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Text-fig. 2. Salicaceae (a–g), Cannabaceae (h–n), cf. Betulaceae (o–r). a–g: Saxifragispermum, USNM PAL 772341. Scale bar = 5 mm except as indicated. a–b: Lateral, c: apical, and d: basal views of fruit, reflected light, palladium coated; apex at top of (a, b). e: Equatorial transverse section reflected light; arrows indicate presumed seeds, scale bar = 2 mm. f: Detail of locule contents extracted from (e), transmitted light, scale bar = 200 Μm. g: Interwoven trichomes or fibers from locule, transmitted light, scale bar = 5 Μm. h–j: Celtis. h, i: USNM PAL 772342, reflected light, palladium coated, scale bar = 5 mm. h: Lateral view parallel with plane of dehiscence. i: Lateral view perpendicular to plane of dehiscence. j: DMNH EPI.47809, Celtis in lateral view; showing reticulate sculpture and the vertically-oriented, plane of dehiscence (arrow), scale bar = 5 mm. k–m: Aphananthe. USNM PAL 772344, reflected light, palladium coated, scale bar = 5 mm. k: Apical view, note triangular cross section and apical plug (arrow). l: Lateral view, apex up. m: Lateral view at 90° to (l). n: Detail of cellular pattern at surface of endocarp, scale bar = 0.5 mm. o–r: in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 2. Salicaceae (a–g), Cannabaceae (h–n), cf. Betulaceae (o–r). a–g: Saxifragispermum, USNM PAL 772341. Scale bar = 5 mm except as indicated. a–b: Lateral, c: apical, and d: basal views of fruit, reflected light, palladium coated; apex at top of (a, b). e: Equatorial transverse section reflected light; arrows indicate presumed seeds, scale bar = 2 mm. f: Detail of locule contents extracted from (e), transmitted light, scale bar = 200 Μm. g: Interwoven trichomes or fibers from locule, transmitted light, scale bar = 5 Μm. h–j: Celtis. h, i: USNM PAL 772342, reflected light, palladium coated, scale bar = 5 mm. h: Lateral view parallel with plane of dehiscence. i: Lateral view perpendicular to plane of dehiscence. j: DMNH EPI.47809, Celtis in lateral view; showing reticulate sculpture and the vertically-oriented, plane of dehiscence (arrow), scale bar = 5 mm. k–m: Aphananthe. USNM PAL 772344, reflected light, palladium coated, scale bar = 5 mm. k: Apical view, note triangular cross section and apical plug (arrow). l: Lateral view, apex up. m: Lateral view at 90° to (l). n: Detail of cellular pattern at surface of endocarp, scale bar = 0.5 mm. o–r:
Text-fig. 10. Carpolithes (a–j). a–d: Carpolithes sp. 10. USNM PAL 772375. Scale bar = 5 mm, reflected light, palladium coated. a: Lateral view of one face of structure; note adherent mineral material. Longitudinal groove is to right. b: Lateral view of one edge of the structure. c: Opposite view from (b), note groove in upper half of the specimen, facing viewer. d: Apical view. e–j: Carpolithes sp. 11. USNM PAL 772376. Scale bar = 5 mm, micro-CT scan surface views. e: Structure in face view showing central protuberance. f: Same, lateral view. g: Opposite face from (e). h: Opposite face from (f). i: View from one end. j: View from opposite end from (i). in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 10. Carpolithes (a–j). a–d: Carpolithes sp. 10. USNM PAL 772375. Scale bar = 5 mm, reflected light, palladium coated. a: Lateral view of one face of structure; note adherent mineral material. Longitudinal groove is to right. b: Lateral view of one edge of the structure. c: Opposite view from (b), note groove in upper half of the specimen, facing viewer. d: Apical view. e–j: Carpolithes sp. 11. USNM PAL 772376. Scale bar = 5 mm, micro-CT scan surface views. e: Structure in face view showing central protuberance. f: Same, lateral view. g: Opposite face from (e). h: Opposite face from (f). i: View from one end. j: View from opposite end from (i).
Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows). in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 6. Cornaceae. Alangium (a–e), Mastixia (f–r). a–e: Alangium, DMNH EPI.47806. Scale bar = 1 cm. b, e: Reflected light, palladium coated. a, c, d: Micro-CT scan surface rendering. a: Locule cast, face view of slightly larger locule. b: Face view of slightly smaller locule. c: Lateral view of the endocarp, the slightly enlarged left carpel separated from the smaller carpel by a longitudinal septal groove; the faint pitting in the groove suggestive of the septal vasculature. d, e: Views of either end of the endocarp, illustrating the size difference between the two carpels and the pitting in the septal groove suggestive of the septal vasculature. f–k: Mastixia USNM PAL 772362. Scale bar = 1 cm. f, g, j, k: reflected light, palladium coated; h, i: micro-CT scan surface rendering. f: Lateral view of endocarp, inferred dorsal germination valve groove facing the viewer. Note irregular, rugose, longitudinal ridges. g: Lateral view of endocarp, inferred germination valve with median longitudinal groove to left. h: Lateral view of endocarp reoriented with the same longitudinal groove to the right. i: Lateral view, rotated to ventral surface. j: View of one end of the endocarp, germination valve groove up. k: Opposite end view, with prominent radial ridges and intervening grooves, germination valve groove up. l–r: Mastixia USNM PAL 772363. Scale bar = 1 cm. l: View of one face of endocarp, displaying a groove that may represent the surficial expression of the dorsal infold of a Mastixia-like germination valve. Surface badly eroded, reflected light, palladium coated. m: Opposite face of endocarp displaying extensive erosion and a central hole interpreted as feeding damage. n: Lateral view; m, n micro-CT scan surface renderings. o: A view of one end, displaying the prominent groove, reflected light, palladium coated. p: Opposite end to (o). q: View as in (o); p, q micro-CT scan surface renderings. r: Virtual transverse section showing curved locule (arrows).
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings.
Text-fig. 9. Carpolithes (a–r). a–d: Carpolithes sp. 5. USNM PAL 772370. Scale bar = 5 mm, reflected light, palladium coated. a: Lateral view of seed, apex up, possible raphe descending from apex toward viewer. b: Lateral view of seed, apex up, possible raphe on right. c: Lateral view, opposite side, apex up, possible raphe on left. d: Apical view, note central pit with raphe descending towards bottom margin. e–h: Carpolithes sp. 6. USNM PAL 772371. Scale bar = 5 mm. e: Basal view illustrating depression and keel in plane of bisymmetry, reflected light, palladium coated. f–h: Micro-CT scan surface rendering. f: Lateral view showing relatively smooth rounded surface. g: Specimen rotated 180° from (f), surface partially eroded. h: Longitudinal view, showing median keel. i–m: Carpolithes sp. 7 USNM PAL 772372. Scale bar = 5 mm. i: View of intact face of globose fruit, possible apical constriction at top. j: Lateral view, intact surface to right, possible apical constriction at top, both micro-CT scan surface renderings. k: Apical view. l: Face view illustrating the mineral filling and the fine, radiating structure of the fruit wall on the left and right margins, both reflected light, palladium coated. m: Closeup of the cellular layer on the left of (l), micro-CT scan surface rendering. n–p: Carpolithes sp. 8. USNM PAL 772373. Scale bar = 3 mm, reflected light, palladium coated. n: Lateral view of pyrene-like structure, one ridge running vertically in the center of view, the other two forming the left and right margins. o: Lateral view of pyrene-like structure, ridge in (n) on the left. p: End-on view illustrating one convex, one concave, and one relatively flat to very slightly concave face. q, r: Carpolithes sp. 9 USNM PAL 772374. Scale bar = 5 mm, reflected light, palladium coated. q: Exterior of the smooth broken half-sphere. r: Interior of the broken half-sphere. