in SSS during this phase, which was different to that under global warming, mainly indicated the response of the ocean surface to the decadal and multi-decadal variability in the climate system, referring to the intensification of the Walker Circulation. The significant contrast of SSS trends between the western Pacific and the southeastern Indian Ocean suggested the importance of oceanic dynamics in the cross-basin interaction in recent decades. Ocean Rossby waves and the Indonesian Throughflow contributed to the freshening trend in SSS in the southeastern Indian Ocean, while the increasing trend in the southeastern Pacific and the decreasing trend in the northern Atlantic implied a long-term linear trend under global warming. In the future, higher resolution SSS data observed by satellites, together with Argo observations, will help to extend our knowledge on the dynamics of mesoscale eddies, regional oceanography, and climate change. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) has important influences on regional and global climate change. Here, we perform an in‐depth study of the relationship between cloud parameters and meteorological factors over the TP. The spatiotemporal variations in cloud cover and cloud optical thickness over the TP during the daytime from 2003 to 2015 are analysed using the Aqua‐MODIS level 2 atmospheric product data MYD06. Results show that the annual average cloud cover over the TP decreases from the southeast to the northwest. The cloud cover of the western TP is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. The cloud covers in most areas of the TP exceed 30% in spring and summer, and the cloud optical thickness in the southeastern TP exceeds 10 in summer, with substantial cloud cover and cloud optical thickness changes. Compared to other seasons, the cooling effect of the near surface cloud net radiative forcing is greater during daytime in summer, which is likely associated with thick cloud optical thickness or large cloud cover. The results of the analysis of relationships among cloud cover, water vapour and air temperature show that positive correlations exist between cloud cover and water vapour, and that significantly negative correlations exist between cloud cover and air temperature over the TP. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. The cloud cover of the western Tibetan Plateau (TP) is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. [ABSTRACT FROM AUTHOR]

In the development of wave scattering models for randomly dielectric rough surfaces, it is usually assumed that the Fresnel reflection coefficients could be approximately evaluated at either the incident angle or the specular angle. However, these two considerations are only applicable to their respective regions of validity. A common question to ask is what are the conditions under which we would choose one or the other of these two approximations? Since these approximations are basically roughness-dependent, how can we handle the in-between cases where neither is appropriate? In this paper, a physical-based transition function that naturally connects these two approximations is proposed. The like-polarized backscattering coefficients are evaluated with the model and are compared with those calculated with a moment method simulation for both Gaussian and non-Gaussian correlated surfaces. It is found that the proposed transition function provides an excellent prediction for the backscattering coefficient in the frequency and angle trends.

Algorithms for estimating dry snow density and the dielectric constant and roughness of the underlying soil or rock use backscattering measurements with VV and HH polarization at L-band frequency (1.25 GHz). Comparison with field measurements of snow density during the first SIR-C/X-SAR overpass shows absolute accuracy of 42 kg [m.sup.-]3 (13% relative error). For the underlying soil, comparisons with the ground scatterometer measurements showed errors of 4% by volume for soil moisture estimation and 4 mm for the surface root mean square (RMS) height. Values of snow density and the properties of the underlying soil are necessary for the estimation of snow water equivalence. Index Terms--Dielectric constant, L-Band radar, polarization, snow density, subsurface roughness.

The relationship between snow water equivalence (SWE) and SAR backscattering coefficients at C- and X-band (5.5 and 9.6 GHz) can be either positive or negative. Therefore, discovery of the relationship with an empirical approach is unrealistic. Instead, we estimate snow depth and particle size using SIR-C/X-SAR imagery from a physically-based first order backscattering model through analyses of the importance of each scattering term and its sensitivity to snow properties. Using numerically simulated backscattering values, we develop semi-empirical models for characterizing the snow--ground interaction terms, the relationships between the ground surface backscattering components, and the snowpack extinction properties at C-band and X-band. With these relationships, snow depth and optical equivalent grain size can be estimated from SIR-C/X-SAR measurements. Validation using three SIR-C/X-SAR images shows that the algorithm performs usefully for incidence angles greater than 30 [degrees], with root mean square errors (RMSEs) of 34 cm and 0.27 mm for estimating snow depth and ice optical equivalent particle radius, respectively. Index Terms--Multifrequency, multipolarization, snow grain size, snow water equivalence.

The simulations of emissivities from a two-dimensional (2-D) wet soil with random rough surfaces are studied with numerical solutions of three-dimensional (3-D) Maxwell equations. The wet soils have large permittivity. For media with large permittivities, the surface fields can have large spatial variations on the surface. Thus, a dense discretization of the surface is required to implement the method of moment (MoM) for the surface integral equations. Such a dense discretization is also required to ensure that the emissivity can be calculated to the required accuracy of 0.01 for passive remote sensing applications. It has been shown that the physics-based two-grid method (PBTG) can efficiently compute the accurate surface fields on the dense grid. In this paper, the numerical results are calculated by using the PBTG in conjunction with the sparse-matrix canonical grid method (SMCG). The emissivities are illustrated for random rough surfaces with Gaussian spectrum for different soil moisture conditions. The results are calculated for L- and C-bands using the same physical roughness parameters. The numerical solutions of Maxwell's equations are also compared with the popular H and Q empirical model. Index Terms--Electromagnetic scattering by rough surfaces, Method of Moments (MoM), remote sensing, soil moisture.