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Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Gobabeb Site in Namibia

The HYPERNETS project (www.hypernets.eu; Ruddick et al. 2024) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical satellite products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu; Kuusk et al. 2024) dedicated to land and water surface reflectance validation with instrument pointing capabilities. This instrument has been deployed over various sites covering a range of water and land types and a range of climatic and logistic conditions. Here, we provide the first fully quality-checked data for the Gobabeb HYPERNETS site in Namibia (GHNA). The HYPERNETS data products were processed using the HYPERNETS_processor (De Vis et al. 2024b). The provided NetCDF files are the L2B hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in these products is the Hemispherical-conical Reflectance Factor (HCRF) defined as: HCRF = π L / E where L is the conical upwelling radiance (with field of 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. The GHNA site has minimal daily variation in surface cover and weather conditions and is an ideal location for sustained, homogeneous measurements. The site is well characterised as it is very close to an instrument already recognised as a radiometric calibration site (GONA) as part of the RadCalNet network (Bialek et al. 2016). The HYPERNETS site itself (23.60153 degrees S, 15.12589 degrees E) is 650 m from the RadCalNet site, and is located on a gravel plain near a dry riverbed which separates it from the neighbouring dune sea. The HYPSTAR®-XR sensor was installed May 2022 at the top of a 9m mast on an extended 1 m horizontal boom to minimise interruption of the field of view. Data are collected every 30 minutes between 9am and 6pm local time (UTC+02) between viewing zenith angles of 0 and 60 degrees. No measurements are taken at 2pm and 2:30pm local time to avoid the hottest part of the day. 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 a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (De Vis et al. 2024b) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). For an example using these data for satellite vicarious calibration, see De Vis et al. 2024a). To obtain this dataset, we start from the full GHNA data record and omit data that does not pass the relevant quality checks (QC). Some QC are performed during the near-real time processing done by the hypernets_processor (see https://hypernets-processor.readthedocs.io/en/latest/content/atbd/processing/quality_checks.html) to produce the L2A files. Then, a number of site-specific QC are performed as post-processing to produce the L2B files. These site-specific QC cover things such as removing flags in the L2A data, avoiding periods with bad deployment conditions, removing unsuitable viewing and solar angles, as well as poorly performing wavelength ranges and individual sequences. Any potential misalignment of the sensor is also corrected, affecting L1D irradiances, and L2B reflectances. These corrected data are then used in a more stringent clear sky check, and in a check that verifies the reflectances are within realistic ranges for a given angle and time of year for the given site.  There was a rain event in Gobabeb in March 2025, resulting in the growth of grass at the site. We expect the site will be back to its normal surface cover in the near future. Since the rain event, less data passed the site-specific QC. A dedicated QC will be developed for this period, as the data with grass surface cover will still be useful for satellite validation. These updated data will be made available in the future.

opencc-by-4.0Dec 2022View details →

Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.

MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm. Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury. The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396). Due to the incomplete coverage and data filtering, some grid cells are empty. After removing these empty cells, the size of the dataset is [55399 × 396]. This specific product is stored as a gzip compressed json, where each element is a grid cell. We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ . Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015. MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm. Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury. The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396). Due to the incomplete coverage and data filtering, some grid cells are empty. After removing these empty cells, the size of the dataset is [55399 × 396]. 0. Pre-filtering We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects. However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects. Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘. We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects. With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet. 1. Spectral resmpling Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning. We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a  4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels. This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range. The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface. The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths. 2. Spatial resmpling The whole dataset of &sim; 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80. The cell longitudinal size varies between &sim; 40 km at the equator to a minimum of &sim; 10 km at ±80∘. Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.

opencc-by-4.0Feb 2023View details →

Look-Up Table of A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase

Tabulated values (look-up table) of the magnitude (dB) and phase (degres) of the unit-cell versus voltage, experimentally characterized, for a Reconfigurable Intelligent Surface prototype based on varactors described in : R. Fara, P. Ratajczak, D. -T. Phan-Huy, A. Ourir, M. Di Renzo and J. de Rosny, "A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase," in IEEE Wireless Communications, vol. 29, no. 1, pp. 70-77, February 2022, doi: 10.1109/MWC.007.00345. also accessible here: https://arxiv.org/ftp/arxiv/papers/2105/2105.11862.pdf

