@article{ToetzkeOswaldHilgeretal.2021,
  author    = {T{\"o}tzke, Christian and Oswald, Sascha and Hilger, Andr{\´e} and Kardjilov, Nikolay},
  title     = {Non-invasive detection and localization of microplastic particles in a sandy sediment by complementary neutron and X-ray tomography},
  series = {Journal of soils and sediments : protection, risk assessment and remediation},
  volume    = {21},
  journal   = {Journal of soils and sediments : protection, risk assessment and remediation},
  number    = {3},
  publisher = {Springer},
  address   = {Berlin ; Heidelberg},
  issn      = {1439-0108},
  doi       = {10.1007/s11368-021-02882-6},
  pages     = {1476 -- 1487},
  year      = {2021},
  abstract  = {Purpose Microplastics have become a ubiquitous pollutant in marine, terrestrial and freshwater systems that seriously affects aquatic and terrestrial ecosystems. Common methods for analysing microplastic abundance in soil or sediments are based on destructive sampling or involve destructive sample processing. Thus, substantial information about local distribution of microplastics is inevitably lost. Methods Tomographic methods have been explored in our study as they can help to overcome this limitation because they allow the analysis of the sample structure while maintaining its integrity. However, this capability has not yet been exploited for detection of environmental microplastics. We present a bimodal 3D imaging approach capable to detect microplastics in soil or sediment cores non-destructively. Results In a first pilot study, we demonstrate the unique potential of neutrons to sense and localize microplastic particles in sandy sediment. The complementary application of X-rays allows mineral grains to be discriminated from microplastic particles. Additionally, it yields detailed information on the 3D surroundings of each microplastic particle, which supports its size and shape determination. Conclusion The procedure we developed is able to identify microplastic particles with diameters of approximately 1 mm in a sandy soil. It also allows characterisation of the shape of the microplastic particles as well as the microstructure of the soil and sediment sample as depositional background information. Transferring this approach to environmental samples presents the opportunity to gain insights of the exact distribution of microplastics as well as their past deposition, deterioration and translocation processes.},
  language  = {en}
}
@article{ZappaSchlafferBroccaetal.2022,
  author    = {Zappa, Luca and Schlaffer, Stefan and Brocca, Luca and Vreugdenhil, Mariette and Nendel, Claas and Dorigo, Wouter},
  title     = {How accurately can we retrieve irrigation timing and water amounts from (satellite) soil moisture?},
  series = {International journal of applied earth observation and geoinformation},
  volume    = {113},
  journal   = {International journal of applied earth observation and geoinformation},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1569-8432},
  doi       = {10.1016/j.jag.2022.102979},
  pages     = {12},
  year      = {2022},
  abstract  = {While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts. In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series. We show that coarse-scale remotely sensed soil moisture products achieve higher correlations with rainfed simulations, while high-resolution satellite observations agree significantly better with irrigated simulations, suggesting that high-resolution satellite soil moisture can inform on field-scale (similar to 40 ha) irrigation. A thorough analysis of the synthetic dataset showed that satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson's correlation > 0.8), are found for noise-free soil moisture observations either with a temporal sampling up to 3 days or if at least one-third of the pixel covers the irrigated field(s). However, irrigation water amounts are systematically underestimated for temporal samplings of more than one day, and decrease proportionally to the spatial resolution, i.e., coarsening the pixel size leads to larger irrigation underestimations. Although lower spatial and temporal resolutions decrease the detection and quantification accuracies (e.g., R between 0.6 and 1 depending on the irrigation rate and spatio-temporal resolution), random errors in the soil moisture time series have a stronger negative impact (Pearson R always smaller than 0.85). As expected, better performances are found for higher irrigation rates, i.e. when more water is supplied during an irrigation event. Despite the potentially large underestimations, our results suggest that high-resolution satellite soil moisture has the potential to track and quantify irrigation, especially over regions where large volumes of irrigation water are applied to the fields, and given that low errors affect the soil moisture observations.},
  language  = {en}
}
@article{OeschWeiseBruno2020,
  author    = {Oesch, Tyler and Weise, Frank and Bruno, Giovanni},
  title     = {Detection and quantification of cracking in concrete aggregate through virtual data fusion of X-ray computed tomography images},
  series = {Materials},
  volume    = {13},
  journal   = {Materials},
  number    = {18},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1996-1944},
  doi       = {10.3390/ma13183921},
  pages     = {27},
  year      = {2020},
  abstract  = {In this work, which is part of a larger research program, a framework called "virtual data fusion" was developed to provide an automated and consistent crack detection method that allows for the cross-comparison of results from large quantities of X-ray computed tomography (CT) data. A partial implementation of this method in a custom program was developed for use in research focused on crack quantification in alkali-silica reaction (ASR)-sensitive concrete aggregates. During the CT image processing, a series of image analyses tailored for detecting specific, individual crack-like characteristics were completed. The results of these analyses were then "fused" in order to identify crack-like objects within the images with much higher accuracy than that yielded by any individual image analysis procedure. The results of this strategy demonstrated the success of the program in effectively identifying crack-like structures and quantifying characteristics, such as surface area and volume. The results demonstrated that the source of aggregate has a very significant impact on the amount of internal cracking, even when the mineralogical characteristics remain very similar. River gravels, for instance, were found to contain significantly higher levels of internal cracking than quarried stone aggregates of the same mineralogical type.},
  language  = {en}
}