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 9. Carpolithes (a–r). a–d: Carpolithes sp. 5. USNM PAL 772370. Scale bar = 5 mm, reflected light, palladium coated. a: Lateral view of seed, apex up, possible raphe descending from apex toward viewer. b: Lateral view of seed, apex up, possible raphe on right. c: Lateral view, opposite side, apex up, possible raphe on left. d: Apical view, note central pit with raphe descending towards bottom margin. e–h: Carpolithes sp. 6. USNM PAL 772371. Scale bar = 5 mm. e: Basal view illustrating depression and keel in plane of bisymmetry, reflected light, palladium coated. f–h: Micro-CT scan surface rendering. f: Lateral view showing relatively smooth rounded surface. g: Specimen rotated 180° from (f), surface partially eroded. h: Longitudinal view, showing median keel. i–m: Carpolithes sp. 7 USNM PAL 772372. Scale bar = 5 mm. i: View of intact face of globose fruit, possible apical constriction at top. j: Lateral view, intact surface to right, possible apical constriction at top, both micro-CT scan surface renderings. k: Apical view. l: Face view illustrating the mineral filling and the fine, radiating structure of the fruit wall on the left and right margins, both reflected light, palladium coated. m: Closeup of the cellular layer on the left of (l), micro-CT scan surface rendering. n–p: Carpolithes sp. 8. USNM PAL 772373. Scale bar = 3 mm, reflected light, palladium coated. n: Lateral view of pyrene-like structure, one ridge running vertically in the center of view, the other two forming the left and right margins. o: Lateral view of pyrene-like structure, ridge in (n) on the left. p: End-on view illustrating one convex, one concave, and one relatively flat to very slightly concave face. q, r: Carpolithes sp. 9 USNM PAL 772374. Scale bar = 5 mm, reflected light, palladium coated. q: Exterior of the smooth broken half-sphere. r: Interior of the broken half-sphere.
North American Coastal Plain PRISMA Surface Reflectance and Mixture Residual Spectra
<p>The data available here include the training and validation spectra for creating the models for the currently unpublished manuscript “Classifying Plant Communities in the North American Coastal Plain with PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual." Spectral data contain both raw surface reflectance (SR) and spectral mixture residual spectra (MR) transformed with endmembers and code from Sousa et al.'s (2022) paper titled "The spectral mixture residual: A source of low‐variance information to enhance the explainability and accuracy of surface biology and geology retrievals." Spectra represent averaged 60 m x 60 m areas (2 x 2-pixel window) located in the Red Hills (RH), the Jones Ecological Research Center (JERC), the Ordway-Swisher Biological Station (OSBS), and the Disney Wilderness Preserve (DSNY). To maintain the confidentiality of private property information on behalf of landowners, the locations of the RH plots were intentionally obscured, considering the nature of the region. See manuscript for further details. </p>
Text-fig. 10. Scanning electron microscope (SEM) images of conifer seeds (a, b) and pollen (c) and monoporate pollen of unknown affinity (d–j); Torres Vedras locality, Portugal. a, b) Unnamed conifer seeds (conifer seed sp. 1); c) Clump of bisaccate pollen grains; d) Fragment with microsporangia that yielded the pollen in (e–j); e, f, h) Monoporate pollen grains folded in various ways, exposing the tiny pore (e, h, arrowheads) or resembling a monocolpate grain (f); g) Detail of pollen grain showing pore (arrowhead) and finely rugulate exine surface that reflects the reticulate infratectal layer beneath the thin tectum; i) Detail of pore showing very slightly thickened margin; j) Spherical orbicules on the surface of two grains. Specimens, TV44-S174594 (a), TV44-S174595 (b), TV44-S174573 (c), TV44-S137904 (d–j). Scale bars 1 mm (a, b), 300 Μm (d), 100 Μm (c), 6 Μm (e, f, h), 3 Μm (g, j), 1.5 Μm (i). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 10. Scanning electron microscope (SEM) images of conifer seeds (a, b) and pollen (c) and monoporate pollen of unknown affinity (d–j); Torres Vedras locality, Portugal. a, b) Unnamed conifer seeds (conifer seed sp. 1); c) Clump of bisaccate pollen grains; d) Fragment with microsporangia that yielded the pollen in (e–j); e, f, h) Monoporate pollen grains folded in various ways, exposing the tiny pore (e, h, arrowheads) or resembling a monocolpate grain (f); g) Detail of pollen grain showing pore (arrowhead) and finely rugulate exine surface that reflects the reticulate infratectal layer beneath the thin tectum; i) Detail of pore showing very slightly thickened margin; j) Spherical orbicules on the surface of two grains. Specimens, TV44-S174594 (a), TV44-S174595 (b), TV44-S174573 (c), TV44-S137904 (d–j). Scale bars 1 mm (a, b), 300 Μm (d), 100 Μm (c), 6 Μm (e, f, h), 3 Μm (g, j), 1.5 Μm (i).
