The India‐Eurasia collision zone is the largest deforming region on the planet; direct measurements of present‐day deformation from Global Positioning System (GPS) have the potential to discriminate between competing models of continental tectonics. But the increasing spatial resolution and accuracy of observations have only led to increasingly complex realizations of competing models. Here we present the most complete, accurate, and up‐to‐date velocity field for India‐Eurasia available, comprising 2576 velocities measured during 1991–2015. The core of our velocity field is from the Crustal Movement Observation Network of China‐I/II: 27 continuous stations observed since 1999; 56 campaign stations observed annually during 1998–2007; 1000 campaign stations observed in 1999, 2001, 2004, and 2007; 260 continuous stations operating since late 2010; and 2000 campaign stations observed in 2009, 2011, 2013, and 2015. We process these data and combine the solutions in a consistent reference frame with stations from the Global Strain Rate Model compilation, then invert for continuous velocity and strain rate fields. We update geodetic slip rates for the major faults (some vary along strike), and find that those along the major Tibetan strike‐slip faults are in good agreement with recent geological estimates. The velocity field shows several large undeforming areas, strain focused around some major faults, areas of diffuse strain, and dilation of the high plateau. We suggest that a new generation of dynamic models incorporating strength variations and strain‐weakening mechanisms is required to explain the key observations. Seismic hazard in much of the region is elevated, not just near the major faults.

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The noise characteristics of the Global Navigation Satellite System (GNSS) position time series can be biased by many factors, which in turn affect the estimates of parameters in the deterministic model using a least squares method. The authors assess the effects of seasonal signals, weight matrix, intermittent offsets, and Helmert transformation parameters on the noise analyses. Different solutions are obtained using the simulated and real position time series of 647 global stations and power law noise derived from the residuals of stacking solutions are compared. Since the true noise in the position time series is not available except for the simulated data, the authors paid most attention to the noise difference caused by the variable factors. First, parameterization of seasonal signals in the time series can reduce the colored noise and cause the spectral indexes to be closer to zero (much “whiter”). Meanwhile, the additional offset parameters can also change the colored noise to be much “whiter” and more offsets parameters in the deterministic model leading to spectral indexes closer to zero. Second, the weight matrices derived from the covariance information can induce more colored noise than the unit weight matrix for both real and simulated data, and larger biases of annual amplitude of simulated data are attributed to the covariance information. Third, the Helmert transformation parameters (three translation, three rotation, and one scale) considered in the model show the largest impacts on the power law noise (medians of 0.4 mm−k/4 and 0.06 for the amplitude and spectral index, respectively). Finally, the transformation parameters and full-weight matrix used together in the stacking model can induce different patterns for the horizontal and vertical components, respectively, which are related to different dominant factors.

Global navigation satellite systems (GNSSs) have been widely used in navigation, positioning, and timing. China's BeiDou Navigation Satellite System (BDS) would reach full operational capability with 24 Medium Earth Orbit (MEO), 3 Geosynchronous Equatorial Orbit (GEO) and 3 Inclined Geosynchronous Satellite Orbit (IGSO) satellites by 2020 and would be an important technology for the construction of Digital Earth. This chapter overviews the system structure, signals and service performance of BDS, Global Positioning System (GPS), Navigatsionnaya Sputnikovaya Sistema (GLONASS) and Galileo Navigation Satellite System (Galileo) system. Using a single GNSS, positions with an error of~10 m can be obtained. To enhance the positioning accuracy, various differential techniques have been developed, and GNSS augmentation systems have been established. The typical augmentation systems, e.g., the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the global differential GPS (GDGPS) system, are introduced in detail. The applications of GNSS technology and augmentation systems for space-time geodetic datum, high-precision positioning and location-based services (LBS) are summarized, providing a reference for GNSS engineers and users.

Precision agriculture needs surface soil moisture information accurately and quickly. Hyper-spectral remote sensing can be used to detect slight differences in soil moisture because of high-resolution and multi-band in spectral. Taking black soil in Jilin Province of China as the research object, this paper analyzed soil hyper-spectral characteristics and extracted parameters by using methods of spectral differentiation, feature-band extraction and multiple stepwise regression analysis. The relationship between soil moisture and soil spectra was analyzed combining with soil moisture laboratory measurement. Five models were used to quantitative inversion in order to seek soil moisture monitoring by applying hyper-spectral remote sensing. The conclusion was as follows: in the below field water holding capacity condition, the sensitive bands of black soil spectral reflectance and its reciprocal and logarithmic transformation mainly focused in 400-470nm, 1950-2050nm and 2100-2200nm. The highest correlation coefficient between laboratory spectral data and soil moisture reached to 0.89 at 2156nm; The best prediction model of black soil moisture content by using hyper-spectral remote sensing was Y = 22.16 + 26278.2x 1328 47785.1x 1439 201.42x 1742 + 49306.34x 2156 , x (lgR)', Y representing soil moisture content (%), coefficient of determination was 0.931.