This review provides an up-to-date summary of mass spectrometry imaging, emphasizing different applications and recent innovations in natural product research.
<p>Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (< 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.</p>
", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.1007/s11104-016-2872-7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-28T16:15:24Z", "type": "Journal Article", "created": "2016-04-08", "title": "Challenges in imaging and predictive modeling of rhizosphere processes", "description": "Background: Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope: In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions: We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes.", "keywords": ["2. Zero hunger", "X-ray CT", "Dewey Decimal Classification::500 | Naturwissenschaften::570 | Biowissenschaften", " Biologie", "Soil Science", "Plant Science", "Chemical mapping", "04 agricultural and veterinary sciences", "15. Life on land", "Dewey Decimal Classification::500 | Naturwissenschaften::580 | Pflanzen (Botanik)", "13. Climate action", "Rhizosphere", "0401 agriculture", " forestry", " and fisheries", "Mathematical modeling", "Correlative imaging"]}, "links": [{"href": "https://eprints.soton.ac.uk/390303/1/Roose%2520et%2520al%25202016%2520Plant%2520Soil%2520Marschner%2520Review%2520Accepted.pdf"}, {"href": "https://doi.org/10.1007/s11104-016-2872-7"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20and%20Soil", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11104-016-2872-7", "name": "item", "description": "10.1007/s11104-016-2872-7", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11104-016-2872-7"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-04-08T00:00:00Z"}}, {"id": "10.1007/s11104-021-05133-2", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:15:25Z", "type": "Journal Article", "created": "2021-09-12", "title": "In situ laser manipulation of root tissues in transparent soil", "description": "AbstractAimsLaser micromanipulation such as dissection or optical trapping enables remote physical modification of the activity of tissues, cells and organelles. To date, applications of laser manipulation to plant roots grown in soil have been limited. Here, we show laser manipulation can be applied in situ when plant roots are grown in transparent soil.
MethodsWe have developed a Q-switched laser manipulation and imaging instrument to perform controlled dissection of roots and to study light-induced root growth responses. We performed a detailed characterisation of the properties of the cutting beams through the soil, studying dissection and optical ablation. Furthermore, we also studied the use of low light doses to control the root elongation rate of lettuce seedlings (Lactuca sativa) in air, agar, gel and transparent soil.
ResultsWe show that whilst soil inhomogeneities affect the thickness and circularity of the beam, those distortions are not inherently limiting. The ability to induce changes in root elongation or complete dissection of microscopic regions of the root is robust to substrate heterogeneity and microscopy set up and is maintained following the limited distortions induced by the transparent soil environment.
ConclusionsOur findings show that controlled in situ laser dissection of root tissues is possible with a simple and low-cost optical set-up. We also show that, in the absence of dissection, a reduced laser light power density can provide reversible control of root growth, achieving a precise \uffe2\uff80\uff9cpoint and shoot\uffe2\uff80\uff9d method for root manipulation.Our understanding of the rhizosphere is limited by the lack of techniques for in situ live microscopy. Current techniques are either destructive or unsuitable for observing chemical changes within the pore space. To address this limitation, we have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles.
MethodsThe transparency of smart soils was achieved using polymer particles with refractive index matching that of water. The surface of the particles was modified both to retain water and act as a local sensor to report on pore space pH via fluorescence emissions. Multispectral signals were acquired from the particles using a light sheet microscope, and machine learning algorithms predicted the changes and spatial distribution in pH at the surface of the smart soil particles.
ResultsThe technique was able to predict pH live and in situ within \uffc2\uffb1\uffe2\uff80\uff890.5 units of the true pH value. pH distribution could be reconstructed across a volume of several cubic centimetres around plant roots at 10\uffc2\uffa0\uffce\uffbcm resolution. Using smart soils of different composition, we revealed how root exudation and pore structure create variability in chemical properties.
ConclusionSmart soils captured the pH gradients forming around a growing plant root. Future developments of the technology could include the fine tuning of soil physicochemical properties, the addition of chemical sensors and improved data processing. Hence, this technology could play a critical role in advancing our understanding of complex rhizosphere processes.
