Floods are natural hazards with severe impacts, and their frequency and intensity are increasing due to climate change. Synthetic Aperture Radar (SAR) satellites are widely used for flood mapping, as they operate independently of weather and time of day. This thesis examines the potential of SAR-based flood monitoring through a case study of the July 2021 flood in Limburg, the Netherlands. Comparing Capella Space on-demand X-Band imagery (sub-meter resolution) with Sentinel-1 open-source C-Band imagery (5 m x 20 m resolution). The Water-Land Boundary was determined by estimating flood extent and water levels, with multiple methods evaluated for the distinct products from both data sources. Capella Space provided a single image acquisition at the flood’s peak, with a thresholding method used to classify flooded pixels. For Sentinel-1, an Amplitude Time Series Analysis (ATSA) was applied to data from 2017 to 2024 to identify flood-related outliers. The water levels are estimated from flood extent edges with the national LiDAR DEM (AHN4).
Evaluation of the modeled results using an error matrix at the acquisition time showed that 67% and 68% of the pixels were correctly classified from the flood extents derived from Capella Space and Sentinel-1, respectively. The maximum flood extent from Sentinel-1 data decreased to 45% correct classification when compared to the modeled results at the peak of the flood. This is consistent with the acquisition times, which were taken before and two days after the flood peak, missing the peak flood moment. SAR-based water levels showed an overall precision of 0.141 m for Capella Space and 0.156 m for Sentinel-1. Agreement with water level gauge measurements was better in flatter, less vegetated areas and lower in steep, vegetated areas. Achieving consistent 20 cm water level accuracy (as required by the Dutch Ministry of Infrastructure and Water Management), across the study area remains complex. Both methods are prone to false positives and negatives, especially in areas with steep slopes, narrow canals, high vegetation, or roads. False classifications result in inaccurate flood extents, thus decreasing water level accuracy. Higher resolution of Capella Space images provided better alignment with the maximum flood extent, while Sentinel-1 images have wider coverage but missed the timing of the flood peak. In the end, the choice of SAR-system depends on timing, surface characteristics, and mapping extent needs.

Over the past three decades, synthetic aperture radar (SAR) interferometry (InSAR) has become one of the most important Earth observation technologies in the world, and its use has become common in applications such as topographic mapping, monitoring earthquakes and volcanoes, as well as the built environment. Despite these advances, many technical and scientific challenges remain unsolved in the field, which prevent its use across diverse regions and biomes. One such type of region are wetlands and peatlands, which are notoriously challenging to monitor remotely due to poor signal quality and rapidly changing conditions between SAR acquisitions. This problem is particularly relevant in the Netherlands, because a significant portion of the country is composed of drained peat and clay soils which lie below sea level. These “soft soils” exhibit highly dynamic temporal behaviour that is closely linked to the phreatic groundwater system. In addition, they also exhibit a slow, irreversible subsidence caused by compaction and oxidation, the latter of which is a greenhouse gas (GHG) emitting process. It is this slow, irreversible subsidence component which scientists, governments, farmers and other stakeholders are trying to better understand, and evaluate the risks it poses. Previous efforts in monitoring the cultivated soft soil regions of the Netherlands by InSAR have been hampered by two main problems, which in this work are referred to as “cycle slips” and “loss-of-lock”. The former refers to consistent errors made in ambiguity resolution due to signals which exhibit such highly dynamic behaviour that standard algorithms cannot correctly interpret the wrapped phase data. The latter term refers to a permanent and irreparable loss of coherence in an interferometric SAR data stack. It is common in peatland regions for coherence levels to rise and fall seasonally, and in general, no coherent interferometric combination exists between the coherent periods. This condition means that the interferometric time series is severed during these incoherent periods, and only intermittent, disconnected temporal subsets of data are useable...@en

Fire both shapes and destroys forests. Forest fires are therefore essential to ecological processes and vegetation as we know them. With climate change, forest fires are expected to increase in severity and frequency. To maintain the functioning of the forests, it is important to understand the vegetation response to and recovery from forest fires. Within this field of study, remote sensing techniques are common, and optical indices such as the NDVI are most prevalent.

