Snow water equivalent (SWE) is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

China’s socioeconomic transitions have dramatically accelerated its economic growth in last three decades, but also companioned with continuous environmental degradation. This study will advance the knowledge of heavy metal water pollution in China from a spatial–temporal perspective. Specifically, this study addressed the following: (1) spatial patterns of heavy metal water pollution levels were analyzed using data of prefecture-level cities from 2004 to 2011; and (2) spatial statistical methods were used to examine the underlying socioeconomic and physical factors behind water pollution including socioeconomic transitions (industrialization, urbanization, globalization and economic development), and environmental characteristic (natural resources, hydrology and vegetation coverage). The results show that only Cr pollution levels increased over the years. The individual pollution levels of the other four heavy metals, As, Cd, Hg, and Pb, declined. High heavy metal water pollution levels are closely associated with both anthropogenic activities and physical environments, in particular abundant mineral resources and industrialization prosperity. On the other hand, economic development and urbanization play important roles in controlling water pollution problems. The analytical findings will provide valuable information for policy-makers to initiate and adjust protocols and strategies for protecting water sources and controlling water pollution; thus improving the quality of living environments.

China’s rapid economic growth and social transitions have deteriorated environmental conditions and caused further public health issues in last three decades. This study examines the complex mechanisms of how socioeconomic transitions and physical environmental conditions impact public health, especially with respect to increasing cancer incidences in mainland China from a spatial-temporal perspective. Specifically, (1) spatial variations of seven types of cancer incidences were analyzed in relation to heavy metal emissions from industrial wastewater at the prefecture-level city scale from 2004 to 2009. Additionally; (2) spatial statistical methods were employed to explore the associations between health outcome, heavy metal emissions from industrial wastewater (arsenic, chromium, cadmium, mercury, lead), as well as socioeconomic transitions (industrialization, urbanization, globalization) and physical environmental factors (hydrology and vegetation coverage). Results showed a significant increase of cancer incidences between 2004 and 2009. Consistent with the spatial pattern of heavy metal emissions, cancer patient clusters were identified in both traditional industrial bases and newly industrialized economic zones, especially in major cities located at downstream watersheds, including Beijing, Shanghai, Guangzhou, Shenyang, and Wuhan. The results also revealed the double-edged effects of industrialization, economic growth, and urbanization on natural environment and human health. The findings provide informative knowledge of heavy metal pollution and cancer outbreaks in China and therefore offer valuable reference for authorities formulating regulations.