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opencc-by-4.0Oct 2018View details →

Spectral transmittance of solar radiation by screens and nets used in horticulture and agriculture

We present a dataset of measurement of the spectral transmittance of 197 horticultural nets and screens from five companies. These materials span a range of uses from shading and reducing the heat load on plants to blocking pests such as birds and insects. Routinely, these materials are used in greenhouses and polytunnels to reduce the sunlight received by plants, however their spectral transmittance is not routinely measured. The spectral irradiance that plants receive can affect plant growth and photomorphogenesis, hence this information is of value when selecting the most appropriate material for a given purpose. The spectral transmittance of the materials was measured outdoors close to solar noon using an array spectrometer calibrated for the range 290-900 nm and compared directly with the ambient solar spectral irradiance. The measured spectrum encompasses those regions perceived by plants through known photoreceptors and used by plants in photosynthesis: ultraviolet (UV); photosynthetically active radiation (PAR), and near infra red (far red – FR). The solar spectral photon irradiance (μmol m-2 s-1) transmitted by screens and nets from several manufacturers was measured with an array spectroradiometer. Our measurements and analyses are focused on the differences in spectral irradiance, created when employing these screens and nets, in order to address the lack of detailed studies of these light environments, rather than the physiochemical properties of materials or their cost-effectiveness. The measurements of spectral irradiance under climate screens, and shade and insect nets, were made on clear days in sunny conditions close to solar noon (between 10 a.m. to 2 p.m local time) at NC State University campus (35.78°N, -78.67°W) in late July and early August 2017, and in Viikki Field Plots at the University of Helsinki (60.22°N, 25.01°E, 55 m asl) in July and August 2018. The methods for measurements at North Carolina State University follow the protocol described below and published in <a href="https://doi.org/10.1371/journal.pone.0199628">Kotilainen et al., (2018)</a>, where a comprehensive assessment of the results of this subset of screens/nets and their meaning is also given. The measurements were performed in an open field with no surrounding structures or buildings within 20 m. Repeated measurements of each different sample were made in a randomised order, thus ensuring comparability among measurements. Measurements were made on a tripod 0.7 m above the ground and the sample was secured to a wooden plate 3 cm above the diffusor. A test, comparing four larger (1 x 1 m) samples against those of the standard dimensions that we used, found that the area of screen/net measured did not affect the results at this distance between the screen/net and diffusor. Thus, there was no evidence that unfiltered diffuse or scattered radiation interfered with measurements despite the relatively small dimensions of the sample. Measurements under each screen/net sample in 2017 (Svensson 13 x 19 cm, Mallas Textiles 8 x 10 cm) were made twice to account for any possible effect of sample placement over the cosine diffuser and change in the sun angle during a set of measurements.  Given that no significant differences were evidence, the 2018 screen/net samples (Criado y Lopez 8 x 12 cm, Howitec 15 x 25 cm, Huachang yarns 25 x 30 cm, and Jiangsu Huachang Yarns and Fabrics 8 x 12 cm) were only measurement once. A recording of spectral irradiance without the screen/net of filtered sunlight was made directly before and after each filter measurement (called “Open”). The spectrometer used had been calibrated for measurements of UV and visible solar radiation (Maya2000 Pro Ocean Optics, Dunedin, FL, USA; D7-H-SMA cosine diffuser, Bentham Instruments Ltd, Reading, UK - see <a href="https://doi.org/10.1002/ece3.4496">Hartikainen et al., 2018</a> for details of the measurement protocol). Briefly, each measurement of irradiance transmitted beneath a screen or net was followed by sequence of measurements in the dark and with a polycarbonate filter attenuating all UV radiation. These controls accounted for the dark noise and stray light in the UV waveband. Both a correction for the shape of the slit function and for stray light were included in the post-processing of the spectra (<a href="http://uv4plants.org/methods/how-to-check-an-array-spectrometer/">Aphalo et al., 2016</a>). Bracketing was performed by taking a measurement of the UV region and splicing this together this the entire spectrum. All measurements were processed using the Photobiology packages in R. Measurements of solar spectral irradiance in the wavelength range from 290 nm to 900 nm were processed in R, using the photobiology packages developed for spectral analysis (<a href="https://doi.org/10.19232/uv4pb.2015.1.14">Aphalo, 2015</a>). We present spectral photon irradiance (μmol m-2 s-1) and spectral energy irradiance (W m-2). Plants absorbs photons producing a chemical change (Grotthus Law) thus photon irradiance is more easily applicable understanding to biological processes in plants. The spectral transmittance of the screens/nets are the most useful data presented. Essentially the patterns of spectral attenuation will be consistent, irrespective of whether spectra are expressed as photon or energy irradiance. Utilizing predefined functions available in the photobiology packages, we calculated the integrals and photon ratios of these integrals as follows: UVB:PAR 280–315 nm/400-700 nm, UVA:PAR 315–400 nm/400-700 nm, blue:green (B:G) 420–490 nm/500-570 nm, blue:red (B:R) 420–490 nm/620-680 nm. Red and far-red for the calculation of R:FR ratio are 655–665 nm and 725–735 nm, respectively. UVB radiation and UVA radiation are defined according to ISO, blue, green and red according to <a href="https://doi.org/10.1104/pp.110.160820">Sellaro et al. (2010)</a>, and R:FR according to <a href="https://doi.org/10.1146/annurev.pp.33.060182.002405">Smith(1982)</a>. The same definitions of the UV-waveband are maintained for both spectral integrals and their ratios throughout, i.e. according to ISO, (<a href="http://doi:%2010.21273/HORTTECH03648-16">Both et al., 2017</a>). This is because the UVB and UVA wavebands of solar radiation follow distinct daily patterns of variation; UVB irradiance is highest during the four hours around solar noon, whereas the UVA region of solar radiation remains a similar proportion of total irradiance throughout the day. These differences also imply that UVA and UVB radiation follow different diurnal and seasonal patterns of variation (<a href="https://doi.org/10.1111/j.1751-1097.2007.00216.x">Seckmeyer et al., 2007</a>). Data Files Available DataBaseScreensNets.zip Graphs (.jpg files) of actual measured (1) spectral energy irradiance, (2) spectral photon irradiance, and (3) proportion transmittance of solar radiation, for each screen and net.  (1) Energy Irradiance figures (suffix _EI.) and (2) Photon Irradiance figures (suffix _PI.) are plot of the measured values of irradiance under the filter (screen/net) and corresponding measurements without the screen or net (“open” measurement) for comparison (290-898 nm wavelength range).  