", "keywords": ["/dk/atira/pure/subjectarea/asjc/1100/1111", "light sheet microscopy", "0301 basic medicine", "570", "0303 health sciences", "name=Soil Science", "/dk/atira/pure/subjectarea/asjc/1100/1110", "Sensing soil", "live imaging", "15. Life on land", "root", "530", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "03 medical and health sciences", "Root", "13. Climate action", "Rhizosphere", "Light sheet microscopy", "name=Plant Science", "rhizosphere", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "Live imaging"]}, "links": [{"href": "https://doi.org/10.1007/s11104-023-06151-y"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20and%20Soil", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11104-023-06151-y", "name": "item", "description": "10.1007/s11104-023-06151-y", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11104-023-06151-y"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-26T00:00:00Z"}}, {"id": "10.1007/s11356-017-8823-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-28T16:15:29Z", "type": "Journal Article", "created": "2017-03-24", "title": "Quantitative characterization of pore structure of several biochars with 3D imaging", "description": "Open Access16 pages, 4 figures. The final publication is available at Springer via http://dx.doi.org/10.1007/s11356-017-8823-x", "keywords": ["x-ray tomography", "Condensed Matter - Materials Science", "soil amendment", "pore structure", "ta1171", "ta1182", "Water", "Materials Science (cond-mat.mtrl-sci)", "FOS: Physical sciences", "04 agricultural and veterinary sciences", "01 natural sciences", "6. Clean water", "Diffusion", "Imaging", " Three-Dimensional", "image analysis", "Charcoal", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "biochar", "Porosity", "soil amendments", "ta218", "water retention", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11356-017-8823-x.pdf"}, {"href": "https://doi.org/10.1007/s11356-017-8823-x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20and%20Pollution%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11356-017-8823-x", "name": "item", "description": "10.1007/s11356-017-8823-x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11356-017-8823-x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-24T00:00:00Z"}}, {"id": "10.1007/s11538-017-0350-x", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:15:31Z", "type": "Journal Article", "created": "2017-10-13", "title": "An Explicit Structural Model of Root Hair and Soil Interactions Parameterised by Synchrotron X-ray Computed Tomography", "description": "The rhizosphere is a zone of fundamental importance for understanding the dynamics of nutrient acquisition by plant roots. The canonical difficulty of experimentally investigating the rhizosphere led long ago to the adoption of mathematical models, the most sophisticated of which now incorporate explicit representations of root hairs and rhizosphere soil. Mathematical upscaling regimes, such as homogenisation, offer the possibility of incorporating into larger-scale models the important mechanistic processes occurring at the rhizosphere scale. However, we lack concrete descriptions of all the features required to fully parameterise models at the rhizosphere scale. By combining synchrotron X-ray computed tomography (SRXCT) and a novel root growth assay, we derive a three-dimensional description of rhizosphere soil structure suitable for use in multi-scale modelling frameworks. We describe an approach to mitigate sub-optimal root hair detection via structural root hair growth modelling. The growth model is explicitly parameterised with SRXCT data and simulates three-dimensional root hair ideotypes in silico, which are suitable for both ideotypic analysis and parameterisation of 3D geometry in mathematical models. The study considers different hypothetical conditions governing root hair interactions with soil matrices, with their respective effects on hair morphology being compared between idealised and image-derived soil/root geometries. The studies in idealised geometries suggest that packing arrangement of soil affects hair tortuosity more than the particle diameter. Results in field-derived soil suggest that hair access to poorly mobile nutrients is particularly sensitive to the physical interaction between the growing hairs and the phase of the soil in which soil water is present (i.e. the hydrated textural phase). The general trends in fluid-coincident hair length with distance from the root, and their dependence on hair/soil interaction mechanisms, are conserved across Cartesian and cylindrical geometries.", "keywords": ["2. Zero hunger", "0301 basic medicine", "0303 health sciences", "Mathematical Concepts", "15. Life on land", "Models", " Biological", "Plant Roots", "Soil", "03 medical and health sciences", "Imaging", " Three-Dimensional", "Rhizosphere", "Original Article", "Computer Simulation", "Tomography", " X-Ray Computed", "Synchrotrons"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11538-017-0350-x.pdf"}, {"href": "https://doi.org/10.1007/s11538-017-0350-x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bulletin%20of%20Mathematical%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11538-017-0350-x", "name": "item", "description": "10.1007/s11538-017-0350-x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11538-017-0350-x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-13T00:00:00Z"}}, {"id": "10.1101/2021.02.13.430456", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:18:27Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "AbstractOur understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the \uffc3\uff85ngstr\uffc3\uffb6m turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value.