Text-fig. 46. Scanning electron microscope (SEM) images of "Rugulate fruit"; Catefica locality, Portugal. a, b) Lateral (a) and ventral (b) views of a rugulate fruit showing the short extension of the sessile stigma at the apex (arrows); c) Irregular surface of fruit that probably reflects an irregular endocarp; note the isodiametric outlines of the epidermal cells; d) Semi-tectate reticulate pollen grains embedded in the remains of a secretion on the stigmatic surface. Specimen, Catefica 154-S101291 (a–d). Scale bars = 300 Μm (a, b), 100 Μm (c), 6 Μm (d). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 46. Scanning electron microscope (SEM) images of "Rugulate fruit"; Catefica locality, Portugal. a, b) Lateral (a) and ventral (b) views of a rugulate fruit showing the short extension of the sessile stigma at the apex (arrows); c) Irregular surface of fruit that probably reflects an irregular endocarp; note the isodiametric outlines of the epidermal cells; d) Semi-tectate reticulate pollen grains embedded in the remains of a secretion on the stigmatic surface. Specimen, Catefica 154-S101291 (a–d). Scale bars = 300 Μm (a, b), 100 Μm (c), 6 Μm (d).
Polarization dataset - Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies
<p>This data are relative to the polarization measurements of the paper </p> <p>"Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies"</p> <p>The dataset consists in 8 .txt files that represent the polarization measurements of mixtures of FeS and Olivine (ol) with different mass ratios. In the name of each file it is specified the mass percentage of the two components respect with the total mass of the sample (eg. data_ol_FeS_10-90_530.txt is the sample composed by 10% olivine and 90% FeS, measured at 530 nm). </p> <p>In each file, the data are organized in the following columns: </p> <p>#phase_angles[°] #Q/I #U/I #V/I #DOLP #DC #delta_Q/I #delta_U/I #delta_V/I #delta_DOLP #delta_dc</p> <p>"delta" refers to the standard deviation of the measurement upon rotation of the sample on the azimuthal axis. <br> </p>
De La Salle University – Outdoor Mirrors and Reflective Surfaces (DLSU-OMRS) Dataset
<p><strong>The De La Salle University – Outdoor Mirrors and Reflective Surfaces (DLSU-OMRS) dataset contains 454 images of outdoor mirrors and reflective surfaces, along with their corresponding ground-truth masks for segmentation</strong>. The images were scraped from Shutterstock using the key phrases <em>outdoor mirror</em> and <em>street mirror</em> and manually filtered to remove duplicates and heavily manipulated photos. Ground-truth masks were produced through manual segmentation.</p> <p>The images have their respective licenses, and the ground-truth masks are licensed under the BSD 3-Clause "New" or "Revised" License. The use of this dataset is restricted to noncommercial purposes only.</p> <p>More details can be found in the paper "<strong>Designing a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfaces</strong>," which was accepted for full paper presentation at the <strong>2023 International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2023)</strong>. The project page is <a href="https://github.com/memgonzales/mirror-segmentation">https://github.com/memgonzales/mirror-segmentation</a>. The paper is published in <em>Computer Science Research Notes</em>: <a href="http://wscg.zcu.cz/WSCG2023/full/E59-full.pdf">http://wscg.zcu.cz/WSCG2023/full/E59-full.pdf</a>.</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Laboratory-based hyperspectral visible near-infrared reflectance spectral dataset of soil samples across a range of surface orientations
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Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 2001-2023