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany

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 ATB HYPERNETS site in Marquardt, Germany [52°27'59.40"N, 12°57'35.16"E] (ATGE). It is a subset of the complete data record, consisting of the measurements which could be used for satellite validation.  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 dgrees), 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. The HYPSTAR®-XR sensor was installed on 11 Oct 2022 at the top of a 5m 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 52.466778°N, 12.959778°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angle. 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 a FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with a 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) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full ATGE 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.

opencc-by-4.0Dec 2019View details →

River Surface Reflectance Database (RiverSR)

RiverSR database (River Surface Reflectance) v1.1.0 This database contains Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. The surface reflectance values across bands (red, green, blue, nir, swir1, swir1) represent the median reflectance of pixels detected as water within each Landsat scene that are within the boundaries of each reach represented by NHDPlusV2 centerlines. Surface reflectance is therefore geo-referenced to river center lines with network topology (NHDPlusV2) for quick geospatial analysis. Files: 1) Metadata (riverSR_v1.1_metadata.docx): Description of all data files associated with this repository.  2) Surface reflectance database (riverSR_usa_v1.1.feather). Feather files are text files readable in R and python with the feather package and this table is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv).

opencc-by-4.0May 2024View details →

Burned Area Maps based on MODIS Surface Reflectance

Burned area (BA) was classified using in-house algorithms, described in detail by Woźniak and Aleksandrowicz (2019). This method utilizes images acquired before and after fire events. All MODIS surface reflectance products MOD09A1 (tiles 24_03 and 25_03) for the period 2002 – 2021 were investigated. Since the study area is obscured by clouds or covered with snow for most of the year, only images from the time window that maximized the number of available frames across most years were selected. Hence, only images acquired between the 145th and 241st day of each year (corresponding to the spring-summer period) were retained for further processing.

opencc-by-4.0May 2023View details →

HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)

Hyperspectral Water Leaving Reflectance spectra (2988) measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the HYPSTAR® (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in Goyens et al.- Remote Sens. 2022 - 14(21)- 5607; https://doi.org/10.3390/rs14215607. <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e., <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated from the HYPSTAR® reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: Suspended particulate matter estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the IFEVA site in Argentina

The HYPERNETS project (www.hypernets.eu) has the overall aim 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 at IFEVA in Buenos Aires  Argentina (IFAR). It is a subset of the complete data record which consists of the best quality IFAR measurements which could be used for satellite validation.  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 field of 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. The IFAR site is a temporary test site located in the Agronomy Faculty campus in Buenos Aires city, Argentina (34.592322°S, 58.479017°W). The venue is managed by the IFEVA (Agricultural Physiology and Ecology Research Institute) and characterized by natural pastures with different treatments distributed in 16 patches of 7mx7m. The HYPSTAR®-XR sensor has been deployed in June 2021 at the top of a 2.4 m high tripod that is pointing to one of the patches where the vegetation has no specific treatment (natural) and is cut regularly every year in February. Data is collected every 30 min between 14:00 and 18:00 hs UTC (11:00 to 15:00 local time). 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 a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a 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 of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full IFAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to remove outliers and only 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 position) for 4 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 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 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 4 different wavelengths are then combined (keeping only measurements for which none of the 4 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.

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Wytham Woods site in the United Kingdom

The HYPERNETS project (www.hypernets.eu) has the overall aim 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 Wytham Woods HYPERNETS site in the United Kingdom (WWUK). It is a subset of the complete data record which consists of the best quality WWUK measurements which could be used for satellite validation.  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 field of 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. The WWUK site is a deciduous broadleaf forest comprised primarily of Oak, Hazel, Ash, Sycamore and Beech. It is located approximately 5 km North-West of Oxford, UK and has an extensive history of scientific research. The site follows the typical seasonal dynamics of a temperate forest with distinctive periods of leaf-off, green up and senescence across the growing season. The HYPERNETS site itself (51.777206 degrees N, 1.338494 W), is located at a height of 28 m upon a flux tower in the centre of the forest. The HYPSTAR®-XR sensor was installed in October 2021. Data are collected e very 30 minutes between 9am and 6pm local time between viewing zenith angles of 0 and 30 degrees. 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 a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a 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 of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full WWUK data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, two additional screening procedures are developed to remove outliers and only supply the best quality data suitable for satellite validation. For Wytham wood, sequences are only supplied that match a typical vegetation spectrum. As such, data is only provided between April and October during the leaf-on period. Reflectances are then tested against three parameters to check that they are vegetation spectrum. Firstly, that there is a peak in the green portion of the visible wavebands (560 nm). Secondly, that a red edge is detected. Finally, the Normalized Difference Vegetation Index (NDVI) is calculated. Spectra with an NDVI of less than 0.42 are removed from the final data set. After the vegetation quality flag are applied, a sigma-clipping method is used to remove outliers. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 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 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 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 4 different wavelengths are then combined (keeping only measurements for which none of the 4 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.