The proportion transmittance under each screen or net is calculated from comparison of the open and measured spectrum (suffix _Trans). The low-wavelength tail end of the spectrum is trimmed (<310 nm) in each plots since % transmittance are inflated by low signal to noise ratio in the UV-B region where irradiance values are very low. The database screens and net are identified by the name of the company “_” name of the screen/net for all 197 materials. These figures can be reproduced from the file “ScreensNets_irrad_trans.txt” using the R code “Plotting_DataBaseScreensNets.r” ImagesScreensNets.zip Image files (.jpg files) from photos and scans of each of the measured screens and nets. One image from each of the 197 filter materials (screens/nets) measured is stored in folders arranged according to the company for each filter type. The companies are: Criado y Lopez; HowiTech; Huanchang yarns; Jiangsu Huachang Yarns & Fabrics; Mallas_Textiles and Svensson. ScreensNets_irrad_trans.txt This is the main database file containing the measurements of spectral irradiance beneath each filter material (screen/net) from 290 nm – 898 nm and corresponding open reading, and calculated spectral transmittance. Data are in columns as follows: (A) Company – the Company name; (B) FilterName – the filter name as given by the company; (C) Serial - a serial number, effectively equivalent to the order in which the materials were measured; (D) wavelength – at intervals recorded by the array spectrometer running for each spectrum from 290.02 nm to 897.73 nm; (D) FilterEI - energy irradiance of transmitted solar radiation measured 3 cm beneath the filter material (screen/net) at each wavelength of the spectrum; (E) FilterPI – photon irradiance equivalent to the energy irradiance; (F) OpenEI – energy irradiance of solar radiation at the same location without the filter material (screen/net) (G) OpenPI – photon irradiance equivalent to the energy irradiance; (H) FilterFactor – the proportion of radiation transmitted by the filter material (screen/net) at each wavelength measured, a value between 0.0 and 1.0 (values out of range at low wavelengths in the UV-B region are replaced with 0.0 or 0.1). Processed spectra are given: processing of raw spectra was done with Photobiology packages in R. Full spectra were recorded with an integration time set manually to give maximum counts of just less than 60 000 at the wavelength corresponding to peak spectral irradiance. Bracketing was performed by recording a second spectrum (long spectrum) with ten-times longer integration time than this, to achieve greater accuracy of measurement in the UV region (< 400 nm). These two spectra were spliced together. Each filter measurement was accompanied by a dark measurement (to estimate dark noise) and a measurement under a polycarbonate filter (PC) to correct for stray light. In 2018, these two readings were performed immediately after the filter material (screen/net) was measured; both within 10 s total of the filter material measurement for both the full spectrum, and long spectrum. ScreensNets_irrad_trans.xlsx This Excel file contains the same information in columns as the file ScreensNets_irrad_trans.txt but with a second worksheet showing the trimming calculations for out-of-range readings at low UV-B wavelength and with an addition final column, the irradiance spectrum open29_irrad (described below). Open29_irrad.txt In order to obtain standardised BSWF files to comparison with each other, the calculated proportion spectral transmittance results for each filter material (screen/net) were applied to a “standard” solar-noon open-spectrum from Helsinki recorded on a date close to midsummer (Open29_irrad.txt). This spectrum was measured as described above. This spectrum was measured at Viikki Fields, Helsinki on Wed June 27th 2018 at 13:15:33 EEST (Integration Time, 110000 μsec; bracketting x10) in a completely open area. To apply the transmittance data to their own locations, database users should substitute the spectrum from their own location for Open29_irrad.txt to obtain spectral irradiance data for the effects of the filter materials (screens/net) at their site using the R code Calculating_Spectral_Integrals.r ScreensNets_spectral_integrals.txt The file gives a matrix of spectral integrals and ratios calculated with the Photobiology packages in R for each of the spectra presented in ScreensNets_irrad_trans.txt.  Column headings are the filter material ID, made up from the “Company name” “_” “filter name”. The first column contains row names identifying spectral integrals and ratios calculated – first as energy irradiance then as photon irradiance and finally as photon ratios. Calculations are made using the BSWF (Spectral_Integrals_Function.r) as follows: PAR_e; UVB_e; UVA_e; UVb350_e; UVa350_e; Blue_e; Green_e; Red_e; Far_red_e; GEN_G_e; GEN_T_e; PG_e; DNA_N_e; CIE_e; FLAV_e; Infra_red_e; PAR_q; UVB_q; UVA_q; UVb350_q; UVa350_q; Blue_q; Green_q; Red_q; Far_red_q; GEN_G_q; GEN_T_q; PG_q; DNA_N_q; CIE_q; FLAV_q; Infra_red_q; UVB_UVA; UVB_PAR; UVA_PAR; R_FR_Sellaro; R_FR_Smith10; R_FR_Smith20; B_G; B_R; PhyEqi. ScreensNets_spectral_integrals.xlsx This files contains the same data as ScreensNets_spectral_integrals.txt and shows on individual worksheets, processing of original, smoothed (in Photobiology package to improve the signal to noise in the UV-B tail of the spectr), and corrected (with values of transmittance greater than 1.0 or less than 0.0 replaced in the UV-B tail) data; and comparisons of the Original vs. Corrected, and Original vs. Smoothed data. The same BSWF calculations for the example open spectrum open29_irrad (used for standardisation) are given on their own worksheet, as is the corresponding “FilterFactor” (proportion spectral transmittance) for each spectral integral and spectral photon ratio. The final worksheet “Type” lists the filters and their expected function (i.e. shade, pest net, hale net, ground cover etc.). This “FilterFactor” information could be of practical use in situations where the spectral irradiance is unavailable for a given location, and comparisons among filters need to be made from only partial data (e.g. PAR PPDF).  These FilterFactors can be applied to the PAR PPDF for instance to calculate the daily light integral through the day for horticultural proposes.  Please note that differences in the shape of the solar spectrum at different locations will cause (small) deviations in the transmitted PAR PPFD calculated from the spectral integral compared with the more precise calculation from the spectral irradiance. Although for the purposes of comparison between filters these are likely to be of minor importance.  Plotting_DataBaseScreensNets.r This file gives the R code for plotting the graphs in DataBaseScreensNets.zip from the source file ScreensNets_irrad_trans.txt. Make sure that the required packages are loaded. The code was run in R version 3.4.3. Calculating_Spectral_Integrals.r The file gives the R code to calculate spectral integrals and to include an open measurement for standardisation (Open29_irrad) from the source file ScreensNets_irrad_trans.txt (as described above). The spectra in ScreensNets_irrad_trans.txt are converted to source.spct for use in the Photobiology packages. Spectral_Integrals_Function.r The file is a function requiring the Photobiology packages in R to run. It is needed to calculate the spectral integrals described above and can be amended to obtain whichever spectral integrals and photon ratios from the Photobiology packages are desired.

