Land subsidence associated with overexploitation of aquifers is a hazard that commonly affects large areas worldwide. The Lorca area, located in southeast Spain, has undergone one of the highest subsidence rates in Europe as a direct consequence of long-term aquifer exploitation. Previous studies carried out on the region assumed that the ground deformation retrieved from satellite radar interferometry corresponds only to vertical displacement. Here we report, for the first time, the two- and three-dimensional displacement field over the study area using synthetic aperture radar (SAR) data from Sentinel-1A images and Global Navigation Satellite System (GNSS) observations. By modeling this displacement, we provide new insights on the spatial and temporal evolution of the subsidence processes and on the main governing mechanisms. Additionally, we also demonstrate the importance of knowing both the vertical and horizontal components of the displacement to properly characterize similar hazards. Based on these results, we propose some general guidelines for the sustainable management and monitoring of land subsidence related to anthropogenic activities.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.

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Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

The understanding of land surface-atmosphere energy exchange is extremely important for predicting climate change and weather impacts, particularly the influence of soil moisture content (SMC) on hydrometeorological and ecological processes, which are also linked to human activities. Unfortunately, traditional measurement methods are expensive and cumbersome over large areas, whereas measurements from satellite active and passive microwave sensors have shown advantages for SMC monitoring. Since the launch of the first passive microwave satellite in 1978, more and more progresses have been made in monitoring SMC from satellites, e.g., the Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions in the last decade. Recently, new methods using signals of opportunity have been emerging, highlighting the Global Navigation Satellite Systems-Reflectometry (GNSS-R), which has wide applications in Earth’s surface remote sensing due to its numerous advantages (e.g., revisiting time, global coverage, low cost, all-weather measurements, and near real-time) when compared to the conventional observations. In this paper, a detailed review on the current SMC measurement techniques, retrieval approaches, products, and applications is presented, particularly the new and promising GNSS-R technique. Recent advances, future prospects and challenges are given and discussed.

The quality of geodetic networks for guiding Tunnel Boring Machines (TBMs) inside long tunnels depends largely on the correct use of a gyroscope. These networks are based on a series of control points at the tunnel entrance, and link each station by means of survey observations as they advance along the tunnel. Once, the networks are used to guide the TBM, they are no longer checked again. It is necessary to perform high accuracy astronomical observations to stars in order to determine the gyrotheodolite constant. Since astronomical observations cannot be made inside tunnels, geodetic azimuths have to be used for the computations. However, these azimuths cannot theoretically be compared with the astronomical azimuths obtained by the gyrotheodolite. An alternative is to compute the instrument constant using the valúes of the deviation of the vertical derived from a geoid model. That is the approach used in this work where a methodology for the design of underground networks in long tunnels is also presented. This procedure has been implemented during the construction of the Guadarrama and Pajares high-speed railway tunnels (Spain).

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.

Spatial Analysis has been used since the early 1990's for rural cadastral models. Nowadays, it is necessary to use Geographic Information Systems (GIS), Global Positioning Systems (GPS) and Cartography as well as dedicated software in order to solve problems in Cadastral Models. Among these models, the selection and priorítization of the parameters affecting the rural property valuation assets, as well as the zoning optimization (homogeneous sectors) within a community, are one of the biggest concems for Cadastral Agencies. This research is focused on rural property in the Región of Murcia, in accordance with data recorded from 2007 to 2009. The techniques used for the spatial zoning were carried out by means of IDW (Inverse Distance Weighting) and kriging interpolators as they are the most widely used in spatial varíation analysis studies. Although both interpolators produced similar results, IDW was better for predicting varíation in "Unit Príces". Afterwards, a statistical study was later completed using ANOVA, Chi-Squared and Correspondence Analysis procedures. The research results demónstrate the consistency of the parameters that have been used in the Cadastral Agencies, and the degree of involvement of these parameters in assessing the results of unit pnces for different áreas. These results are secured by the verification techniques appüed Keywords Geographicinformation system (GIS) • Spatialinterpolation • IDW-Kriging • Multivariate statistical • Cadastre