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72 results for “Surface reconstruction”
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
Data for: Machine-learning-accelerated simulations enable heuristic-free surface reconstruction
<p>This is the dataset for the publication "Machine-learning-accelerated simulations to enable automatic surface reconstruction", by X. Du, J.K. Damewood, J.R. Lunger, R. Millan, B. Yildiz, L. Li, and R. Gómez-Bombarelli. The repository contains the density-functional theory (DFT) data used to train the neural network force fields (NFF), selected results from our GaN(0001), Si(111), and SrTiO3(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for analysis and plots. To run the .ipynb's, you will need to install <a href="https://github.com/learningmatter-mit/surface-sampling">surface-sampling</a> (tested up to commit 02820d339eed6291b6af6ccb809f154ad6244110 on master) and <a href="https://github.com/learningmatter-mit/NeuralForceField">NeuralForceField</a> (tested up to commit 72d1f32f43f202c1a466116beeed15845a6456e7 on master) from the <a href="https://github.com/learningmatter-mit">Rafael Gómez-Bombarelli Group @ MIT</a>.</p>
LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)
<p>3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps).</p> <p>Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016).</p> <p>Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)</p>
Video S1 - GEXP02 surface reconstruction
<p>Transgenic Plasmodium falciparum stage III gametocyte expressing GEXP02-HA and PF3D7_0424600-GFP treated with paraformaldehyde and glutaraldehyde and then labeled with antibodies against PF3D7_0936800 (green), HA-tag (red), and GFP-tag (blue). Sections obtained with a confocal microscope were visualized in Imaris and a surface resconstruction of all three channels was obtained.</p>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
Reconstructed remote sensing land surface temperature data in North America in 2002-2018
<p>In order to more accurately study the change trend of land surface temperature in North America in recent years, we combined remote sensing and meteorological station data and used various restoration models to generate more accurate and more complete remote sensing land surface temperature data. Our data covered the North American continent from 2002 to 2018, with a spatial resolution of 0.05°×0.05°. In order to facilitate the statistics of the data, we set the projection mode of the data as World_Cylindrical_Equal_Area. We collated the data from different time dimensions, including month, season and year.</p>
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling).</p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the 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. We also </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 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</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 v3.1 (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>
Multiproxy Reconstruction of Pliocene North Atlantic Sea Surface Temperatures and Implications for Rainfall in North Africa
<p>This dataset accompanies the publication by Wycech et al. "Multiproxy Reconstruction of Pliocene North Atlantic Sea Surface Temperatures and Implications for Rainfall in North Africa" in <em>Paleoceanography and Paleoclimatology</em>. The dataset is comprised of the raw paleo-proxy (Mg/Ca ratios and U<sup>k’</sup><sub>37</sub>) data and reconstructed sea surface temperatures (SSTs) from the early Pliocene (5 Ma) to modern. The provided data were input into the accompanying R codes, which executed principal component analysis and generated the results described in Wycech et al.</p>
Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions
<p>The archive "dataset.tar.gz" contains trained models (neural networks), training-, validation- and test-data and selected structures in POSCAR format, obtained in the neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions.</p> <p>See README for more information on the archive content.</p>
Text-fig. 5. Reconstruction drawing of Pterigophycos sp. thallus growing on a rock surface (blades slightly simplified, details deemphasized). in A Whole-Plant Specimen Of The Marine Macroalga Pterigophycos From The Eocene Of Bolca (Veneto, N-Italy)
Text-fig. 5. Reconstruction drawing of Pterigophycos sp. thallus growing on a rock surface (blades slightly simplified, details deemphasized).
Supplementary files for the article "Reconstruction of the surface temperature employing models with different complexity levels: a study for the mid-Holocene."
