Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.

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In this paper, universal kriging with linear trend is used to interpolate the strain tensor elements over a region along San Andreas Fault in California. The main goal of this paper is to improve the ordinary kriging interpolation results. A 7-year time series (2006)(2007)(2008)(2009)(2010)(2011)(2012) of 12 permanent stations is utilized to obtain the coordinate changes in UTM coordinates system and calculate the strain tensor elements by means of finite difference method. Comparing the results we can find an improvement about 40 % for universal kriging at critical points in which ordinary kriging can't be appropriate method of interpolation.

One of the main concerns during DEM evaluation is the number of ground control points (GCP). Accordingly, in this paper, a new method is proposed for calculating the appropriate number of GCPs for DEM evaluation based on a Confidence Interval (CI) of RMSE. Then, the method is employed to determine the CI of the estimated vertical accuracy of AW3D30, SRTM and ASTER GDEM Free 30m resolution global DEMs in mountainous, hilly, flat and urban regions of two study areas. To provide a more reliable estimation of errors, robust statistical methods including Median, Normalized Median Absolute Deviation (NMAD) and Huber's µ and σ are also investigated. Furthermore, a new formulation is developed to analyse propagation of the errors in slope and aspect products of DEM. The results showed that to evaluate the accuracy of AW3D30, ASTER GDEM and SRTM with a CI of ±1m and the probability of 99%, in the study area, a minimum number of 2110, 1483 and 750 GCPs are required, respectively. The results also showed that in the flat, hilly and mountainous study areas AW3D30 is the most accurate DEM. However, SRTM fits better to the urban study area. Finally, the results of the error propagation analysis illustrates that the slope and aspect errors bear a striking relation to the surface gradient.

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