", "keywords": ["Photovoltaic Arrays", "Cleanliness", "Particle", "PV", "02 engineering and technology", "Oceanography", "7. Clean energy", "soiling; experimental; transmittance; spectrum", "Turbidity", "Size", "Materials Science and Engineering", "\u00c5ngstr\u00f6m turbidity equation", "Transmittance", "0202 electrical engineering", " electronic engineering", " information engineering", "Photovoltaic system", "Ultraviolet", "Microscopy", "Soiling", "Energy", "Ecology", "Physics", "Q", "R", "Imaging and sensing", "Geology", "Particle size", "6. Clean water", "Photovoltaic Efficiency", "Chemistry", "Physical chemistry", "Particle (ecology)", "Physical Sciences", "Sunlight", "Medicine", "Infrared", "570", "Particle-size distribution", "PV System", "Energy science and technology", "Science", "Optical spectroscopy", "Partial Shading", "530", "Modelling", "Article", "Environmental science", "Techniques and instrumentation", "Optical physics", "Meteorology", "Artificial Intelligence", "Machine Learning Methods for Solar Radiation Forecasting", "Optical techniques", "Optoelectronics", "Aerosol", "Biology", "Renewable Energy", " Sustainability and the Environment", "Electronics", " photonics and device physics", "Building Integrated Photovoltaics", "Optics", "Photovoltaic Maximum Power Point Tracking Techniques", "FOS: Earth and related environmental sciences", "Materials science", "Photovoltaics", "Optics and photonics", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Solar Thermal Energy Technologies"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625670/2/Smestad_Modelling_2020.pdf"}, {"href": "https://www.nature.com/articles/s41598-019-56868-z.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-56868-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-56868-z", "name": "item", "description": "10.1038/s41598-019-56868-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-56868-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-09T00:00:00Z"}}, {"id": "10.1039/d2np00011c", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:17:52Z", "type": "Journal Article", "created": "2022-06-23", "title": "Image to insight: exploring natural products through mass spectrometry imaging", "description": "This review provides an up-to-date summary of mass spectrometry imaging, emphasizing different applications and recent innovations in natural product research.
Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.
", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1073/pnas.2109176118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.2109176118", "name": "item", "description": "10.1073/pnas.2109176118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.2109176118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1093/jxb/erv544", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:18:21Z", "type": "Journal Article", "created": "2016-01-20", "title": "Image-based modelling of nutrient movement in and around the rhizosphere", "description": "In this study, we developed a spatially explicit model for nutrient uptake by root hairs based on X-ray computed tomography images of the rhizosphere soil structure. This work extends our previous work to larger domains and hence is valid for longer times. Unlike the model used previously, which considered only a small region of soil about the root, we considered an effectively infinite volume of bulk soil about the rhizosphere. We asked the question: At what distance away from root surfaces do the specific structural features of root-hair and soil aggregate morphology not matter because average properties start dominating the nutrient transport? The resulting model was used to capture bulk and rhizosphere soil properties by considering representative volumes of soil far from the root and adjacent to the root, respectively. By increasing the size of the volumes that we considered, the diffusive impedance of the bulk soil and root uptake were seen to converge. We did this for two different values of water content. We found that the size of region for which the nutrient uptake properties converged to a fixed value was dependent on the water saturation. In the fully saturated case, the region of soil we needed to consider was only of radius 1.1mm for poorly soil-mobile species such as phosphate. However, in the case of a partially saturated medium (relative saturation 0.3), we found that a radius of 1.4mm was necessary. This suggests that, in addition to the geometrical properties of the rhizosphere, there is an additional effect of soil moisture properties, which extends further from the root and may relate to other chemical changes in the rhizosphere. The latter were not explicitly included in our model.", "keywords": ["0301 basic medicine", "550", "Oryza", "04 agricultural and veterinary sciences", "15. Life on land", "Models", " Biological", "Plant Roots", "630", "03 medical and health sciences", "Imaging", " Three-Dimensional", "Rhizosphere", "0401 agriculture", " forestry", " and fisheries", "Tomography", " X-Ray Computed", "Research