With recent developments, the ASCAT variables slope and curvature have become of increasing interest in structural vegetation monitoring. These variables are a second-order Taylor polynomial’s first and second derivatives used to normalise the ASCAT backscatter-incidence angle relationship. This study explored the possibility to use these novel variables in forest fire research.
The focus was to discover to what extent the variables responded to a major forest fire.

To do so, grid points affected during the 2009 Australian Black Saturday Fires are compared to unaffected control grid points utilizing Z-scores. These control grid points have been selected based on time series similarity in the two years before the fire. Time series from 2007 to 2021 are used to investigate fire impact and recovery. The ASCAT variables are compared to a similar
NDVI time series to aid in interpreting the results.

The findings show that both slope and curvature are sensitive to the major forest fire. Both variables show an impact shortly after the fire, which can be explained by the loss of scattering elements in the vegetation due to the fire. In the following years, there is a notable recovery which can be explained by the vegetation regrowth forming new scatterers. The NDVI showed similar
behaviour but the recovery was differently timed, suggesting that the signal recovery is driven by something else or that the regrowth of leaves is different from the regrowth of the structural elements that the ASCAT variables represent.

The results help in understanding the ASCAT variables and their interpretation in terms of vegetation scatterers. Especially for the curvature, the clear change in signal deflects the discussion of whether the variable has information potential. Nevertheless, for the ASCAT variables to be applicable in forest fire research, they need to be better understood and additional research is necessary. Suggestions are for example a ground validation study or a global forest fire study, which would suit the coarse resolution of the ASCAT variables better and improve the understanding of the interaction between forest fires and the variables.

Although using the ASCAT variable for forest fire research is in its infancy, the results about its suitability are promising, both for ASCAT product development, as well as for the forest fire research field. Exploring the possibilities further is worthwhile, especially considering the fact that the slope and curvature time series cover a long continuous period starting as early as 1991. This long time series makes the ASCAT variables suitable for long-term forest fire monitoring, and the daily nature of the data might also make them interesting for short-term monitoring.

This thesis developed a forward model for Sentinel-1 C-band co-pol and cross-pol backscatter and coherence using crop biophysical variables including leaf area index, tops weight, surface soil moisture and root zone soil moisture as inputs for sugarbeet. These input variables are simulated using a crop model called Decision Support System for Agrotechnology Transfer (DSSAT). The prediction of SAR signals is conducted using random forest regression model across all the sugarbeet fields in Noord-Brabant, the Netherlands. The correlation between simulated variables and the C-band SAR observables is investigated, as well as an evaluation of the effect of different feature combinations.

How to deal with the presence of weather affected data is an unavoidable topic in the processing of optical imagery. Clouds and cloud shadows significantly alter the spectral signatures obtained from satellite data, which often leads to problems for any kind of scientific analysis. In this research there has been elaborated on two different kind of problems: The detection of clouds and cloud shadows and the mitigation of the effect caused by cloud shadows. Most of existing operational cloud detection algorithms are so-called rule-based. Their performance is highly variable and they have their limitations. A new promising research was done by Mohajerani and Parvaneh (2019), where a convolutional neural network (CNN) named ’Cloud-Net’ was developed. In this study we have elaborated on this CNN, by converting the analysis to Sentinel-2 data and making significant modifications on the model setup. The results have been compared to the ESA Scene Classification Map (SEN2COR algorithm). It was found that for the detection of clouds the overall CNN accuracy outperforms the ESA Scene Map (95.6% vs. 92.0% respectively). For the detection of cloud shadows the modified Cloud-Net model also gave better results (90.4% vs. 84.4%). Previous work on cloud shadow correction algorithms show rather complex and inconvenient methods, where the only goal was to remove the effect of the shadow. If one is interested to also correct for illumination effects, to make it more aligned to a predetermined ground truth, new possibilities arise which allows for simpler and more direct methods. Two proposed methods have been investigated in this study. The first method, called ’decomposition of components’, investigated the use of a single formula. The affected cloud shadow pixel is corrected based on the RGB difference with a ground truth image, and a single correction factor that was determined based under the assumption that cloud shadows cause a homogeneous alteration effect in a small area. The second method, called the ’CNN based method’, presents a totally new idea by changing the Cloud-Net model to a regression model, in order to correctly alter cloud shadow affected pixels. The performance of both methods was quantified by the structural similarity index measure (SSIM). It was found that the decomposition of components method has the most potential, showing significant improvements on the correction of cloud shadow affected areas.