opencc-by-4.0Sep 2024View details →

zenodo44/100

REFLECTION CHARACTERISTICS OF RECONFIGURABLE INTELLIGENT SURFACES

This dataset consists of 2 CSV files containing measurement results of the RIS reflectivity characteristics and 1 JSON file. The file 24_04_ch_ka_3D_5_5Ghz_1_5m.csv consists of 5 columns of data. The first column contains the elevation angle, the second column contains the azimuth angle. The third column provides the number of the selected pattern – descriptions of the used patterns can be found in the file RIS_patterns_description.json. The fourth column contains the frequency at which the measurement was conducted, in Hertz.The fifth column contains the received power level measured at the receiving antenna in dBm.  The structure of the file 2D_2RIS_1_5m_16_06.csv is very similar but does not include the first column with the elevation angle.

zenodo44/100

GF1 WFV Surface Reflectance product

GF1 WFV SR products are generated by the SR retrieval procedure (AirSR) and the GF1 WFV images received by the remote sensing satellite ground station of China. AirSR adopts a per-pixel atmospheric correction method based on the 6S radiative transfer model and MODIS AOD (Aerosol Optical Depth) spatial fusion. The MODIS AOD products used include MOD08D3, MOD09CMA, and MCD19A2, which are spatially fused using the Universal Kriging Method (UK) to obtain a complete coverage of AOD data. The required atmospheric water vapor content and ozone data are from MCD19A2 and MOD09CMG products, which can be downloaded from the MODIS product website (https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/). AirSR also considers the influences of solar zenith angle and satellite observation zenith angle. contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn

opencc-by-4.0Oct 2024View details →

opencc-by-4.0Dec 2023View details →

Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000

Usage Notes: 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. Key updates in version 3.2 include: <ul> <li>Improved Calibration: 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>Quality Flags: 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>Extension to include observations from the year of 2023.</li> </ul> Other LCSPP repositories can be accessed via the following links: <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> 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.  In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links: <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> 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 LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. 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. 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 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: 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.

Text-fig. 10. Langtonia bisulcata REID et CHANDLER. a, b, e–g: Holotype, V. 22984, from micro-CT data. a: Dorsiventral view surface rendering. b: Dorsiventral view translucent volume rendering showing outline of locule cast. c: Equatorial transverse fracture showing paired dorsal infolds and locules with shape of a ε in cross section, reflected light, V. 22993. d: Digital transverse section from micro-CT data, of fruit with two well developed ε-shaped locules, V. 22985. e–g: Successive digital transverse sections with one well developed ε-shaped locule and infolds of the abortive locule visible in (g) (arrows). h–j: Physical transverse thin sections of specimen from middle Eocene Clarno Formation, Oregon, USA with well-preserved mesocarp including longitudinal canals in (j) (arrows), USNM 424875; Scale bars 0.5 cm in (a, b), 2.5 mm in (c–g), 5 mm in (h), 2 mm in (i), 1 mm in (j); (a, b) share same scale bar; (c, d) share same scale bar; (e, f, g) share same scale bar. in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 10. Langtonia bisulcata REID et CHANDLER. a, b, e–g: Holotype, V. 22984, from micro-CT data. a: Dorsiventral view surface rendering. b: Dorsiventral view translucent volume rendering showing outline of locule cast. c: Equatorial transverse fracture showing paired dorsal infolds and locules with shape of a ε in cross section, reflected light, V. 22993. d: Digital transverse section from micro-CT data, of fruit with two well developed ε-shaped locules, V. 22985. e–g: Successive digital transverse sections with one well developed ε-shaped locule and infolds of the abortive locule visible in (g) (arrows). h–j: Physical transverse thin sections of specimen from middle Eocene Clarno Formation, Oregon, USA with well-preserved mesocarp including longitudinal canals in (j) (arrows), USNM 424875; Scale bars 0.5 cm in (a, b), 2.5 mm in (c–g), 5 mm in (h), 2 mm in (i), 1 mm in (j); (a, b) share same scale bar; (c, d) share same scale bar; (e, f, g) share same scale bar.