Paper"]}, "links": [{"href": "https://doi.org/10.1093/jxb/erv544"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/erv544", "name": "item", "description": "10.1093/jxb/erv544", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/erv544"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-05T00:00:00Z"}}, {"id": "10.1109/access.2025.3569213", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:18:30Z", "type": "Journal Article", "created": "2025-05-12", "title": "An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals", "description": "The most vulnerable parts of tomatoes are the tips of the sepals, which are the primary entry points for fungal spores. Their precise segmentation within hyperspectral images (HSIs) plays a pivotal role in the development of automated and non-destructive systems for assessing tomatoes’ sensitivity to fungal infections. This research addresses the critical need for encoding spectral information in hyperspectral imaging to enhance the efficiency of such automated systems. We investigate four different techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Probabilistic Principal Component Analysis (PPCA), and Non-Negative Matrix Factorization (NMF), to perform transfer learning for tomato sepal instance segmentation using models previously trained on RGB images. A comparative analysis of three Mask Region-based Convolutional Neural Network (Mask R-CNN) backbone models is conducted: the Faster R-CNN, Deformable ConvNet, and Feature Pyramid Network (FPN) on spectral-encoded HSIs of the Brioso tomato variety. The Mask R-CNN with FPN, integrated with the NMF technique achieved the highest level of accuracy, yielding a Mean Average Precision (mAP) of 94.05%. Furthermore, on the second dataset, which included an additional three tomato varieties: Capricia, Provine, and Sao Paolo, the same model achieved mAP score of 86.42% across all tomato varieties with only a single false positive detection. Additionally, we incorporated a custom convolutional layer initialized it with estimated NMF coefficients, and achieved a mAP score of 87.40%. This demonstrates the potential of integrating spectral information encoding with trained deep learning-based instance segmentation models to enable robust and accurate transfer learning for automated agricultural food quality assessments.", "keywords": ["instance segmentation", "deep learning", "Electrical engineering. Electronics. Nuclear engineering", "transfer learning", "tomato", "encoding spectral information in HSI", "Hyperspectral imaging (HSI)", "TK1-9971"]}, "links": [{"href": "https://doi.org/10.1109/access.2025.3569213"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Access", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/access.2025.3569213", "name": "item", "description": "10.1109/access.2025.3569213", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/access.2025.3569213"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.1109/TMI.2017.2743819", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-07-28T16:18:29Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/10.1109/TMI.2017.2743819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/TMI.2017.2743819", "name": "item", "description": "10.1109/TMI.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/TMI.2017.2743819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "10.1109/tmi.2017.2743819", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-07-28T16:18:32Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/10.1109/tmi.2017.2743819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/tmi.2017.2743819", "name": "item", "description": "10.1109/tmi.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/tmi.2017.2743819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "10.3390/rs11080913", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-28T16:20:47Z", "type": "Journal Article", "created": "2019-04-15", "title": "Multispectral Contrast of Archaeological Features: A Quantitative Evaluation", "description": "This study provides an evaluation of spectral responses of hollow ways in Upper Mesopotamia. Hollow ways were used for the transportation of animals, carts, and other moving agents for centuries. The aim is to show how the success of spectral indices varies in describing topologically simple features even in a seemingly homogeneous geographic unit. The variation is further highlighted under the changing precipitation regime. The methodology begins with an exploration of the relationship between the date of a multispectral scene and the visibility of hollow ways. The next step is to evaluate the impact of rainfall levels on numerous indices and to quantify spectral contrast. The contrast between a hollow way and its background is evaluated with Welch\uffe2\uff80\uff99s t-test and the association between precipitation regime and spectral responses of hollow ways are investigated with Correspondence Analysis and Fisher\uffe2\uff80\uff99s test. Results highlight an intrinsic relationship between the precipitation regime and the ways in which archaeological features reflects and/or emits electromagnetic energy. Next, the categorization of spectral indices based on different rainfall levels can be used as a guidance in future studies. Finally, the study suggests contrast becomes an even more fruitful concept as one moves from the spatial domain to the spectral domain.