<p>Dear members of the scientific community,</p> <p>This dataset contains all data and scripts used to prepare the manuscript "Reconstruction of the Surface Temperature Employing Models with Different Complexity Levels: A Study for the Mid-Holocene." </p> <p>Notice that the compressed file contains folders for the mid-Holocene and pre-industrial scenarios and the climatologies of the scenarios' differences. To sum up, we provide each model output adopted in this study and their ensembles: high-complexity models (HCM), Reduced-complexity models (RCM), and All-complexity models (ACM). The statistics and plots can be generated by running the R scripts within the "R_script" folder.</p> <p><br>Best regards,</p> <p>Emerson D. Oliveira</p>
Data assimilation-based surface temperature reconstructions over the last two millennia over Antarctica
<p>This dataset contains data assimilation-based temperature and δ<sup>18</sup>O reconstructions in 10 Antarctic regions over the last two millennia, presented in :</p> <blockquote> <p><a href="https://www.clim-past-discuss.net/cp-2018-90/">Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018. </a></p> </blockquote> <p>We use a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al., 2017) to constrain model ensembles derived from two simulations: one performed using ECHAM5-MPI-OM that covers the period 800-1999 CE with a horizontal resolution of 3.75° by 3.75° (Sjolte et al., 2018), and the other performed with ECHAM5-wiso, spanning 1871-2011 CE at 1.125° spatial resolution (Steiger et al., 2017). This latter simulation is available <a href="https://zenodo.org/record/1249604#.XHa824Uo_RY">here</a>.</p> <p>Four netCDF files are available:</p> <ol> <li>d18O_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based δ<sup>18</sup>O reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>ts_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>d18O_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based δ<sup>18</sup>O reconstructions using the model ensemble derived from ECHAM5-wiso</li> <li>ts_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-wiso</li> </ol> <p>The variables included in the NetCDF files are:</p> <ul> <li>region: integers from 1 to 10 corresponding to the ID of the ten reconstructions targets, that were defined in Stenni et al. (2017): <ul> <li>1: East Antarctic Plateau</li> <li>2: Wilkes Land Coast</li> <li>3: Weddell Sea Coast</li> <li>4: Antarctic Peninsula</li> <li>5: West Antarctic Ice Sheet</li> <li>6: Victoria Land Coast-Ross Sea</li> <li>7: Dronning Maud Land Coast</li> <li>8: West Antarctica</li> <li>9: East Antarctica</li> <li>10: Antarctica</li> </ul> </li> <li>time: integers from 1 to 2015, corresponding to the years CE covered by the reconstructions</li> <li>DA_ts (or DA_d18O): data assimilation-based reconstructed surface temperature (or δ<sup>18</sup>O). The values are annual means and are given in anomalies computed over full period. The units are degrees celsius (or permil). </li> <li>DA_ts_std (or DA_d18O_std): Weighted standard deviation of the particles used for reconstructing temperature (or δ<sup>18</sup>O). The units are degrees celsius (or permil).</li> </ul> <p>For a detailed description of the experimental design, please see the associated publication (Klein et al., 2018). Don't hesitate to contact <a href="mailto:francois.klein@uclouvain.be">François Klein</a> for more information.</p> <p>References</p> <p>Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018.</p> <p>Sjolte, J., Sturm, C., Adolphi, F., Vinther, B. M., Werner, M., Lohmann, G., and Muscheler, R.: Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Climate of the Past, 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018, 2018.</p> <p>Steiger, N. J., Steig, E. J., Dee, S. G., Roe, G. H., and Hakim, G. J.: Climate reconstruction using data assimilation of water isotope ratios from ice cores, Journal of Geophysical Research: Atmospheres, 122, 1545–1568, https://doi.org/10.1002/2016JD026011, 2017.</p> <p>Stenni, B., Curran, M. A. J., Abram, N. J., Orsi, A., Goursaud, S., Masson-Delmotte, V., Neukom, R., Goosse, H., Divine, D., van Ommen, T., Steig, E. J., Dixon, D. A., Thomas, E. R., Bertler, N. A. N., Isaksson, E., Ekaykin, A., Werner, M., and Frezzotti, M.: Antarctic climate variability on regional and continental scales over the last 2000 years, Climate of the Past, 13, 1609–1634, https://doi.org/10.5194/cp-13-1609-2017, 2017.</p>
Text-fig. 6.—Endocranial mold of the Jordan theropod (LACM 28471). A, Dorsal view. B, Lateral view. Anterior is to the right. Lined areas represent broken bone surface and the mold is partially reconstructed in dashed lines. Abbreviations: c.h.—cerebral hemispheres, hb.—hindbrain, o.l.—optic lobe, o.n.—olfactory passage. in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 6.—Endocranial mold of the Jordan theropod (LACM 28471). A, Dorsal view. B, Lateral view. Anterior is to the right. Lined areas represent broken bone surface and the mold is partially reconstructed in dashed lines. Abbreviations: c.h.—cerebral hemispheres, hb.—hindbrain, o.l.—optic lobe, o.n.—olfactory passage.