Text-fig. 9. Portnallia. a–j: P. bognorensis M.CHANDLER. a–g: Holotype, V. 30421. a: Oblique lateral view with dorsal surface of locule cast facing towards right side. b: Basal view (original illustration from pl. 28, fig. 40 of Chandler 1961). c–g: Micro CT data. c–f: Surface renderings. c: Lateral view with interlocular septum facing forward. d: lateral view with dorsal surface of locule facing forward. e: Basal view. f: Apical view. g: Digital transverse section near equatorial position showing (c) to u-shaped locules. h: Apical view of tetralocular fruit, V. 30423 (original illustration from pl. 28, fig. 42 of Chandler 1961). i: Transverse section of specimen in (h), reflected light. j–o: P. sheppeyensis M.CHANDLER, Holotype V. 30428, here synomomized with P. bognorensis, from micro-CT data. j–m: Surface renderings. j: Lateral view with interlocular septum facing forward. k: Lateral view with dorsal surface of locule facing forward. l: Basal view. m: Apical view. n: Digital equatorial transverse section showing the three preserved locules and extensive cracking due to pyrite decomposition. o: Translucent volume rendering, apical view showing (c) to u-shaped locules. Scale bars 2 mm, bar in (a) applies also to (b), bar in (e) applies to also to (c, d), bar in (g) applies also to (f), bar in (j) applies to applies also to (k–m). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 9. Portnallia. a–j: P. bognorensis M.CHANDLER. a–g: Holotype, V. 30421. a: Oblique lateral view with dorsal surface of locule cast facing towards right side. b: Basal view (original illustration from pl. 28, fig. 40 of Chandler 1961). c–g: Micro CT data. c–f: Surface renderings. c: Lateral view with interlocular septum facing forward. d: lateral view with dorsal surface of locule facing forward. e: Basal view. f: Apical view. g: Digital transverse section near equatorial position showing (c) to u-shaped locules. h: Apical view of tetralocular fruit, V. 30423 (original illustration from pl. 28, fig. 42 of Chandler 1961). i: Transverse section of specimen in (h), reflected light. j–o: P. sheppeyensis M.CHANDLER, Holotype V. 30428, here synomomized with P. bognorensis, from micro-CT data. j–m: Surface renderings. j: Lateral view with interlocular septum facing forward. k: Lateral view with dorsal surface of locule facing forward. l: Basal view. m: Apical view. n: Digital equatorial transverse section showing the three preserved locules and extensive cracking due to pyrite decomposition. o: Translucent volume rendering, apical view showing (c) to u-shaped locules. Scale bars 2 mm, bar in (a) applies also to (b), bar in (e) applies to also to (c, d), bar in (g) applies also to (f), bar in (j) applies to applies also to (k–m).