", "keywords": ["Random Forests", "Lidar", "satellite remote sensing", "Science", "Q", "0211 other engineering and technologies", "Effectiveness of data fusion", "06 humanities and the arts", "02 engineering and technology", "Data fusion", "910", "15. Life on land", "archaeology of roads", "precipitation regime", "Imaging spectroscopy", "Precipitation regime", "spectral contrast", "Hollow ways", "Natura 2000 habitat", "13. Climate action", "Satellite remote sensing", "Upper Mesopotamia", "0601 history and archaeology", "Spectral contrast", "hollow ways"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/8/913/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/390208/1/prod_402195-doc_199283.pdf"}, {"href": "http://dro.dur.ac.uk/27994/1/27994.pdf"}, {"href": "http://dro.dur.ac.uk/27994/2/27994.pdf"}, {"href": "https://www.mdpi.com/2072-4292/11/8/913/pdf"}, {"href": "https://doi.org/10.3390/rs11080913"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs11080913", "name": "item", "description": "10.3390/rs11080913", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11080913"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-15T00:00:00Z"}}, {"id": "10.1111/nph.14705", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:18:59Z", "type": "Journal Article", "created": "2017-07-31", "title": "High\u2010resolution synchrotron imaging shows that root hairs influence rhizosphere soil structure formation", "description": "Summary
In this paper, we provide direct evidence of the importance of root hairs on pore structure development at the root\uffe2\uff80\uff93soil interface during the early stage of crop establishment.
This was achieved by use of high\uffe2\uff80\uff90resolution (c. 5\uffc2\uffa0\uffce\uffbcm) synchrotron radiation computed tomography (SRCT) to visualise both the structure of root hairs and the soil pore structure in plant\uffe2\uff80\uff93soil microcosms. Two contrasting genotypes of barley (Hordeum vulgare), with and without root hairs, were grown for 8\uffc2\uffa0d in microcosms packed with sandy loam soil at 1.2\uffc2\uffa0g\uffc2\uffa0cm\uffe2\uff88\uff923 dry bulk density. Root hairs were visualised within air\uffe2\uff80\uff90filled pore spaces, but not in the fine\uffe2\uff80\uff90textured soil regions.
We found that the genotype with root hairs significantly altered the porosity and connectivity of the detectable pore space (>\uffc2\uffa05\uffc2\uffa0\uffce\uffbcm) in the rhizosphere, as compared with the no\uffe2\uff80\uff90hair mutants. Both genotypes showed decreasing pore space between 0.8 and 0.1\uffc2\uffa0mm from the root surface. Interestingly the root\uffe2\uff80\uff90hair\uffe2\uff80\uff90bearing genotype had a significantly greater soil pore volume\uffe2\uff80\uff90fraction at the root\uffe2\uff80\uff93soil interface.
Effects of pore structure on diffusion and permeability were estimated to be functionally insignificant under saturated conditions when simulated using image\uffe2\uff80\uff90based modelling.
Soil adjacent to roots has distinct structural and physical properties from bulk soil, affecting water and solute acquisition by plants. Detailed knowledge on how root activity and traits such as root hairs affect the three\uffe2\uff80\uff90dimensional pore structure at a fine scale is scarce and often contradictory.
Roots of hairless barley (Hordeum vulgare L. cv Optic) mutant (NRH) and its wildtype (WT) parent were grown in tubes of sieved (<250\uffc2\uffa0\uffce\uffbcm) sandy loam soil under two different water regimes. The tubes were scanned by synchrotron\uffe2\uff80\uff90based X\uffe2\uff80\uff90ray computed tomography to visualise pore structure at the soil\uffe2\uff80\uff93root interface. Pore volume fraction and pore size distribution were analysed vs distance within 1\uffc2\uffa0mm of the root surface.
Less dense packing of particles at the root surface was hypothesised to cause the observed increased pore volume fraction immediately next to the epidermis. The pore size distribution was narrower due to a decreased fraction of larger pores. There were no statistically significant differences in pore structure between genotypes or moisture conditions.
A model is proposed that describes the variation in porosity near roots taking into account soil compaction and the surface effect at the root surface.