opencc-by-4.0Dec 2021View details →

Open Soil Spectral Library (training data and calibration models)

Open Soil Spectral Library contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set: <ul> <li>ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information;</li> </ul> Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe. To use the models and data please install <a href="https://hub.docker.com/r/opengeohub/r-geo">R and required packages</a>. Read more about the <a href="https://github.com/traversc/qs">QS data format</a> and how to convert it to CSV or similar. Modeling steps are explained in detail in: <a href="https://github.com/soilspectroscopy/ossl-models">https://github.com/soilspectroscopy/ossl-models</a>. To visualize database please use: <a href="https://explorer.soilspectroscopy.org/">https://explorer.soilspectroscopy.org/</a> Complete OSSL documentation can be found at: <a href="https://soilspectroscopy.github.io/ossl-manual/">https://soilspectroscopy.github.io/ossl-manual/</a> <a href="https://soilspectroscopy.org/">Soil Spectroscopy for the Global Good</a> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (<a href="https://nifa.usda.gov/press-release/nifa-invests-over-7-million-big-data-artificial-intelligence-and-other">Award #2020-67021-32467</a>). Input datasets are property of the <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/research">USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory</a>, <a href="https://www.worldagroforestry.org/">ICRAF-World Agroforestry</a>, <a href="https://www.isric.org/">ISRIC-World Soil Information</a>, the <a href="http://africasoils.net/services/data/soil-databases/">Africa Soil Information Service</a> funded by the Bill and Melinda Gates Foundation, the <a href="https://esdac.jrc.ec.europa.eu/">European Soil Data Centre</a>, the <a href="https://www.neonscience.org/">National Ecological Observatory Network</a>, and <a href="https://sae.ethz.ch/">ETH Zurich</a>.  For more advanced uses of the soil spectral libraries we advise to contact the original data producers especially to get help with using, extending and improving the original SSL data.

opencc-by-4.0Feb 2023View details →

Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"