From three coherent SAR images it is possible to estimate three interferograms. Combined in a circular way, the sum of the three interferometric phases is called the closure phase which necessarily adds up to zero on a single pixel level. However, if the interferograms are spatially averaged, phase consistency is not guaranteed. In most of the interferometric studies, those mismatches were assumed to be caused by decorrelation noise alone, and were either not considered or deemed negligible, eluding further investigations of its origin. However, recent publications have confirmed that inconsistent phase closures are systematic and not the exception, pointing to an underlying geophysical cause. Comparisons of the spatial signatures of phase closures with land cover maps suggest a spatial and temporal correlation that is related to the characteristics of different land cover types. Since interferometric measurements are sensitive to variations of the dielectric constant, those similarities have been attributed to dynamics in vegetation and soil moisture. A closure phase significance test developed at the Geoscience and Remote Sensing department at TU Delft aimed to increase the signal-to-noise ratio of this geophysical signal component by providing a significance ratio for phase closures. However, the sensitivity of (significant) phase closures to dynamics in vegetation and soil over different land cover types has not been assessed yet. Here we show that with enough averaging of the interferometric phase, the spatial and temporal characteristics of closure phase can be used to distinguish between different land cover types. We found that the degree of spatial averaging has a significant impact on both the phase closure values and its spatial and temporal consistency. The magnitudes of significant phase closures generally increased over low-vegetated land covers, suggesting that closure phases are most sensitive to soil moisture dynamics, whereas vegetation cover was associated with decreasing phase closure magnitudes and spatial inconsistency. Besides spatial averaging, significant differences were observed between closure phases from different polarizations. Furthermore, we found that amplitude backscatter and closure phase are spatially and temporally correlated, pointing to similar influencing mechanisms. Our results demonstrate the importance of applying a closure phase significance test and describe the effect of spatial averaging on the characteristics of phase closures with respect to different land cover types. We anticipate this study to provide useful steps towards using the closure phase for soil and vegetation monitoring in the future. For example, the findings could be used to further exploit potential synergies with amplitude backscatter for soil moisture retrieval from closure phase or develop more sophisticated methods for land cover mapping using InSAR. If not used for applications linked to land cover, vegetation or soil, being able to better predict the effect of those parameters on the interferometric phase and coherence, eventually enables to separate their contribution from other signals, such as deformation estimates. Additional research is needed to relate significant phase closures to moisture changes in vegetation.

Oceans play a vital role in the regulation of Earth's intricate climate system. The majority of gas and energy exchanges occurring at the ocean-atmosphere interface are driven by small-scale, O(1 km), coupled processes. Despite its importance, too few observations have been able to capture the ocean-atmosphere coupling at scales smaller than O(10 km). This has led to poor parameterisation in climate models. In an effort to enhance our understanding of the ocean-atmosphere coupling, this study aims to test and improve upon two separate Marine Atmospheric Boundary Layer (MABL) characterisation methodologies (called algorithm 2A and algorithm 2B) put forward by Young et al., (2000). Algorithms 2A and 2B relate processed Synthetic Aperture Radar (SAR) image properties to a specific atmospheric state through use of surface-layer similarity theory and a combination of surface-layer and mixed-layer similarity theory respectively. Results of both methods indicate significant inherent limitations. Algorithm 2A's utility is curbed by uncertainty introduced during the estimation of Convective Boundary Layer depth Zi and spectral power-law extrapolation. Algorithm 2B suffers from uncertainty introduced by the dimensionless energy dissipation rate psi. As a result of these (and other) uncertainties, the estimated atmospheric instability can be off by a factor 2 or more. Further analyses suggest shortcomings in the applicability of the Geophysical Model Function (GMF). It is hypothesised that both employed GMFs underestimate the horizontal wind-field variance at scales relevant to turbulent convection, which subsequently manifests itself as (part of) the observed average 50% overestimation of absolute Obukhov length and subsequent 33% underestimation of the atmospheric instability. Additional research is required to support and quantify the GMF-induced underestimation hypothesis. Inspired by algorithm 2B, a third method (algorithm 2C) is developed which circumvents major uncertainties inherent to both algorithm 2A and 2B. However, due to limitations of its own, algorithm 2C is incapable of replacing algorithms 2A or 2B for a large range of atmospheric instabilities. If improved upon and successfully employed, spaceborne characterisation of the MABL could benefit climate studies by providing a wealth of continuous and global atmospheric-state measurements on scales previously unavailable.