", "keywords": ["name=Physiology", "STABILIZATION", "Physiology", "EP/M020355/1", "Supplementary Data", "QH301 Biology", "Plant Science", "Supplementary data available", "Plant Roots", "630", "noninvasive imaging", "Soil", "646809DIMR", "STRENGTH", "BB/J00868/1", "Hordeum vulgare", "2. Zero hunger", "04 agricultural and veterinary sciences", "Rhizosphere", "COMPRESSION", "soil structure", "Porosity", "European Research Council", "/dk/atira/pure/subjectarea/asjc/1300/1314", "/dk/atira/pure/subjectarea/asjc/1100/1110", "root hairs", "COMPACTION", "QH301", "Imaging", " Three-Dimensional", "synchrotron", "particle packing", "SOIL-STRUCTURE", "BB/L025620/1", "WATER-STRESS", "NE/L00237/1", "580", "ELONGATION", "Civil_env_eng", "Natural Environment Research Council (NERC)", "POROSITY", "Water", "Hordeum", "15. Life on land", "Engineering and Physical Sciences Research Council (EPSRC)", "Mutation", "Biotechnology and Biological Sciences Research Council (BBSRC)", "0401 agriculture", " forestry", " and fisheries", "PENETRATION", "name=Plant Science", "rhizosphere", "Tomography", " X-Ray Computed", "MAIZE", "BB/P004180/1", "Synchrotrons", "BB/L025825/1"]}, "links": [{"href": "https://repository.uwl.ac.uk/id/eprint/5489/1/AS6808504337817661539338801587_content_1.pdf"}, {"href": "https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.15516"}, {"href": "https://doi.org/10.1111/nph.15516"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/nph.15516", "name": "item", "description": "10.1111/nph.15516", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/nph.15516"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-11-20T00:00:00Z"}}, {"id": "10.1111/pce.12983", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:19:01Z", "type": "Journal Article", "created": "2017-05-15", "title": "Quantification of root water uptake in soil using X\u2010ray computed tomography and image\u2010based modelling", "description": "AbstractSpatially averaged models of root\uffe2\uff80\uff93soil interactions are often used to calculate plant water uptake. Using a combination of X\uffe2\uff80\uff90ray computed tomography (CT) and image\uffe2\uff80\uff90based modelling, we tested the accuracy of this spatial averaging by directly calculating plant water uptake for young wheat plants in two soil types. The root system was imaged using X\uffe2\uff80\uff90ray CT at 2, 4, 6, 8 and 12\uffc2\uffa0d after transplanting. The roots were segmented using semi\uffe2\uff80\uff90automated root tracking for speed and reproducibility. The segmented geometries were converted to a mesh suitable for the numerical solution of Richards' equation. Richards' equation was parameterized using existing pore scale studies of soil hydraulic properties in the rhizosphere of wheat plants. Image\uffe2\uff80\uff90based modelling allows the spatial distribution of water around the root to be visualized and the fluxes into the root to be calculated. By comparing the results obtained through image\uffe2\uff80\uff90based modelling to spatially averaged models, the impact of root architecture and geometry in water uptake was quantified. We observed that the spatially averaged models performed well in comparison to the image\uffe2\uff80\uff90based models with <2% difference in uptake. However, the spatial averaging loses important information regarding the spatial distribution of water near the root system.Recent advances in measuring and modeling root water uptake along with refined electrical petrophysical models may help fill the existing gap in hydrological root model parametrization. In this paper, we discuss the choices to be made to combine root-zone hydrology and geoelectrical data with the aim of characterizing the active root zone. For each model and observation type we discuss sources of uncertainty and how they are commonly addressed in a stochastic inversion framework. We point out different degrees of integration in the existing hydrogeophysical approaches to parametrize models of root-zone hydrology. This paper aims at giving emphasis to stochastic approaches, in particular to Data Assimilation (DA) schemes, that are generally identified as the best way to combine geoelectrical data with Root Water Uptake (RWU) models. In addition, the study points out a more suitable objective function taken from the optimal transport theory that better captures complex geometry of root systems. Another pathway for improvement of geoelectrical data integration into RWU models using DA relies on the use of stem based methods as a leverage to introduce more extensive root knowledge into RWU macroscopic hydrological models.
