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Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics based units such as radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing the photoelectric effect. Temporal variations of the detector characteristics that differ with foregoing radiometric calibration cause visually perceptible along-track stripes in the at-sensor radiance data that aggravate succeeding image-based analyses. Especially, variations of the thermally induced dark current dominate and have to be reduced. In this work, a new approach is presented that efficiently reduces dark current related stripe noise. It integrates an across-effect gradient minimization principle. The performance has been evaluated using artificially degraded whiskbroom (reference) and real pushbroom acquisitions from EO-1 Hyperion and AISA DUAL that are significantly covered by stripe noise. A set of quality indicators has been used for the accuracy assessment. They clearly show that the new approach outperforms a limited set of tested state-of-the-art approaches and achieves a very high accuracy related to ground-truth for selected tests. It may substitute recent algorithms in the Reduction of Miscalibration Effects (ROME) framework that is broadly used to reduce radiometric miscalibrations of pushbroom data takes.

Cosmic-ray neutron sensing (CRNS) has gained popularity for estimating soil moisture due to its innovative capability to measure at an intermediate scale – a notable advantage over point-scale sensors, which are often sparsely installed and lead to inaccurate absolute values due to small-scale heterogeneity. CRNS serves as a crucial link between small and large scales and has been emerging as a reference measurement for validating remote sensing algorithms. However, the sparse availability of long-term datasets limits use of this possibility. Within the DFG-research unit Cosmic Sense and the European 21GRD08 SoMMet project, multiscale soil moisture monitoring was implemented to integrate CRNS with complementary in-situ observations. In this paper, we present harmonized soil moisture data from different sensor types, including a CRNS cluster, shallow soil moisture measurements, and soil moisture profile data, creating a ready-to-use dataset as a reference observation for remote sensing products, covering a highly-instrumented agricultural site in the northeast of Germany. The newly established Potsdam Soil Moisture Observatory (PoSMO) comprises 16 stationary CRNS sensors with point-scale soil moisture sensors installed at the same locations in different depths and data from intensive manual sampling campaigns (covering soil moisture, bulk density, organic matter, etc.). This dataset goes beyond other studies by covering a larger area of approx. 1 km2, while nevertheless achieving a high sensor density and mostly overlapping CRNS footprints allowing for nearly complete coverage. Complementary measurements of soil properties, vegetation, groundwater, meteorology, and remote sensing imagery provide the context required to interpret the observed soil moisture dynamics across spatial and temporal scales. The data are available at https://doi.org/10.23728/b2share.bxamy-4zh85 (Grosse et al., 2025) and provide a new reference dataset for remote sensing products, hydrological or land-surface models, and other applications related to soil water balance.

The component of the resistivity tensor \\(_ij\\) corresponding to voltage transverse to both an applied current and a magnetic field can be separated into odd and even parts with respect to the applied magnetic field. The former contains information, for example, about the ordinary or anomalous Hall effect. The latter is often ascribed to experimental artefacts and ignored. Here, we show that upon suppressing these artefacts in carefully controlled experiments, useful information remains. We first investigate the well-explored ferromagnet CoFeB, where the even part of \\(_yx\\) contains a contribution from the anisotropic magnetoresistance, which we confirm by Stoner--Wohlfarth modelling. We then apply our approach to magnetotransport measurements of \\( Mn_5Si_3\\) thin films, which undergo a transition from non-collinear to an altermagnetic collinear state. In this material, the even part of the transverse signal is sizable only in the low-spin-symmetry phase below \\( 80\\)~K. Transverse resistivity measurements thus offer a simple and readily available probe of magnetic order transitions.