This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior

opencc-by-4.0Jul 2023View details →

The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access

Abstract: NEW VERSION V.002 (Some Lat Long Coordinates added). Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use. BSSL Description and Usefulness The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data. The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative. The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability. Coverage The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range. Accessing original data source Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors: a) Refer to sheet two for name and code-based searches; b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state; c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found. Proceeding to data analysis We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011). The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition. a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh; b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm; c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings; d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample; e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm; f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°. The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface. We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°. Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard. For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory. The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm-1 to 600 cm-1, with a resolution of 2 cm-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.   Dataset characterization The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:   Sheet 1.       BSSL_Soil_Attributes_Dataset Column 1.    ID_unique: Sequential code assigned to every record; Column 2.    Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; Column 3.    Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges; Column 4.    MIR_availability: availability of spectral data in the middle infrared range; Column 5.    Sampling: type of soil sampling; Column 6.    Depth_cm: soil surface layer depth in centimeters;   Column 7.    Lat: Latitude;   Column 8.    Lat: Longitude;   Column 9.    Region: Brazilian geographical region of samples' source; Column 10.    Municipality: Brazilian municipality of samples' source; Column 11.   State: Brazilian Federation Unit of samples' source; Column 12. Vegetation: type of vegetal covering; Column 13. Biome: groupings of ecosystems that share similar characteristics and span different regions; Column 14. Geology: type of rock matter from local soil sampling; Column 15. Sand_gkg: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram; Column 16. Clay_gkg: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram; Column 17. SOM_gkg: Soil organic matter content, expressed in grams per kilogram; Column 18. pH_H2O: Soil hydrogen ion potential measured in water; Column 19. Ca_mmolkg: Exchangeable calcium content in the soil, expressed in millimoles per kilogram; Column 20. Mg_mmolkg: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram; Column 21. Na_mmolkg: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and Column 22. CEC_Ph7_mmolkg: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.   Sheet 2.     BSSL_Vis_NIR_SWIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 2153. 350 – 2500: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).   Sheet 3.    BSSL_MIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 683. 4000 – 600: Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm-1).   Sheet 4.       Contributors Column 1. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets; Column 2. Owner: Name of the collaborator who agreed to the availability of the data; Column 3. E-mail: Contact the e-mail of the owner for more information or a data request; Column 4. Institution: Contributor's affiliation; Column 5. Samples NIR: Number of Vis-NIR-SWIR samples sent to the BSSL collection; Column 6. Samples MIR: Number of MIR samples sent to the BSSL collection;   Sheet 5.       Metadata Column 1. Material and Methods: Description of procedures performed for soil data analyses   Expectation and Social Relevance These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.

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Spectral and biological characteristics of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019)

An intensive field campaign was conducted during the 2018-2019 austral summer to assess spectral and biological characteristics of multiple microbial mat types, as well as mosses, across nine ephemeral glacial meltwater streams in the Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys region of Antarctica. In addition to biological sample collection, hyperspectral visible/near-infrared (VNIR) measurements were conducted using an ASD FieldSpec4 spectrometer, corrected and averaged across each mat, moss, and soil type, and downsampled to the multispectral resolution of the WorldView-2 satellite. This package contains a detailed sample archive, associated images, biological characteristics that include ash free dry mass (AFDM), chlorophyll-a, and pigment concentrations, as well as hyperspectral and multispectral reflectance spectra.

openCC (other)Oct 2023View details →

edi48/100

Spectral algal concentrations measured with a bbe Moldaenke FluoroProbe in discrete water column samples from lakes in the McMurdo Dry Valleys, Antarctica (2004–2023, ongoing)

An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring the spatial and temporal patterns and processes that regulate primary production in perennial ice-covered lakes. Phytoplankton are the dominant primary producers in the photic zones of these lakes and play a central role in carbon cycling. This data package quantifies spectral algal concentrations measured with a bbe Moldaenke FluoroProbe at discrete depths throughout the water column of multiple lakes across the McMurdo Dry Valleys of Antarctica, providing long-term observations that support investigations into primary production dynamics and ecosystem responses to environmental change.

openCC (other)Dec 2025View details →

edi48/100

Field-Collected Spectral Reflectance of Dominant Vegetation at the Sevilleta National Wildlife Refuge

This dataset includes field-collected spectral reflectance of dominant vegetation species in grassland and shrubland at the Sevilleta National Wildlife Refuge collected monthly May – September 2019. A spectroradiometer was used to collect the percent spectral reflectance of electromagnetic radiation (range 400-2500nm) of a sample of dominant vegetation species ("spectra"), yielding a spectral curve for each species. At least ten individuals per species were sampled. These data form a spectral library which was used to calibrate a multiple-endmember spectral mixture analysis (MESMA) of satellite imagery of the Sevilleta NWR, as part of an ongoing collaboration between the LTER and the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Ultimately, we aim to produce fractional images of green vegetation, non-photosynthetic vegetation, bare soil, and shade to form a synoptic thirty-year record of vegetation dynamics at the Refuge. The spectral library can be referenced by future researchers using remote sensing methods to examine vegetation dynamics at the Sevilleta NWR.