Ice jam events can be devastating for the environment, human infrastructure, and local population. During breakup season, it is of great importance to be informed about the river ice cover condition in order to mitigate breakup flood risk. The Athabasca River near FortMcMurray, located in Alberta, is particularly prone to ice jam events and subsequent floodings. Satellite remote sensing techniques provide the necessary means to monitor the ice cover. Because of the wide availability most research and operational services for SAR river ice classification are based on single- or dual-polarized images. However, such imagery is limited in its ability to distinguish certain river ice types and open water states. The research presented examines how SAR polarimetry influences the detecting possibilities of specific ice types. Sentinel-1 (dual-polarization), RADARSAT-2 (quad-polarization) and RADARSAT Constellation Mission (compact-polarization) data were used to classify river ice during breakup. This study was about analysing a stretch of the Athabasca River which is prone to ice jam formation. First, SAR images from the 2018-2019 and 2019-2020 breakup were studied to find the temporal and spatial patterns of the radar backscatter. Next, sample areas with known ice stage (sheet ice, ice jam or open water) were selected. The sample areas of each ice stage were compared to assess the influence of SAR characteristics, as incidence angle and overpass time. In the last part of this study, a Random Forest classification was implemented in which intensity, texture and polarimetric features were used. Results show that classification accuracies increase with the inclusion of polarimetric decomposition features and GLCM mean texture features by enhancing between class separability and reducing the misclassification. Accuracies of 85.6% (Kappa = 0.78), 91.2% (Kappa = 0.87) and 91.0% (Kappa = 0.87) were obtained for Sentinel-1, RADARSAT-2 and RCM, respectively. The majority of the confusion between classes was due to similarities at backscatter signatures in very small incidence angles, mainly between open water and sheet ice under melting conditions. Also sheet ice early in the breakup season was confused with ice jams. To reduce the likelihood of misclassification, it is recommended to only use images with incidence angles higher than 30º and to include polarimetric and texture features in a classifier. Additional improvements can be achieved when using expert knowledge for tracking, since previous SAR images can provide added information when one understands the temporal patterns of river ice breakup. Further research should be directed at the development of an automatic classification approach that should be able to detect ice jams during the entire ice covered season. Having more knowledge about river ice breakup may help to eventually develop a river ice forecasting system, which may significantly reduce flood risk.

Oceans cover a significant part of the Earth's surface. The coupling between the upper ocean and the atmosphere is very complicated with defied theoretical understanding, while it is essential for climate studies, weather prediction, and marine ecosystems. With the advent of spaceborne Synthetic Aperture Radar (SAR) systems, surface signatures of ocean and atmospheric processes have been revealed. As winds blowing over the ocean excite the wind waves, all undulations of the ocean surface are assumed as waves in this study. The primary sources for ocean surface signatures in SAR images are waves that are created by the exertion of the local wind stress. Wind waves cause changes in the backscattered power due to three mechanisms: specular reflections, Bragg scattering, and a contribution from wave breaking. A statistical multi-static normalized radar cross-section (NRCS) background model in terms of the directional wave spectrum is developed, considering both Bragg and non-Bragg mechanisms for various polarization states. As the qualitative comparison between optical and SAR data reveals a significant correlation in sea surface signatures, a synthetic attempt is made to estimate the SAR signals from optical signatures. This is realized with the transformation of the wave spectrum in a nonuniform medium, as a consequence of surface currents, and varying near-surface wind fields. A comparison between modeled NRCS and observations is presented. This modulated NRCS model advances the quantitative interpretation of the upper ocean dynamics from satellite measurements.