Text-fig. 8. Lanfrancia subglobosa E.REID et M.CHANDLER. a–c, e–g: Holotype V. 23014. a: reflected light. b, c: Surface renderings from micro-CT data. a, b: Lateral views with dorsal surface of locule facing forward and locule casts protruding in upper part. c: Apical view. d: Fruit showing two locule casts the dorsal surfaces of which face to the left and the right, V. 30417(1). e–g: Successive digital transverse sections showing four u to v to c-shaped locules from micro-CT data. h: Physical transverse section of specimen in (d). i–k: Physical transverse section, V. 30419 from Herne Bay, blue lines in K indicating limits of fibre layer lining the locule. l: Detail from (h), showing sclerenchyma composing the septa and central axis. m: Transverse section, enlargement from (i), showing anatomy of tissues adjacent to the dorsal infold. Blue lines indicate limits of the fibre layer lining the locule. n: Part of (m) recut, tangential section transecting the dorsal infold (central), both limbs of the locule cast, and peripheral parts of the pericarp on either side. o: Detail from (n), showing anatomy of the infold. Scale bars 5 mm in (a–h) (a–g share the same bar), 3 mm in (i), 1 mm in (j–m), 0.5 mm in (n), 0.2 mm in (o). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 8. Lanfrancia subglobosa E.REID et M.CHANDLER. a–c, e–g: Holotype V. 23014. a: reflected light. b, c: Surface renderings from micro-CT data. a, b: Lateral views with dorsal surface of locule facing forward and locule casts protruding in upper part. c: Apical view. d: Fruit showing two locule casts the dorsal surfaces of which face to the left and the right, V. 30417(1). e–g: Successive digital transverse sections showing four u to v to c-shaped locules from micro-CT data. h: Physical transverse section of specimen in (d). i–k: Physical transverse section, V. 30419 from Herne Bay, blue lines in K indicating limits of fibre layer lining the locule. l: Detail from (h), showing sclerenchyma composing the septa and central axis. m: Transverse section, enlargement from (i), showing anatomy of tissues adjacent to the dorsal infold. Blue lines indicate limits of the fibre layer lining the locule. n: Part of (m) recut, tangential section transecting the dorsal infold (central), both limbs of the locule cast, and peripheral parts of the pericarp on either side. o: Detail from (n), showing anatomy of the infold. Scale bars 5 mm in (a–h) (a–g share the same bar), 3 mm in (i), 1 mm in (j–m), 0.5 mm in (n), 0.2 mm in (o).

Text-fig. 1. Diplopanax cacaoides (ZENKER) comb. nov. a–d: [Holotype of Mastixia cantia E.REID et M.CHANDLER, V.22953]. a: Lateral view of longitudinally broken specimen, reflected light. b–d: Surface renderings from micro-CT data. b: Lateral view of longitudinal fracture surface. c: Same specimen rotated to show external surface. d: Enlargement of lower half from (a, b), reflected light. e, f: Specimen figured originally as a paratype of M. cantia, V.22954 (Reid and Chandler 1933: pl. 25, fig. 3), reflected light. e: Ventral view with much of the endocarp wall fallen away exposing smooth convex ventral surface of locule cast. f: Transversely fractured surface, showing thick wall of the endocarp, and dehiscence plane leading to the left limb of the locule. g: Transversely sectioned, laterally compressed specimen from Miocene of Wiesa, Germany for comparison, Senckenberg Museum, SM.B. 21034/I. h–j: Digital transverse sections from micro-CT data of the Holotype V.22953. h: Transverse fracture surface from (b), showing curved locule and zone of weakness defining the germination valve (arrow), reflected light. i: Same orientation with clear demarcation of the separation plane of the germination valve (arrow), digital section from micro-CT scan. j: Enlargement from (h). Scale bars 5 mm. in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 1. Diplopanax cacaoides (ZENKER) comb. nov. a–d: [Holotype of Mastixia cantia E.REID et M.CHANDLER, V.22953]. a: Lateral view of longitudinally broken specimen, reflected light. b–d: Surface renderings from micro-CT data. b: Lateral view of longitudinal fracture surface. c: Same specimen rotated to show external surface. d: Enlargement of lower half from (a, b), reflected light. e, f: Specimen figured originally as a paratype of M. cantia, V.22954 (Reid and Chandler 1933: pl. 25, fig. 3), reflected light. e: Ventral view with much of the endocarp wall fallen away exposing smooth convex ventral surface of locule cast. f: Transversely fractured surface, showing thick wall of the endocarp, and dehiscence plane leading to the left limb of the locule. g: Transversely sectioned, laterally compressed specimen from Miocene of Wiesa, Germany for comparison, Senckenberg Museum, SM.B. 21034/I. h–j: Digital transverse sections from micro-CT data of the Holotype V.22953. h: Transverse fracture surface from (b), showing curved locule and zone of weakness defining the germination valve (arrow), reflected light. i: Same orientation with clear demarcation of the separation plane of the germination valve (arrow), digital section from micro-CT scan. j: Enlargement from (h). Scale bars 5 mm.