openCC (other)Apr 2021View details →

opengpl-2.0Mar 2020View details →

Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations

This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a> DAMASKPhenoPowerLaw75x75x75TestCase.zip An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation. DAMASKPhenoPowerLaw256x256x256ProductionRun.zip All input to run the DAMASK simulation that we discussed in the paper. DAMASKPDTSettings256x256x256ProductionRun.zip All damaskpdt settings files to execute the individual post-processing studies of the paper. DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS. DAMASKPDTSlurmLogs256x256x256ProductionRun.zip All logs from the SLURM job management system from the individual post-processing runs. DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip The source code to the tool we developed during the revision process of our paper to verify the methods via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the DAMASK microstructure instantiation. DAMASKPDTSourceCode_Production.zip The source code we used to post-process all results from the DAMASK simulations. GitHub repository: https://github.com/mkuehbach/damaskpdt

opencc-by-4.0Sep 2020View details →

Spectral library of laser-induced fluorescence (LiF) properties from Smithsonian rare-earth element (REE) orthophosphate standards

The spectral library presents a data set of laser-induced fluorescence (LiF) spectra from rare-earth element (REE) orthophosphates provided and distributed as reference material for microbeam analysis by the Smithsonian National Museum of Natural History (sample IDs: 16484 - NMNH 168499; Jarosewich and Boatner, 1991; Donovan et al., 2002 and 2003). The data set delivers high-resolution LiF spectra excited at three standard laser wavelengths (325 nm, 442 nm, 532 nm) recorded in the UV-visible to near-infrared spectral range (340 - 1080nm). Presented LiF spectra represent data from efficient signal excitation conditions and contain the diagnostic emission lines of individual REE including detailed information on splitting into sub-levels. The LiF spectral library data provides a reference for various applications in spectroscopy-based material composition analysis with the scope of REE identification. LiF as a tool can complement the merging technique of reflectance spectroscopy, because LiF is a particularly well suited method for REE detection and can be used to cross-validate results (e.g. Lorenz et al. 2019) The LiF library allows for transparent and reproducible result analysis in scientific studies and promotes further developments of efficient automated algorithms for REE identification and characterisation. This addresses especially the need for innovative, non-invasive techniques of raw material exploration (securing REE supply) and material stream characterisation (e.g. in e-waste recycling) or for manifold applications in other fields of geosciences (e.g. geology) and physics. references: Donovan, J., Hanchar, J., Picolli, P., Schrier, M., Boatner, L., Jarosewich, E., 2002. Contamination in the rare-earth element orthophosphate reference sam- ples. J. Res. National Institute of Standards and Technology 106, 693–701. doi:10.6028/jres.107.056.  Donovan, J., Hanchar, J., Piccoli, P., Schrier, M., Boatner, L., Jarosewich, E., 2003. A reexamination of the rare-earth element orthophosphate reference samples for electron microprobe analysis. Canadian Mineralogist 41, 221– 232. doi:10.2113/gscanmin.41.1.221.  Jarosewich, E., Boatner, L., 1991. Rare-earth element reference samples for electron microprobe analysis. Geostandards Newsletter 15, 397–399. doi:10. 1111/j.1751-908X.1991.tb00115.x.  Lorenz, S., Beyer, J., Fuchs, M., Seidel, P., Turner, D., Heitmann, J., Gloaguen, R., 2019. The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Ex- ploration. Remote Sensing 11, 21. doi:10.3390/rs11010021.

opencc-by-4.0Jan 2021View details →

Spectral Induced Polarization (SIP) field data sets for comparison between time and frequency domain (Martin et al. 2020)

Data and scripts for the scientific journal article: Martin, T., Günther, T., Flores Orozco, A. & Dahlin, T. (2020):  Evaluation of spectral induced polarization field measurements in time and frequency domain,  J. Appl. Geophys., 180, 104141, doi:10.1016/j.jappgeo.2020.104141. The paper deals with the comparison of instruments for measuring induced polarization (IP) data, both in frequency domain (FD) as well in time-domain. Four instruments have been used to measure two profiles (IP1: a=1m, IP5: a=5m) over a known blackshale near the town Schleiz in Thuringia, Germany. Preface: Our vision is not only to publish scientific results, but also openly demonstrate how the results have been produced. This includes the raw data, processed data and inversion results, but also all scripts that have been used for it in a reproducible way. The scripts use the pyBERT (Python Boundless Electrical Resistivity Tomography) based on the pyGIMLi (Python Geophysical Inversion and Modelling Library) computational framework. Here, we used the pyBERT version 2.3.1 based on pygimli 1.1.1, but it should be reachable by similar versions as well. All is based on the classes FDIPdata and TDIPdata. For installing, we refer to the webpages www.pygimli.org and gitlab.com/resistivity-net/bert Folders:     figures  figures used for production process, generated by the scripts in the folder     scripts  Python scripts for generating the pdf files     IP1      data for the shallow profile using an electrode spacing of a=1m     IP5      data for the deeper profile using an electrode spacing of a=5m Each of the two data folders contains data and inversion results in separated folder named according to the instrument names:  * SIP256C (Radic Research) - FDIP instrument with remote units (intelligent electrodes) * DAS-1 (Multi-Phase Technology) - FDIP and TDIP instrument using multi-core cables * Terrameter LS2 (GuidelineGeo ABEM) - TDIP instrument using multi-core cables * Syscal Pro Switch72 (Iris instruments) - TDIP instrument using multi-core cables Please see readme files in the individual folders for specific information. Notice: In additon to the published results, we also added more data from the field  measurements which completes our results, e.g.  - TDIP data with 8s aquisition time - all four instruments for the long profile IP5 Complete full-waveform data for the Terrameter LS2 are available on request.

