All existing examples of current measurements by spaceborne synthetic aperture radar (SAR) along-track (AT) interferometry (ATI) have suffered from short baselines and corresponding low sensitivities. Theoretically, the best data quality at X-band is expected at effective baselines on the order of 30 m, i.e., 30 times as long as the baselines of the divided-antenna modes of TerraSAR-X. In early 2012, we had a first opportunity to obtain data at near-optimum baselines from the TanDEM-X satellite formation. In this paper, we analyze two TanDEM-X interferograms acquired over the Pentland Firth (Scotland) with effective AT baselines of 25 and 40 m. For comparison, we consider a TerraSAR-X dual-receive-antenna (DRA)-mode interferogram with an effective baseline of 1.15 m, as well as velocity fields obtained by Doppler centroid analysis (DCA) of single-antenna data from the same three scenes. We show that currents derived from the TanDEM-X interferograms have a residual noise level of 0.1 m/s at an effective resolution of about 33 m × 33 m, while DRA-mode data must be averaged over 1000 m × 1000 m to reach the same level of accuracy. A comparison with reference currents from a 1-km resolution numerical tide computation system shows good agreement in all three cases. The DCA-based currents are found to be less accurate than the ATI-based ones but close to short-baseline ATI results in quality. We conclude that DCA is a considerable alternative to divided-antenna mode ATI, while our TanDEM-X results demonstrate the true potential of the ATI technique at near-optimum baselines.

Information about a company's environmental, social and governance (ESG) performance has become increasingly important in the decision-making process of financial institutions. The financial implications of environmental challenges (e.g. water stress), negative social impacts (e.g. health impacts in local communities) or poor corporate governance (e.g. breaching legislation) all continue to increase. Accordingly, there is a need for financial institutions to incorporate information on ESG risks, opportunities and impacts in decisions that relate to risk management, investments, credit, strategy, and reporting. ESG information is typically disseminated through ESG ratings, which combine the three constituents into a single rating, or ascribe them separate scores. The compilation of ESG ratings and the identification of appropriate data sources is an inherently complex process; as such, there is no single standard for data collection or reporting. This has led to a divergence in the underlying data sources used by different rating providers, as well as in the determination of factors that are deemed worthy of measurement in the first place. For example, when assessing a company's environmental impact, one rating provider may rely on company-provided data, while another may incorporate independent third-party assessments. Unfortunately, there is currently no clear mechanism for effectively resolving such disagreements to establish a standardised approach to ESG rating assessments. However, geospatial data and analyses offer several key advantages for ESG assessments, including consistency, the potential for enhanced accuracy, and the ability to identify and assess environmental impacts at a detailed physical asset level, in addition to evaluating the broader spatial context. By incorporating geospatial information (obtained through manually processing remotely sensed data, or by using existing products) rating methodologies can be improved, and disparities can be addressed more effectively. This would enable a more comprehensive understanding of the environmental considerations of ESG assessments, promoting a more informed and precise decision-making process. Within this context, a few institutions (e.g. the University of Oxford, the WWF, and a few others) are pioneering thought leadership around spatial finance, including the assessment of ESG issues utilising geospatial intelligence, but there are no consistent frameworks for incorporating geospatial data into ESG ratings and analysis. This paper explores the opportunity for such a geospatial environmental scoring framework, defining a variety of methods in which open data with broad geographic coverage could be incorporated into ESG analysis, generalisable to a range of assets and sectors. The proposed framework is organised into two categories: localised effects, which directly impact the immediate vicinity of an asset, and delocalised effects, which contribute to global climate change and atmospheric pollution. Sub-scores are defined within these categories, which capture both the localised effects on land use, biodiversity, soils, and hydrology, and the global impacts resulting from atmospheric emissions. The approaches for handling geospatial data to generate both these sub-scores and the final E-score are presented, including a test case, and the complete methodology is made available in open repositories.

This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

This paper evaluates the potential of spaceborne bistatic interferometric synthetic aperture radar images for the monitoring of biophysical variables in wetlands, with a special interest on paddy rice. The assessment is made during the rice cultivation period, from transplanting to harvesting time (May to October) for fields around Gala lake (Turkey), one of the largest and most productive paddy rice planting area in the country. Detailed ground truth measurements describing biophysical parameters are collected in a dedicated campaign. A stack of 16 dual-pol TanDEM-X images is used for the generation of 32 digital elevation models (DEMs) over the studied area. The quality of the data allows the use of the interferometric phase as a state variable capable to estimate crop heights for almost all the growing stages. The early vegetative rice stage, which is characterized by flooded fields, cannot be represented by the interferometric phase due to a low signal-to-noise ratio but can be easily detected by amplitude and interferometric coherence thresholding. A study on the impact of the polarization in the signal backscatter is also performed. An analysis of the differences between HH and VV DEMs shows the varying signal penetration for the two polarizations at different growing stages. The validation with reference data demonstrates the capability to establish a direct relationship between interferometric phase and rice growth. The very high coherence of TanDEM-X data yields elevation estimates with root-mean-square error in a decimetric level, supporting temporal change analysis on a field-by-field basis.

Since July 2010, TerraSAR-X and TanDEM-X jointly acquire interferometric data. Starting their common commissioning phase with a so called pursuit monostatic configuration with 3 seconds time lag between the two passes, they were later put in a close formation in October 2010, acting since then as the first freely configurable bistatic SAR interferometer in space. All operational acquisitions were processed from instrument raw data to DEMs from day one of the data taking on by one single processing system: the Integrated TanDEM-X Processor (ITP) (see [1],[2]). Data take analysis, common parameter calculation, synchronization, bistatic focusing, filtering, co-registration, phase unwrapping and geocoding are all performed in one sequence inside this processor. This approach allows a high precision processing by passing all applied corrections and determined parameters from one step to the next. Specifically the geometric & phase accuracy and stability of the instruments, the processor and the auxiliary data (i.e. the millimetric precision of the baseline products) provide an unprecedented level of relative and absolute geometric accuracy in the bistatic operation. While many challenges of bistatic processing of the TanDEM-X data are encountered, the benefits of this single pass acquisition mode can be used to derive additional information from the data itself for further processing and calibration. In this paper, we will outline the bistatic interferometric processing steps of the ITP and focus on the aspects of geometric accuracy in DEM generation.