The Netherlands has for long been witnessing problems due to subsidence. While urban infrastructure is mostly safeguarded from differential settlement by deep pile foundations, the same cannot be said about greenhouses. The greenhouses of the Netherlands has enabled the country to become the largest exporter of vegetables, second only to the United States of America. It is imperative that these greenhouses which form the backbone of the agriculture infrastructure of the country, are monitored continuously to minimize unprecedented damage.

This thesis aims to study the feasibility of using time series Interferometric Synthetic Aperture Radar (InSAR) as a means to monitor differential settlement in greenhouse structures. The analysis was primarily done using RADARSAT-2 data. In case of translucent surfaces of greenhouses, it was important to firstly identify the physical targets that are associated to scattering centres. This was done by analysing the statistics of the heights of the scatterers which helps in ascertaining where the radar signal is getting back-scattered from. It was inferred that the persistent and distributed scatterers are primarily identified from objects on the roof and outer walls of the greenhouses.

Moreover, the magnitude of deformation estimated from the scatterers have been corroborated with geotechnical data. It was seen that higher magnitudes of deformation was seen in locations with compressible soil types such as clay and peat close to the ground surface. It was also seen that greenhouse structures are prone to differential settlement when the depths of the piles are insufficient in areas with varying soil types. The effect of thermal contributions has also been studied and it was found that the estimation of thermal expansion does not significantly affect the estimated deformations.

From the study, it is evident that time series InSAR offers an effective means to monitor differential settlements in greenhouses. In order to check for differential settlement in individual greenhouses, it is proposed that a persistent scatter interferometric analysis be done initially and if it is seen that the density of these scatterers is insufficient, the analysis can be followed up with time series interferometry of distributed scatterers. Incorporating multiple track directions of radar data increases the number of greenhouses that can be monitored. Moreover, it was also seen that persistent scatterers were identified from additional greenhouses when Sentinel-1 data was used, despite its poorer spatial resolution. For further study, it is recommended that corner reflectors are used to validate the positions of the targets that are identified as persistent and distributed scatterers.

Natural or man-made disasters can have a drastic impact on social, economic and environmental aspects of an affected population. Specifically, earthquakes are one of the most potent natural hazards, which cause a disproportionate amount of fatalities, primarily due to a) unexpected building collapses, b) restricted or limited access to basic amenities and c) potential hazards following earthquakes such as landslides, tsunamis etc. It is crucial to have an overview of the infrastructural damage caused following a disaster for search and rescue services to assess the extent of the damage. For the purpose of this research, Sentinel 1 imagery is used to map the building damage in an urban area after a disaster. A combination of parameters such as persistent scatterers, pixel amplitude and phase is used with a timeseries of full-resolution and spatially averaged radar images. Points that are stable in amplitude over a long timeseries, also known as Persistent Scatterers, are extracted from a stack of full-resolution images. The amplitudes of persistent scatterers, along with amplitude and coherence of pixels derived from a stack of spatially-averaged images, are statistically analysed to check the trends of the parameters pre- and post the disaster. A change detection algorithm is applied to this stack in order to localise the areas of building damage. The results are superimposed on Google Earth for easy interpretation using a graded damage scale. The analysis shows that exploiting the persistent scatterer amplitudes in the manner used in this research provides a novel way of locating building damage. This technique can be used effectively in urban areas. Using a combination of pixel amplitudes and coherence along with the persistent scatterers helps correctly find new and unique points of damage for each parameter used. The results were validated using reference Grading and crowd-sourced maps. The results illustrate that the proposed approach can be used for detecting and producing informative maps on infrastructural damage detection in urban areas.