Text-fig. 3.—Frontals and parietals of the Jordan theropod (LACM 28471). A. Dorsal view. B. Lateral view. Anterior is to the left. Lined areas represent broken surfaces and elements are partially reconstructed with dashed lines. in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 3.—Frontals and parietals of the Jordan theropod (LACM 28471). A. Dorsal view. B. Lateral view. Anterior is to the left. Lined areas represent broken surfaces and elements are partially reconstructed with dashed lines.
Text-fig. 1. Stutzeliastrobus bohemicus (BAYER) J.KVAČEK, No. NM-F 2746, Harcov, lectotype. a – surface view of ovuliferous cone photograph, scale bar 10 mm, b – microCT isosurface of ovuliferous cone, scale bar 10 mm, c – microCT longitudinal section of ovuliferous cone with segmented seeds, scale bar 10 mm, d – microCT longitudinal section of ovuliferous cone in yellow, seeds in red, e – 3D visualised bract-scale complex bearing three seeds, adaxial view, scale bar 2.5mm, f – 3D visualised bract-scale complex bearing two seeds, lateral view (incomplete reconstruction of the scale visualises the front seed), scale bar 2.5mm. in Stutzeliastrobus Bohemicus Comb. Nov. - Basal Cupressaceae Conifer From The Cenomanian Of The Bohemian Cretaceous Basin, Central Europe
Text-fig. 1. Stutzeliastrobus bohemicus (BAYER) J.KVAČEK, No. NM-F 2746, Harcov, lectotype. a – surface view of ovuliferous cone photograph, scale bar 10 mm, b – microCT isosurface of ovuliferous cone, scale bar 10 mm, c – microCT longitudinal section of ovuliferous cone with segmented seeds, scale bar 10 mm, d – microCT longitudinal section of ovuliferous cone in yellow, seeds in red, e – 3D visualised bract-scale complex bearing three seeds, adaxial view, scale bar 2.5mm, f – 3D visualised bract-scale complex bearing two seeds, lateral view (incomplete reconstruction of the scale visualises the front seed), scale bar 2.5mm.
Text-fig. 3. Stutzeliastrobus bohemicus (BAYER) J.KVAČEK, Harcov. a – surface view of bract-scale complex (arrow) and probably aborted seed, No. NM-F 2746 (lectotype), scale bar 1 mm, b – seed with a wing (arrow) NM-F 872, scale bar 1 mm, c – microCT perpendicular section of bract scale complex showing two seeds with wings (arrows), No. NM-F 2746 (lectotype), scale bar 1 mm, d – microCT longitudinal section of bract scale complex showing three seeds, No. NM-F 2746 (lectotype), scale bar 1 mm, e – microCT longitudinal section of bract scale complex with one seed reconstructed showing micropyle (arrow), No. NM-F 2746 (lectotype), scale bar 1 mm, f – isolated seed with a fragment of wing (arrow), No. NM-F 2746 (lectotype), scale bar 1 mm. in Stutzeliastrobus Bohemicus Comb. Nov. - Basal Cupressaceae Conifer From The Cenomanian Of The Bohemian Cretaceous Basin, Central Europe
Text-fig. 3. Stutzeliastrobus bohemicus (BAYER) J.KVAČEK, Harcov. a – surface view of bract-scale complex (arrow) and probably aborted seed, No. NM-F 2746 (lectotype), scale bar 1 mm, b – seed with a wing (arrow) NM-F 872, scale bar 1 mm, c – microCT perpendicular section of bract scale complex showing two seeds with wings (arrows), No. NM-F 2746 (lectotype), scale bar 1 mm, d – microCT longitudinal section of bract scale complex showing three seeds, No. NM-F 2746 (lectotype), scale bar 1 mm, e – microCT longitudinal section of bract scale complex with one seed reconstructed showing micropyle (arrow), No. NM-F 2746 (lectotype), scale bar 1 mm, f – isolated seed with a fragment of wing (arrow), No. NM-F 2746 (lectotype), scale bar 1 mm.
The reconstructed surface water area time series (2000-2019) dataset for lakes >1 km2 in China
<p>This repository contains the <strong>revised version</strong> of the supplementary data for the paper: <strong>Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China </strong>(https://www.sciencedirect.com/science/article/pii/S0034425723003267?via%3Dihub). </p> <p>Specifically, this dataset documents the reconstructed surface water area time series for all studied lakes in China during the period of 2000-2019. In the prior version of the dataset, there was an erroneous assignment of IDs to each lake. This issue has been rectified in the revised version, ensuring that the updated IDs now accurately correspond to the actual GLAKES_ID for each of the GLAKES lake polygons.</p> <p>For more detailed information of the dataset, please refer to the README file.</p>
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>
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