Network Adjustment of Orbit Errors in SAR Interferometry

Orbit errors can induce significant long wavelength error signals in synthetic aperture radar (SAR) interferograms and thus bias estimates of wide-scale deformation phenomena. The presented approach aims for correcting orbit errors in a preprocessing step to deformation analysis by modifying state vectors. Whereas absolute errors in the orbital trajectory are negligible, the influence of relative errors (baseline errors) is parametrised by their parallel and perpendicular component as a linear function of time. As the sensitivity of the interferometric phase is only significant with respect to the perpendicular baseline and the rate of change of the parallel baseline, the algorithm focuses on estimating updates to these two parameters. This is achieved by a least squares approach, where the unwrapped residual interferometric phase is observed and atmospheric contributions are considered to be stochastic with constant mean. To enhance reliability, baseline errors are adjusted in an overdetermined network of interferograms, yielding individual orbit corrections per acquisition

Reliable estimation of orbit errors in spaceborne SAR interferometry. The network approach

An approach to improve orbital state vectors by orbit error estimates derived from residual phase patterns in synthetic aperture radar interferograms is presented. For individual interferograms, an error representation by two parameters is motivated: the baseline error in cross-range and the rate of change of the baseline error in range. For their estimation, two alternatives are proposed: a least squares approach that requires prior unwrapping and a less reliable gridsearch method handling the wrapped phase. In both cases, reliability is enhanced by mutual control of error estimates in an overdetermined network of linearly dependent interferometric combinations of images. Thus, systematic biases, e.g., due to unwrapping errors, can be detected and iteratively eliminated. Regularising the solution by a minimum-norm condition results in quasi-absolute orbit errors that refer to particular images. For the 31 images of a sample ENVISAT dataset, orbit corrections with a mutual consistency on the millimetre level have been inferred from 163 interferograms. The method itself qualifies by reliability and rigorous geometric modelling of the orbital error signal but does not consider interfering large scale deformation effects. However, a separation may be feasible in a combined processing with persistent scatterer approaches or by temporal filtering of the estimates

Analyzing coastal erosion and sedimentation using Sentinel-1 SAR change detection: An application on the Volta Delta, Ghana

Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning

Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.5FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50942-

On the Effect of Reference Frame Motion on InSAR Deformation Estimates

For processing of interferometric synthetic aperture radar (InSAR) data, precise satellite orbits are required. These orbits are given in a reference frame with respect to which tectonic plates perform a relative motion. Neglecting this motion can cause temporally increasing baseline errors that induce large scale error ramps into the interferometric phase. The amount of error depends on the geographical location and is evaluated globally for the ENVISAT orbit. Predicted biases of deformation estimates can reach up to 7 mm/a in some areas. Whereas these biases are not separable from actual deformation signals by spatio-temporal correlation properties, they are well predictable and can easily be accounted for. A most simple correction approach consists in compensating the plate motion by modifying orbital state vectors, assuming a homogeneous velocity for the whole plate. This approach has been tested on Persistent Scatterer Interferometry (PSI) results over the area of Groningen, the Netherlands

Vegetation characterization through the use of precipitation-affected SAR signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.1010FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50943-

Optimizing the use of InSAR observations in data assimilation problems to estimate reservoir compaction

Hydrocarbon production may cause subsidence as a result of the pressure reduction in the gas-producing layer and reservoir compaction. To analyze the process of subsidence and estimate reservoir parameters, we use a particle method to assimilate Interferometric synthetic-aperture radar (InSAR) observations of surface deformation with a conceptual model of reservoir. As example, we use an analytical model of the Groningen gas reservoir based on a geometry representing the compartmentalized structure of the subsurface at the reservoir depth. The efficacy of the particle method becomes less when the degree of freedom is large compared to the ensemble size. This degree of freedom, in turn, varies because of spatial correlation in the observed field. The resolution of the InSAR data and the number of observations affect the performance of the particle method. In this study, we quantify the information in a Sentinel-1 SAR dataset using the concept of Shannon entropy from information theory. We investigate how to best capture the level of detail in model resolved by the InSAR data while maximizing their information content for a data assimilation use. We show that incorrect representation of the existing correlations leads to weight collapse when the number of observation increases, unless the ensemble size growths. However, simulations of mutual information show that we could optimize data reduction by choosing an adequate mesh given the spatial correlation in the observed subsidence. Our approach provides a means to achieve a better information use from available InSAR data reducing weight collapse without additional computational cost