Blue ice areas, are areas in Antarctica where, either due to local heat sources (areas with lower albedo and thus more absorption of shortwave radiation - i.e. Nunataks) or high windspeed, all the snow is melted or eroded away and the underlying (blue) ice is visible. This occurs often around the grounding line between the ice sheet and ice shelf. At this grounding line area, a micro climate exists above the blue ice, which increase surface melt, due to a combination of decreased albedo and warming due to the mixing of cold and warm air. Detection of surface melt on this blue ice is important because this warmer surface melt water results in the increase of hydrofracturing and as a result, the decrease of ice shelf stability. Radar imagery above snow areas is a effective method to detect surface melt, which also ensures a continuous data record. Above blue ice, this is continuous data record of surface melt is also desired, but not done yet and therefore the focus of this thesis is surface melt detection on blue ice with radar imagery. By using the method of Hui et al., 2014 to classify blue ice areas, it is shown that the blue ice area extent (non-stable blue ice) is increasing over the years in the peak of the melt season. However, the extent is slightly decreasing during the non-melt season (stable blue ice). The data of Sentinel-1B is used during the austral summer of 2017/2018, to detect
surface melt on blue ice. This is done via interferometry (and the corresponding coherence) and with the backscatter coefficient. Coherence turns out the be an unreliable method to detect surface melt, since the influence of wind and precipitation on the decrease of coherence is dominant. Thus, surface melt detection via this method is difficult. Backscatter showed some potential to detect surface melt on blue ice, but due to the larger standard deviation than the actual decrease of backscatter (assumed due to surface melt), a clear distinction between blue ice and surface melt can not be made. Melt features, such as rivers, lakes and ponds are detectable with the backscatter, due to their distinctive shape. Since these melt features are linked to surface melt, backscatter can indirectly be used to detect surface melt on blue ice.

Satellite altimetry is an important technology used to measure sea level with high spatial and temporal resolution. Sentinel-3, a Copernicus satellite mission, offers three types of variables captured simultaneously for the first time; sea level (SSH), sea surface temperature (SST) and ocean colour (OC) variables. Sea level is measured with SAR altimetry, a technique that considerably increases spatial resolution compared to other means of observation. Altimetry measures sea level across a line that coincides with the satellite ground track, whereas SST and OC are measured on a grid. What we lack are sea level observations in-between ground tracks that would better resolve meso-scale variability. This thesis is focused on two objectives, considering previous work that has indicated associations between those variables. The first objective was to investigate the correlations among SSH, SST and OC, while the second objective was to assess to what extent inter-track sea level can be estimated using SST and OC as predictors in machine learning algorithms. Daily Sentinel-3 data over a period of eleven months were pre-processed and brought into a form that allowed for computation of metrics such as auto- and cross-correlations in the along-track direction. The focus was on the spatial scales that would enable to detect meso-scale features, such as eddies. With respect to the inter-track sea level estimation two paths were followed. In the first path, Random Forest (RF) and Multilayer Perceptron (MLP) were applied using the complete 11-month dataset as input. Moreover, RF was applied on input data that belong to each separate day. In the second path, 1D Convolutional Neural Network (CNN) was used on the complete 11-month dataset, which inherently honors the spatial dependency of the variables in contrast to the first path. Generally, the correlations between the variables were found to exist in the meso-scale but were not always strong and they depend on several other factors, such as meteorological conditions, scales included in the analysis and techniques used. All three techniques -RF, MLP and 1D CNN- that were applied on the complete 11-month dataset gave poor results. On the contrary, when RF was applied on the per-day data gave promising results that are reliable mostly in the vicinity of the ground track, although they are not based on one single global model. The results from this project suggest that there must be more research on the correlation analysis of Sentinel-3 data. It can be improved by using additional or similar techniques, such as localized cross-correlation metrics on various spatial scales. With respect to the inter-track sea level estimation, far more investigation is needed. However, there are indications that a machine learning data-driven approach could potentially work to some extent. Sentinel-3 data will become more abundant in the next years which will assist data science algorithms such as CNNs which require huge datasets.