opencc-by-4.0Dec 2016View details →

RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties

We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.

opencc-by-4.0Nov 2023View details →

Sensitivity and cluster by spectral clustering algorithm

File contains the node pressure sensitivity matrix to 4LPS leaks and the cluster matrix for each node determined using the spectral clustering algorithm.

opencc-by-4.0Nov 2023View details →

Spectral albedo and summer ground temperature of herbaceous and shrub tundra vegetation at Bylot Island, Canadian High-Arctic

These data are in support of a preprint: Comparing spectral albedo and NDVI of herbaceous and shrub tundra vegetation at Bylot Island, Canadian High-ArcticFlorent Domine, Maria-Belke-Brea, Ghislain Picard, Laurent Arnaud, and Esther LévesqueTo be submitted in 2023. The spectral albedo of several vegetation assemblages on Bylot Island and in Mala River valley on nearby Baffin Island were recorded between 10 and 18 July 2015. The spectral range covered was 346 to 2400 nm. Surfaces were classified according to the main vegetation types. Classes used are graminoids, moss, Salix arctica, soil, and Salix richardsonii. S. richardsonii is the only truly erect species on Bylot Island. Transmission spectra of radiation through the S. richardsonii canopy were also recorded. S. richardsonii spectra were different depending on the location where they were measured and we present spectra for sites in active parts of an alluvial fan (Salix-G2), an inactive part of an alluvial fan (Salix-D1) and in a mesic area on Mala River Valley (Salix-M). We also present typical relative solar irradiance spectra recorded at Bylot Island during the campaign, under clear and overcast conditions. In conjunction with spectral albedo data, these irradiance spectra allow the calculation of the broadband (BB) albedo of the vegetation types and to compare BB albedo values under identical irradiance conditions.  83 spectra were recorded: 39 for S. richardsonii and 44 for low vegetation and soil. 17 transmission spectra under S. richardsonii were recorded. We present here only averages for each vegetation type. We also present averages for all low vegetation types and for all S. richardsonii spectra, to allow the calculation of the radiative impact of erect shrubs at Bylot Island. We also present soil temperature data at 15 cm depth for the spots GRASS (mostly Salix Arctica), TUNDRA (Mostly moss), SALIX-D1 (Salix richardsonii) and SALIX-F (Salix richardsonii). SALIX-F is similar to SALIX-G2. The data are during summer 2020. The locations of the various spots investigated are: Spot name  Latitude  Longitude Vegetation types foundTUNDRA 73.150° -80.004° Humid and moist polygons with low vegetation dominated by mosses, graminoids, S. arctica and S. herbacea.PLAINE 73.167° -79.915° Low vegetation and bare soil patches caused by cryoturbation (mudboils) with mosses, graminoids and S. arctica.GRASS 73.158° -79.907° Low vegetation between patches of S. richardsonii dominated by S. arctica, with litter, mosses, graminoids and occasional bare soil.  SALIX-D1 73.158° -79.907° Scattered patches of S. richardsonii <35 cm tall. Understory is mosses, graminoids, litter, S. arctica and bare soil.SALIX-M 73.006° -80.685° Mesic area with patches of S. richardsonii 35 to 40 cm tall. Understory includes moss, graminoids and litter. Between patches: herb tundra with graminoids and mosses. The area is not within an alluvial fan.SALIX-G2 73.168° -79.812° Extended area in an alluvial fan with S. richardsonii >40 cm. Understory includes litter, mosses, graminoids, bare soil, S. arctica and S. reticulata.SALIX-F 73.182° -79.745° Similar to SALIX-G2. Ground temperature is monitored there. No spectral data were recorded at that site.

opencc-by-4.0Dec 2023View details →

Spectral Units for Tsiolkovskiy crater (Moon, Far side)

Spectral Units derived from the Moon Mineralogy Mapper (M3) data for the lunar far side Tsiolkovskiy crater.

opencc-by-4.0Sep 2023View details →

An updated modular set of synthetic spectral energy distributions for young stellar objects

These are the models released with the following publication: An updated modular set of synthetic spectral energy distributions for young stellar objects (<a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">Richardson et al. 2024</a>). This is a set of young stellar object (YSO) models with associated spectral energy distributions (SEDs) calculated through radiative transfer. It is a significant update to the data published alongside Robitaille (2017, R17). It contains the parameters shaping each model and adds the newly calculated parameters of envelope mass, average dust temperature, disk stability, and line-of-sight extinction. It also makes explicit quantities, such as source luminosity, that were left implicit in the previous release. This set also convolves the SEDs with several new filters, primarily those on the James Webb Space Telescope, and adds a script to facilitate convolution of these models with additional filters as desired by users. All data included in Version 1.1 of the R17 set (the most recent) are included here. Like their predecessors, these models are versioned. Updates will be released as more models are completed or other changes are made. Files unzip to r+24_models-{version}/{geometry}. "files.tar.gz" contains scripts for SED convolution and main sequence comparison, the opacity to absorption of dust used in the radiative transfer calculations, main sequence T/L values used for results in the accompanying work, and reference material for the contents of the dataset and latest version. The primary use of these models is as templates for SED fitting. The R17 models were structured for use with the <a href="https://sedfitter.readthedocs.io/en/stable/" target="_blank" rel="noopener">sedfitter</a> python package, which enables fitting and analysis of the fit results. For a version of sedfitter which accommodates the new additions, use <a href="https://github.com/richardson-t/sedfitter/tree/dev" target="_blank" rel="noopener">this fork</a>.

