Land surface water mapping is one of the most important remote-sensing applications. However, water areas are spectrally similar and overlapped with shadow, making accurate water extraction from remote-sensing images still a challenging problem. This paper develops a novel water index named as NDWI-MSI, combining a new normalized difference water index (NDWI) and a recently developed morphological shadow index (MSI), to delineate water bodies from eight-band WorldView-2 imagery. The newly available bands (e.g. coastal, yellow, red-edge, and near-infrared 2) of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations. Through our testing, a new NDWI is defined in this study. In addition, MSI, a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas. The NDWI-MSI is created by combining NDWI and MSI, which is able to highlight water bodies and simultaneously suppress shadow areas. In experiments, it is shown that the new water index can achieve better performance than traditional NDWI, and even supervised classifiers, for example, maximum likelihood classifier, and support vector machine.

The classical Least-Squares (LS) adjustment has been widely used in processing and analysing observations from Global Satellite Navigation System (GNSS). However, in detecting temporal correlations of GNSS observations, which can be described by means of autoregressive (AR) process, the LS method may not provide reliable estimates of process coefficients, since the Yule-Walker (YW) equations refer to structured Errors-In-Variables (EIV) equations. In this contribution, we proposed a Total Least-Squares (TLS) solution with the singular cofactor matrix to solve the YW equations. The proposed TLS solution is obtained based on the fact that random errors belong to column space of its cofactor matrix. In addition the proposed solution does not need any substitution of the squared true parameter vector as done by the current publications. Finally, we simulate the AR process to prove that our solution is more reliable than the existing methods.

The modern GNSS technique is one of the most effective geoscience and remote-sensing tool to observe crustal motions and quantify plate tectonics dynamics. Given multiple installed continuously operating GNSS observing stations, the multi-frame transformation is implemented to connect the timevarying GNSS coordinates by the traditional step-wise method. Compared with the step-wise treatment of each pair of frames, the proposed structured total least-squares method considers the combined estimation for all frames, guaranteeing unique and consistent results for the multi-frame symmetric transformation. Furthermore, we introduce the variance component as the nutshell and flexible indicator for land movement. The variance components can quantify the movement coordinate-wise, regional-wise, or frame-wise if the variance components are estimable as we analyze. The simulated experiment shows that the multi-frame symmetric transformation is statistically superior to the traditional stepwise treatment. For the application, the deformation caused by Tohoku earthquake that happened in 2011 in northeast Japan is analyzed.

Datum transformations are a fundamental issue in geodesy, Global Positioning System (GPS) science and technology, geographical information science (GIS), and other research fields. In this study, we establish a general total least squares (TLS) theory which allows the errors-in-variables model with different constraints to formulate all transformation models, including affine, orthogonal, similarity, and rigid transformations. Through the adaptation of the transformation models to the constrained TLS problem, the nonlinear constrained normal equation is analytically derived, and the transformation parameters can be iteratively estimated by fixed-point formulas. We also provide the statistical characteristics of the parameter estimator and the unit of precision of the control points. Two examples are given, as well as an analysis of the results on how the estimated quantities vary when the number of constraints becomes larger.

Shockwaves lead to speed variation and capacity drop, which hamper the stationarity and throughput of traffic network greatly in reality. In order to dominate or suppress shockwaves, there exist two philosophies: the analytical and numerical methods to investigate various traffic management schemes. However, both are studied completely separately in the existing literature. In this paper, we primarily focus on the uniformity and combination of the two philosophies, especially in terms of traffic evolution and shockwave trajectory extraction. Aiming at exploring the uniformity, numerical methods are equipped with gradient boundary detection and polar-parameter coordinate projection to iteratively calculate traffic states and extract shockwave trajectories as line segments. By contrast, analytical methods derive traffic evolution from fundamental diagrams and present shockwave trajectories as vector graphics in the traffic time-space diagram. Furthermore, to rationally measure the accuracy of extracted trajectories, a calibration model is established to decrease angle and distance errors yielded by the two methods. In the uncontrolled and variable speed limit (VSL)-controlled bottleneck scenarios, the simulation results have shown that: 1) both analytical and numerical methods have the capability to precisely describe traffic evolution and the effects of VSL strategies on recovering traffic throughput; 2) shockwave trajectories extracted by the two methods are coincident, with the significant reduction of relative distance errors from 15%–80% to 0.1%–3%; and 3) it is quite promising to take full advantage of both the visual/intuitive nature of analytical methods and the iterative optimization of numerical methods to investigate efficient traffic management strategies.

Water vapor is an essential element in the hydrologic and energy cycles, as well as in the climate and atmospheric circulation on Earth. Though various techniques have been developed, water vapor is still difficult to retrieve with both high accuracy and resolution. To calibrate the biases and restrain large errors in the Moderate-Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) Precipitable Water Vapor (PWV) in Western Europe, we propose a hybrid model that combines the Spherical Cap Harmonic Analysis (SCHA) model and the Extreme Gradient Boosting (XGBoost) model. This model includes two main steps (1) initial calibration of MODIS PWV using a SCHA model and (2) advanced calibration of the interim PWV using an XGBoost model. Results show that the hybrid model achieves an average bias of 0.0 mm, STD of 2.0 mm, and RMS of 2.0 mm, increasing the MODIS PWV accuracy by 55.6% in terms of RMS in Western Europe in 2020. We further demonstrate that the hybrid model outperforms the SCHA model and the XGBoost model in calibrating biases and restraining large errors. We find that the MODIS PWV typically exceeds the GNSS PWV by 0.7 mm on annual average and their difference fluctuates seasonally, varying from -4.0 mm in winter to 4.0 mm in summer. This study provides a powerful method to optimize the MODIS PWV and obtain high-quality PWV products for meteorological research and Earth observation systems.