Stereo Thermo-Optically Enhanced Radar for Earth, Ocean, Ice, and land Dynamics (STEREOID) is one of the candidates of the ESA (European Space Agency) , Earth Explorer 10 missions. The novel constellation system will consist of the active Sentinel-1 satellites and two passive spacecrafts, which can provide flexible baseline configurations. The main objective of the mission lies
in monitoring the variation of spatially diverse ice sheets, the eruptions of earthquakes, the volcano activities, and the landslides, playing therefore an extremely important role in understanding the global climate dynamics and the geophysical processes involved. The purpose of the thesis is to develop an end-to-end simulator incorporating the STEREOID bistatic configuration operating in TOPS ( Terrain Observation by Progressive Scans) mode and evaluate its performance. To achieve this goal, the key component of the simulator, the SAR (Synthetic Aperture Radar) processing kernel was first implemented. The kernel employs an imaging algorithm which assists in image formation and focusing for different bistatic geometries generated by relevant working modes of the STEREOID mission. This is further extended to bistatic TOPS acquisition mode with azimuth beamforming under dual antenna receiver configuration of STEREOID. The performance of STEREOID mission is evaluated under different bistatic geometries and the dual beamforming strategy is evaluated for parameters such as resolution, pointing errors and gain imbalances. This is evaluated to analyse and understand the importance of calibration errors introduced into the system.

Direct measurement of ocean surface velocity from space with a Synthetic Aperture Radar has shown to be a promising method to observe ocean surface currents. In this thesis report a method for Total Surface Current Vector (TSCV) retrieval using an experimental Bidirectional (BiDi) Along-Track Interferometric (ATI) acquisition mode with TanDEM-X is presented. Errors of retrieval results from simulated data and from real data are studied to assess the quality of the proposed method. The available data consists of a StripMap acquisition at the coast of Tromso and a data set acquired over Novaya Zemlya.
The measurement concept relies on the ATI phase, which provides an estimate of the first moment of the Doppler spectrum associated to total surface velocity. Observing with two beams squinted as far as 13.2 degree apart in azimuth on ground, allows the Doppler velocity to be observed in line of sight of the beams. Projection to the ocean surface gives a velocity field. This Doppler velocity field consists of a Normalized Radar Cross Section (NRCS) weighted average of velocities of sea-state dependent biases such as short wind generated waves, long swell waves and underlying currents.
Assuming the surface velocity is dominated by wind generated waves and underlying currents, the method attempts to solve for TSCV simultaneously with the surface wind vector by coupling geophysical model functions (GMF) for returned Doppler Centroid (DC) and NRCS from an ocean surface shaped by wind.
For NRCS the empirical GMF XMOD2 for X-band radar is used, based on the same regression algorithm as the widely used CMOD5 in scatterometry. For DC a GMF based on statistics of the sea surface and the Kirchhoff Approximation developed by IFREMER is used.
A cost function of the wind vector is defined as the squared difference between NRCS observations and values of the GMF in both beams. The wind speed magnitude and wind direction for which this cost function is minimal provide an estimate to the local wind vector and evaluating the GMF for DC with the estimated wind vector results in a component of surface motion caused by wind generated waves. Wind wave induced surface velocities and TSCV can then be separated.
Retrieval from simulated data shows that the wind retrieval algorithm gives an ambiguous result for the wind direction. To constrain the solution ECMWF ERA-5 reanalysis wind data is added to the cost function as an additional term with a low weight factor.
Error analyses on the propagation of data errors shows success of the method relies on calibration quality that itself depends on local conditions of the acquired data. Comparison of retrieved wind using different GMF's indicates there is a high uncertainty in the models. The average of retrieved wind vector field over the image is highly similar to the lower resolution ECMWF ERA-5 wind vector data. TSCV results appear good for data with small ATI phase errors, but are dependent on the accuracy of used GMF's.

Climate change has been a heated topic in recent years, and the mass loss of ice sheets is one aspect of it. The Antarctic Ice Sheet has experienced certain mass loss in the form of ice-shelf collapse and (sub-)surface melting, but thorough study remains limited due to the remote location of the continent. Therefore, remote sensing is expected to provide valuable information on Antarctica, in order to monitor its mass balance and gain insights on the extent of climate change.