Landsat-based Spectral Indices for pan-EU 2000-2022

<h2>Description</h2> <h3>General description</h3> Here, we present the ARCO (analysis-ready and cloud-optimized) Landsat-based Spectral Indices data cube. Available at 30m resolution from 2000 to 2022, it includes multiple spectral indices and multi-tier predictors (bimonthly, annual, and long-term) for continental Europe, including Ukraine, the UK, and Turkey (excluding Svalbar). This data cube has a broad coverage of indices, each providing unique insights into different aspects, including: surface reflectance, vegetation, water, soil and crop. All data layers are cloud-masked and then gap-filled, ready for analysis, modeling, and mapping applications. Technical details: <ul> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> Considering the data volume, only bimonthly data layers for the years 2000 and 2022 are uploaded. However, all annual and long-term layers are available. For the full data cube, please visit this <a href="https://docs.google.com/spreadsheets/d/1QTA6OkkYlZljfHst_inCrkC7DJcMAyHnM9k0iHulwpg/edit?usp=sharing">catalog</a>. Due to Zenodo's storage limits, the data layers are stored in different buckets. Use the identifier-navigation list below to access the bucket of your interest and download the corresponding layers. <h3>Identifier navigation list</h3> This data cube includes 4 tiers of data, depending on the processing extend in the temporal scale: <ul> <li>Tier-1: Bimonthly Landsat reflectance bands 2000 (<a href="../records/12805260">Jan</a> <a href="../records/12806079">Mar</a> <a href="../records/12803844">May</a> <a href="../records/12805020">Jul</a> <a href="../records/12915187">Sep</a> <a href="../records/12804826">Nov</a>) 2022 (<a href="../records/12805807">Jan</a> <a href="../records/12804063">Mar</a> <a href="../records/12911628">May</a> <a href="../records/12805508">Jul</a> <a href="../records/12805649">Sep</a> <a href="../records/12804421">Nov</a>)</li> <li>Tier-2: Bimonthly spectral indices 2000 (<a href="../records/10884234">Jan</a> <a href="../records/10884272">Mar</a> <a href="../records/10884296">May</a> <a href="../records/10884311">Jul</a> <a href="../records/10884340">Sep</a> <a href="../records/10884358">Nov</a>) 2022 (<a href="../records/10884387">Jan</a> <a href="../records/10884406">Mar</a> <a href="../records/10884419">May</a> <a href="../records/10884447">Jul</a> <a href="../records/10884482">Sep</a> <a href="../records/10884503">Nov</a>)</li> <li>Tier-3: Annual predictors <ul> <li>Reflectance bands, NDVI and NDWI P25 <a href="../records/10777969">2000</a> <a href="../records/10777971">2001</a> <a href="../records/10777973">2002</a> <a href="../records/10777975">2003</a> <a href="../records/10777977">2004</a> <a href="../records/10777979">2005</a> <a href="../records/10777981">2006</a> <a href="../records/10777983">2007</a> <a href="../records/10777985">2008</a> <a href="../records/10777987">2009</a> <a href="../records/10777989">2010</a> <a href="../records/10777991">2011</a> <a href="../records/10777993">2012</a> <a href="../records/10777995">2013</a> <a href="../records/10777997">2014</a> <a href="../records/10777999">2015</a> <a href="../records/10778001">2016</a> <a href="../records/10778003">2017</a> <a href="../records/10778005">2018</a> <a href="../records/10778007">2019</a> <a href="../records/10778009">2020</a> <a href="../records/10778011">2021</a> <a href="../records/10778013">2022</a></li> <li>Reflectance bands, NDVI and NDWI P50 <a href="../records/10851076">2000</a> <a href="../records/10864828">2001</a> <a href="../records/10864838">2002</a> <a href="../records/10864852">2003</a> <a href="../records/10864874">2004</a> <a href="../records/10864896">2005</a> <a href="../records/10864952">2006</a> <a href="../records/10865027">2007</a> <a href="../records/10865253">2008</a> <a href="../records/10865315">2009</a> <a href="../records/10865398">2010</a> <a href="../records/10865486">2011</a> <a href="../records/10865536">2012</a> <a href="../records/10865630">2013</a> <a href="../records/10865721">2014</a> <a href="../records/10865803">2015</a> <a href="../records/10865857">2016</a> <a href="../records/10865905">2017</a> <a href="../records/10865920">2018</a> <a href="../records/10865948">2019</a> <a href="../records/10865973">2020</a> <a href="../records/10865992">2021</a> <a href="../records/10851078">2022</a></li> <li>Reflectance bands, NDVI and NDWI P75 <a href="../records/10851080">2000</a> <a href="../records/10866025">2001</a> <a href="../records/10866071">2002</a> <a href="../records/10866095">2003</a> <a href="../records/10866108">2004</a> <a href="../records/10866121">2005</a> <a href="../records/10866153">2006</a> <a href="../records/10866194">2007</a> <a href="../records/10866224">2008</a> <a href="../records/10866256">2009</a> <a href="../records/10866294">2010</a> <a href="../records/10866319">2011</a> <a href="../records/10866337">2012</a> <a href="../records/10866367">2013</a> <a href="../records/10866410">2014</a> <a href="../records/10866436">2015</a> <a href="../records/10866474">2016</a> <a href="../records/10866496">2017</a> <a href="../records/10866514">2018</a> <a href="../records/10866533">2019</a> <a href="../records/10866572">2020</a> <a href="../records/10866603">2021</a> <a href="../records/10851082">2022</a></li> <li>Aggregated spectral indices <a href="../records/10777868">2000</a> <a href="../records/10777870">2001</a> <a href="../records/10777872">2002</a> <a href="../records/10777874">2003</a> <a href="../records/10777876">2004</a> <a href="../records/10777878">2005</a> <a href="../records/10777880">2006</a> <a href="../records/10777882">2007</a> <a href="../records/10777884">2008</a> <a href="../records/10777886">2009</a> <a href="../records/10777888">2010</a> <a href="../records/10777890">2011</a> <a href="../records/10777892">2012</a> <a href="../records/10777894">2013</a> <a href="../records/10777896">2014</a> <a href="../records/10777898">2015</a> <a href="../records/10777900">2016</a> <a href="../records/10777902">2017</a> <a href="../records/10777904">2018</a> <a href="../records/10777906">2019</a> <a href="../records/10777908">2020</a> <a href="../records/10777910">2021</a> <a href="../records/10777912">2022</a></li> <li>Cumulative spectral indices 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022</li> </ul> </li> <li>Tier-4: Long-term predictors 2000-2022 <a href="../records/10776891">trend</a> <a href="../records/12805999">P25</a> <a href="../records/12804351">P50</a> <a href="../records/12805181">P75</a></li> </ul> <h3>Name convention</h3> To ensure consistency and ease of use across the data layers, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: <ol> <li>generic variable name: ndti.min.slopes = the long term slope of minNDTI</li> <li>variable procedure combination: glad.landsat.ard2.seasconv.yearly.min.theilslopes - theil slopes calculated from yearly minimum values of NDTI</li> <li>Position in the probability distribution/variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20000101 = 2000-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = europe (without Svalbar)</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20231218 = 2023-12-18 (creation date)</li> </ol> <h3>Citation</h3> Please cite this dataset using the DOI: [<a href="https://doi.org/10.5281/zenodo.10776891">10.5281/zenodo.10776891</a>], which represents all versions of this dataset. This ensures your citation remains up to date with the latest version. <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">GitHub issue</a>! <h2>Long-term spectral indices trend</h2> On this landing page of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>,  four long-term spectral indices trend data are stored, as Zenodo doesn't allow empty buckets. Therefore, this page serves not only as the landing page for the entire dataset but also as the bucket for the long-term trend of spectral indices.

