Airborne test flight of HY-2A satellite microwave scatterometer and data analysis

This paper introduces the background, aim, experimental design, configuration and data processing for an airborne test flight of the HY-2 Microwave scatterometer(HSCAT). The aim was to evaluate HSCAT performance and a developed data processing algorithm for the HSCAT before launch. There were three test flights of the scatterometer, on January 15, 18 and 22, 2010, over the South China Sea near Lingshui, Hainan. The test flights successfully generated simultaneous airborne scatterometer normalized radar cross section(NRCS), ASCAT wind, and ship-borne-measured wind datasets, which were used to analyze HSCAT performance. Azimuthal dependence of the NRCS relative to the wind direction was nearly cos(2w), with NRCS minima at crosswind directions, and maxima near upwind and downwind. The NRCS also showed a small difference between upwind and downwind directions, with upwind crosssections generally larger than those downwind. The dependence of airborne scatterometer NRCS on wind direction and speed showed favorable consistency with the NASA scatterometer geophysical model function(NSCAT GMF), indicating satisfactory HSCAT performance.

A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices

We introduce the so-called naive tests and give a brief review of the new developments. Naive testing methods are easy to understand and perform robustly, especially when the dimension is large. We focus mainly on reviewing some naive testing methods for the mean vectors and covariance matrices of high-dimensional populations, and we believe that this naive testing approach can be used widely in many other testing problems.

Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index（NDVI） time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil–Sen median trend analysis and the Mann–Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive： regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.