As one of the factors to the mass loss in Antarctica, subsurface melt can be critical yet hard to capture. The limitation of remote sensing data, especially valid optical images over polar regions may add to the issue. This study aims to exploit the potentiality of SAR Interferometry in detecting subsurface melt, as the microwave bands are not affected by weather and illumination, and the interferometric data may provide supportive information reflecting the properties of the physical environment. It is expected that by using this technique, the gap can be filled in when optical images are not available, or pure SAR images are not informative. The technique is applied to two ice shelves in East Antarctica, Roi Baudouin Ice Shelf and Amery Ice Shelf. And this study is expected to be operated over a broader scale such as the Antarctic continent and Greenland.

Humanitarian crises related to building and infrastructure damage, i.e. natural hazards and collateral damage during warfare, result in many hundreds of thousands of casualties on a yearly base. Earthquakes, for example, are responsible for taking around 50.000 lives on a yearly base due to falling debris and collapsing buildings, making them one of the most deadly natural disasters known to mankind. These casualties are caused by both direct effects (falling debris) as well as indirect effects (being trapped by debris without access to air/water/food). In the first hours after an earthquake hit an area, a clear overview of the situation is hard to obtain due to inaccessible roads and damaged communication systems, which in its turn hampers setting up rapid disaster response operations. In order to assist the search and rescue teams in wide-area situation awareness, remote sensing techniques can be used to acquire and create damage detection maps which could be used to prioritize their tasks and help save more lives.

Spaceborne Synthetic Aperture Radar (SAR) is one of the most promising techniques in rapid disaster response as it is an active sensor which remains unaffected by the weather, smoke, cloud cover and/or daylight. The Sentinel-1 mission is a relatively new SAR mission that acquires data over the entire Earth's surface every 3 days in any viewing geometry. More importantly, this mission has an open-data policy and hence provides the opportunity to easily perform multi-temporal data analysis instead of the bi- and tri-temporal change detection approaches proposed in previous studies. The development of a fully automatic change detection algorithm has been done in the Google Earth Engine: an online, open-source processing tool that allows users to run algorithms on geo-referenced Earth observation imagery stored on Google's infrastructure, with one of these datasets being the global Sentinel-1 GRD dataset. The time reduction that results from bringing the algorithm to the data instead of vice versa as well as the possibility to easily combine various geo-referenced datasets both benefited the rapid disaster response operations.

The processing of the data is performed completely automatically based on the date and location of the disaster as input, making it easy to use for non-remote sensing specialists. Using these two parameters, the Sentinel-1 images overlapping the area of interest are selected after which a change detection algorithm is applied to the timeseries. The resulting damage detection maps has been validated in terms of operability and accuracy for wide-area situation awareness in search and rescue operations for three case studies: the 2016 M6.2 Central Italy earthquake, the 2017 M7.1 Central Mexico earthquake and the 2018 Syria military strikes. Overall, the algorithm performs well in terms of damage detection, with a low amount of false positives and an even lower amount of false negatives, for both damage due to natural hazards and intentional or collateral damage during warfare. The algorithm experienced difficulties when identifying individual collapsed buildings in a dense urban environment as around 50\% of the collapsed buildings were detected as such. However, the accuracy of the damage detection is much better when looking at clusters of damaged buildings in urban areas or at individual buildings, which are equal to or larger than the size of a SAR resolution cell, in less dense urban environments.

The complete approach and final damage detection maps have been presented to the Urban Search And Rescue team of The Netherlands, whom are the potential end-users of these maps. They saw great potential in this approach in general to help prioritize their search and rescue operations, with the only bottleneck being the data latency. We showed that once a new image has been acquired after the disaster, the total data latency by ESA and the GEE is at most 72h. This is also the maximal time the USAR team has in order to move from their home base to any country in the world in case of a disaster. The data latency time frame is a maximum, so actual averages are lower and the final damage detection map will be completed and ready to use once needed by the USAR team. This illustrates that the approach proposed in this research can be used on an operational base to assist in rapid disaster response situations.