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Persistent Scatterer Interferometry (PSI) is a well-established technique for monitoring millimetre deformation of the Earth’s surface. The availability of free and open SAR data with a repeat cycle of 6–12 days from the Copernicus mission Sentinel-1, allows PSI to be used complementary to traditional surveying techniques. Whilst the data resolution may not allow a precise determination of the geolocation of the occurring deformation, observed deformation patterns can be analysed with auxiliary data and often show correlation with the location of geophysical processes or human activities. In this paper, we investigate the particular case of the church tower of Bad-Frankenhausen in the north of the Free State Thuringia, Germany, with PSI processing of Sentinel-1 data. Both pass directions (descending and ascending) are considered, and different motion models are tested in order to retrieve the most accurate displacement pattern around the church location. Deformation up to −6 mm/yr are observed near the church location for the period 2016–2019 in ascending direction.

This study explores the potential of Sentinel-1 time series for crop monitoring. In this study an attempt was made to use Copernicus Sentinel-1 time-series (backscatter & coherence) to map differences in crop development (growth) within fields, to define the main factors affecting radar backscatter, and to learn about plant-relevant parameters with the aim to fill gaps in Copernicus Sentinel-2 time-series with relevant spatial information for crop monitoring.

This study explores the potential of Sentinel-1 time series to map field heterogeneities as an additional information into optical vegetation index products into operational farming practices. The presentation explains the data processing strategies as well as their analysis and validation to calculate the crop parameters. First results on the influence of different spatio-temporal speckle filters on the time series evaluation and the derivation of a first radar vegetation index are presented as well as the characteristics of field heterogeneities that were mapped with own drone surveys.

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas.

This paper proposes a method for quality assessment of synthetic aperture radar (SAR) scenes to facilitate selection of a master scene for persistent scatterer interferometry (PSI). Parameters like perpendicular and temporal baseline or weather conditions at acquisition time are evaluated using a scoring system. By defining scoring rules every available information can be used for rating image quality thus helping to make a quick decision to find the best possible scene in a large data stack. The method can be further improved by weighting the single results as needed. The approach is successfully tested on an exemplary data stack containing 57 SAR images of ERS.

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.