", "keywords": ["hydrogeophysics", "0208 environmental biotechnology", "0207 environmental engineering", "02 engineering and technology", "data assimilation; geoelectrical imaging; hydrogeophysics; inversion; root water uptake; soil-plant modeling; Wasserstein distance", "Environmental technology. Sanitary engineering", "root water uptake", "Hydrogeophysics", "inversion", "geoelectrical imaging", "soil-plant modeling", "Wasserstein distance", "data assimilation", "TD1-1066"]}, "links": [{"href": "https://www.research.unipd.it/bitstream/11577/3410667/1/frwa-03-767910.pdf"}, {"href": "https://doi.org/10.3389/frwa.2021.767910"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/frwa.2021.767910", "name": "item", "description": "10.3389/frwa.2021.767910", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/frwa.2021.767910"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-14T00:00:00Z"}}, {"id": "10.3390/drones9020129", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-28T16:20:39Z", "type": "Journal Article", "created": "2025-02-11", "title": "Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms", "description": "This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.
Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.
Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 \u00b5m, and 218, centered at 2.382 \u00b5m. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.
Remote sensing of phenology is adopted as the practice in greenery monitoring. Now research is turned towards the fusion of data from various sensors to fill in the gap in time series and allow monitoring of pests and disturbances. Poplar species were monitored for the determination of the best approach for detecting phenology and disturbances. With the adjustments that include a choice of indices, wavelengths, and a setup, a multispectral camera may be used to calibrate satellite images. The image processing pipeline included different denoising and interpolation methods. The correlation of the changes in a signal of top and lateral imaging proved that the contribution of the whole canopy is reflected in satellite images. Normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) successfully distinguished among phenophases and detected leaf miner presence, unlike enhanced vegetation index (EVI). Changes in the indices were registered before, during, and after the development of the disease. NDRE is the most sensitive as it distinguished among the different intensities of damage caused by pests but it was not able to forecast its occurrence. An efficient and accurate system for detection and monitoring of phenology enables the improvement of the phenological models\u2019 quality and creates the basis for a forecast that allows planning in various disciplines.
Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.
Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.
Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\uffe2\uff80\uff89km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\uffe2\uff80\uff89km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\uffe2\uff80\uff932018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\uffe2\uff80\uff932018). The field was seeded for the 2014\uffe2\uff80\uff932015 (S1), 2016\uffe2\uff80\uff932017 (S2) and 2017\uffe2\uff80\uff932018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\uffe2\uff80\uff932016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \uffce\uffb1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \uffce\uffb1PT remains at a mostly constant value (\uffe2\uff88\uffbc\uffe2\uff80\uff890.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\uffe2\uff80\uff89W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\uffe2\uff80\uff89W/m2 for S1, S2, S3 and B1 respectively.
", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.5194/hess-24-1781-2020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-24-1781-2020", "name": "item", "description": "10.5194/hess-24-1781-2020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-24-1781-2020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "3130873339", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-28T16:25:08Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "AbstractOur understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.
", "keywords": ["Technology and Engineering", "Markov random field", "LORAKS", "Chemical technology", "TP1-1185", "02 engineering and technology", "image reconstruction", "Article", "NETWORKS", "magnetic resonance imaging; Markov random field; image reconstruction", "03 medical and health sciences", "0302 clinical medicine", "0202 electrical engineering", " electronic engineering", " information engineering", "magnetic resonance imaging", "MAGE-RECONSTRUCTION"], "contacts": [{"organization": "Pani\u0107, M., Jakoveti\u0107, Du\u0161an, Vukobratovi\u0107, Dejan, Crnojevi\u0107, Vladimir, Pi\u017eurica, Aleksandra,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/1424-8220/20/11/3185/pdf"}, {"href": "https://doi.org/10.5281/zenodo.3957267"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3957267", "name": "item", "description": "10.5281/zenodo.3957267", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3957267"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4005769", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-28T16:22:43Z", "type": "Report", "title": "A case study on prediction of sensitivity of tomato sepals to fungal infection using hyperspectral imaging", "description": "Tomato quality is dependent on growing conditions and chain conditions like humidity and temperature, as well as crop handling during harvest and post-harvest processes (transport, packaging, storage etc.). Like many other perishable fruits and vegetables, it is highly prone to postharvest losses, reaching up to 30% in some developing countries. Tomato is known to be highly susceptible to pathogenic fungi, such as Penicillium, Aspergillus and Mucor, which tend to attack crops with high moisture and nutrient content. Tomato tissue cell damage can occur due to changes in environmental conditions as well as damage during product handling. Such damage creates potential entrance for fungal spores which, given appropriate germination conditions, may infect stem, calyx,Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.
Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 \u00b5m, and 218, centered at 2.382 \u00b5m. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.
Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.