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.2 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with MODIS surface reflectance (LCSPP-MODIS), 2001-2023
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the MODIS record from 2001–2023. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals. The MODIS-based LCSPP is generated as an ancillary product to complement and benchmark the LCSPP-AVHRR product from 1982-2023.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>LCSPP-AVHRR repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p> <p> </p>
Modelling the transmission component in TIR reflectance spectra of sandstones to understand the effect of surface roughness and clinging fines
<p>This dataset includes a model that combines rock surface reflection with transmission through clinging fines the surface. All IDL scripts are provided. The dataset includes the transmission input spectra, raw data of transmission measurements and SEM images of the surfaces. The reflectance spectra presented in the paper are part of a previous publication, see related identifiers for database of this dataset.</p>
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
Seismic interpretation of key stratigraphic and structural surfaces, and crustal faults within the Galicia 3-D reflection survey
<p>This repository contains all the seismic interpretations utilized for the analysis in the article "<em>Origin of serpentinization patterns beneath the S-reflector detachment fault in the Galicia margin, offshore Spain</em>". </p> <p>The "<em>Surfaces</em>" file contains the CPS-3 shape files of the major stratigraphic and structural surfaces (seafloor, base of post-rift sedimentary strata, base of pre/syn-rift sedimentary strata, base of crystalline basement, S-reflector detachment fault, Moho). The "<em>Faults</em>" file contains the interpretations of the major crustal faults overlying the S-reflector detachment. The "<em>Data</em>" file contains the spatial boundary of where the S-reflector is the crust-mantle boundary, the P-wave velocities of Schuba et al. (2019) and calculated degree of serpentinization (Schuba et al., submitted) based on Christensen's (2004) 200 MPa/200<sup>o</sup>C serpentinite compilation study. </p> <p>All seismic interpretations were carried out on Petrel<sup>TM</sup> versions 2015 and 2017. The seismic reflection volume that was interpreted can be found at https://doi.org/10.1594/IEDA/500151.</p> <p> </p> <p>References: </p> <ul> <li>Christensen, N.I. (2004). Serpentinites, Peridotites, and Seismology. <em>International Geology Review</em>, <em>46</em>(9), 795-816. https://doi.org/10.2747/0020-6814.46.9.795</li> <li>Schuba, C.N., Schuba, J.P. Gray, G.G., and Davy, R.G., (2019). Interface targeted velocity estimation using machine learning. <em>Geophysical Journal International</em>, <em>218</em>(1), 45-56. https://doi.org/10.1093/gji/ggz142</li> <li>Schuba, C.N., Gray, G.G., Morgan, J.K., Schuba, J.P., and Sawyer, D.S., (submitted). Interface targeted velocity estimation using machine learning. <em>Geochemistry, Geophysics, Geosystems.</em></li> </ul>
Seasonal surface reflectance mosaics of Blackhawk Island, Wisconsin May-October 2018
<p>This dataset contains high-resolution hyperspectral surface reflectance mosaics collected over Blackhawk Island, Wisconsin, USA. Images were collected at eight dates during the 2018 growing season (May – October) using a VNIR-SWIR (400–2400nm) HySpex airborne imaging system at 1m spatial resolution.</p>
Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo within the Fully Coupled CESM2
<p>CESM2 simulations were performed to study the light attenuation effects at the ocean surface layer on Arctic surface temperature. This dataset provides some simulated variables analyzed in our study.</p>
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