Text-fig. 4. Mastixia cf. oregonensis (R.A.SCOTT) TIFFNEY et HAGGARD from the London Clay, originally included within the concept of M. cantiensis. a–c: V. 22960(1). a: Transverse fracture, showing c-shaped locule, dorsal infold, and sculptured endocarp, reflected light. b: Transverse digital section from micro-CT scan data. c: Surface view of ribbed endocarp extracted from micro-CT data. d: Transverse fracture, reflected light, V. 22955 (originally illustrated in pl. 25, fig. 4 of Reid and Chandler 1933). e, f: Transverse physical section, V. 22963(2) showing U-shaped locule and longitudinal dorsal infold. g–i reflected light. g: Detail from left of (d). h, i: Detail from right of (f). Scale bars 5 mm in (a–f), 1 mm in (g–i). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 4. Mastixia cf. oregonensis (R.A.SCOTT) TIFFNEY et HAGGARD from the London Clay, originally included within the concept of M. cantiensis. a–c: V. 22960(1). a: Transverse fracture, showing c-shaped locule, dorsal infold, and sculptured endocarp, reflected light. b: Transverse digital section from micro-CT scan data. c: Surface view of ribbed endocarp extracted from micro-CT data. d: Transverse fracture, reflected light, V. 22955 (originally illustrated in pl. 25, fig. 4 of Reid and Chandler 1933). e, f: Transverse physical section, V. 22963(2) showing U-shaped locule and longitudinal dorsal infold. g–i reflected light. g: Detail from left of (d). h, i: Detail from right of (f). Scale bars 5 mm in (a–f), 1 mm in (g–i).

Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s).

Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.

Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT

Text-fig. 7. Cornacaeae (a–j), Icacinaceae (k–o). a–e: Mastixia. USNM PAL 772364. Scale bar = 1 cm. a: Lateral view of eroded endocarp – the opposite side being missing and the endocarp broken near its mid point, reflected light, palladium coated. b–e: Micro-CT scan surface renderings. b: Rotated 90° from the view in (a). c: Rotated 90° from the view in (b). d: Rotated 90° from (c), exhibiting the damaged "back" face of the endocarp. e: Axillary view of the endocarp; the opposite end missing as apparent in (d). f–j: Cf. Nyssa. DMNH EPI.47808. Scale bar = 1 cm. Micro-CT scan surface renderings. f: Intact face of the endocarp; note ridges and "apical" point. g: Eroded (?gnawed; note horizontal grooving) opposite face of the endocarp. h: Lateral view of the endocarp, eroded/gnawed face to left. i: Apical view, eroded/gnawn portion below. j: Basal view of endocarp. k–o: Iodes DMNH-EPI.47807. Micro-CT scan surface renderings. Scale bar = 1 cm. k: Face view of endocarp. l: Opposite face of endocarp. m: Lateral view demonstrating the compressed nature of the endocarp, note thickened suture marking the probable track of the primary bundle. n: Apical view, primary bundle trace to right. o: Basal view, primary bundle trace to right. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.

Text-fig. 7. Cornacaeae (a–j), Icacinaceae (k–o). a–e: Mastixia. USNM PAL 772364. Scale bar = 1 cm. a: Lateral view of eroded endocarp – the opposite side being missing and the endocarp broken near its mid point, reflected light, palladium coated. b–e: Micro-CT scan surface renderings. b: Rotated 90° from the view in (a). c: Rotated 90° from the view in (b). d: Rotated 90° from (c), exhibiting the damaged "back" face of the endocarp. e: Axillary view of the endocarp; the opposite end missing as apparent in (d). f–j: Cf. Nyssa. DMNH EPI.47808. Scale bar = 1 cm. Micro-CT scan surface renderings. f: Intact face of the endocarp; note ridges and "apical" point. g: Eroded (?gnawed; note horizontal grooving) opposite face of the endocarp. h: Lateral view of the endocarp, eroded/gnawed face to left. i: Apical view, eroded/gnawn portion below. j: Basal view of endocarp. k–o: Iodes DMNH-EPI.47807. Micro-CT scan surface renderings. Scale bar = 1 cm. k: Face view of endocarp. l: Opposite face of endocarp. m: Lateral view demonstrating the compressed nature of the endocarp, note thickened suture marking the probable track of the primary bundle. n: Apical view, primary bundle trace to right. o: Basal view, primary bundle trace to right.