The GNSS derived Zenith Tropospheric Delay (ZTD) plays today a very critical role in meteorological study and weather forecasts, as ZTDs of thousands of GNSS stations are operationally assimilated into numerical weather prediction models. Recently, the Chinese BeiDou Navigation Satellite System (BDS) was officially announced to provide operational services around China and its neighborhood and it was demonstrated to be very promising for precise navigation and positioning. In this contribution, we concentrate on estimating ZTD using BDS observations to assess its capacity for troposphere remote sensing. A local network which is about 250 km from Beijing and comprised of six stations equipped with GPS- and BDS-capable receivers is utilized. Data from 5 to 8 November 2012 collected on the network is processed in network mode using precise orbits and in Precise Point Positioning mode using precise orbits and clocks. The precise orbits and clocks are generated from a tracking network with most of the stations in China and several stations around the world. The derived ZTDs are compared with that estimated from GPS data using the final products of the International GNSS Service (IGS). The comparison shows that the bias and the standard deviation of the ZTD differences are about 2 mm and 5 mm, respectively, which are very close to the differences of GPS ZTD estimated using different software packages.

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm.

On 27 December 2012 it was announced officially that the Chinese Navigation Satellite System BeiDou (BDS) was able to provide operational services over the Asia-Pacific region. The quality of BDS observations was confirmed as comparable with those of GPS, and relative positioning in static and kinematic modes were also demonstrated to be very promising. As Precise Point Positioning (PPP) technology is widely recognized as a method of precise positioning service, especially in real-time, in this contribution we concentrate on the PPP performance using BDS data only. BDS PPP in static, kinematic and simulated real-time kinematic mode is carried out for a regional network with six stations equipped with GPS- and BDS-capable receivers, using precise satellite orbits and clocks estimated from a global BDS tracking network. To validate the derived positions and trajectories, they are compared to the daily PPP solution using GPS data. The assessment confirms that the performance of BDS PPP is very comparable with GPS in terms of both convergence time and accuracy.

Downward shortwave radiation (DSR) is a highly variable solar source on spatiotemporal basis and essential for energy and agriculture systems, while its calculations are helpful in the environment-related studies, climatology, and monitoring fire risk. Statistical methods developed to extrapolate values of climatic variables and radiation could fail to generate reliable findings of DSR over a complex terrain without considering local topographic factors. In the present study, we proposed an integrative approach of MTCLIM-XL extrapolation with remote sensing (RS) and geographic information system (GIS) to estimate real-time DSR and its spatial potential over surfaces of contrasting elevated sites on a mountainous terrain of Quetta (Pakistan).Based on methodological approach, remote sensing data product of high-resolution DEM (SRTM 30m) was processed to extract topographic data, and meteorological data were obtained from a base site, Subsequently, MTCLIM-XL executed the simulation to calculate the daily-based DSR (W/m²).Spatial distribution of DSR was generated by applying deterministic interpolation with complementing quantification of Hillshade analysis for spatially obstructive surfaces, and resultant spatial hotspot-based potential was assessed on basis of specified threshold level (above 250 W/m² = 2 kW h/m²) over the specified area. We observed usable potential of DSR at target sites and its spatial distribution during the study period of 2015 to April 2016. Using EUMETSAT CMSAF data as a standard, the validation demonstrates agreeable results of low RMSE and high correlation coefficient values for selected sites, except some sites with relatively high elevations and irregular gradients. Analysis of solar zenith angle to evaluate its inverse relation with increment in DSR values shows agreeable high inverse relation, while the negative trend for only some sites features relatively high rugged topography. In conclusion, MTCLIM-XL with RS and GIS integration manifests as a reliable approach for estimation and spatial potential assessment-based exploration of DSR over complex terrain having no ground data, while prospectively it will complement to the environment-related studies on local to mesoscale.