On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity

Overview The HDF5 file contains primary measurement data and secondary processing data that was used to assess clear sky effective emissivity and transmissivity estimates and generate the results in the associated manuscript (accepted and forthcoming). Data is indexed by solar time and provided per site for years 2010 through 2015. Sample Python code is provided to reconstruct training and validation sets by concatenating all 'tra' or 'val' samples across sites. Results can be explored by modifying choice of filters and constructing new training and validation sets. Data usage The usage of the data presented here is intended for research and development purposes only and implies explicit reference to the paper: Matsunobu, L. M., & Coimbra, C. F. M. (2024). On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity. Journal of Geophysical Research: Atmospheres, 129, e2023JD039798. https://doi.org/10.1029/2023JD039798 Data description Column names and descriptions are as follows: - dlw_m: measured downwelling longwave [W/m^2] - ghi_m: measured global horizontal irradiance [W/m^2] - dni_m: measured direct normal irradiance [W/m^2] - dhi_m: measured diffuse horizontal irradiance [W/m^2] - rh_m: measured relative humidity [%] - pa_m: measured atmospheric pressure [hPa] - t_m: measured temperature [K] - sza: solar zenith angle [deg] - ghi_c: clear sky global horizontal irradiance [W/m^2] - dni_c: clear sky direct normal irradiance [W/m^2] - dhi_c: clear sky diffuse horizontal irradiance [W/m^2] - cs1: clear sky filter 1 - cs2: clear sky filter 2 - site_elev: station elevation [m] - clr_pct: fraction of samples identified as clear for the given site and day - clr_num: number of samples identified as clear for the given site and day - pw_hpa: water vapor partial pressure [hPa] - alt_correction: altitude correction - tra: indicate if sample is included in training set - val: indicate if sample is included in validation set - sqrt_pw: square root of non-dimensional water vapor partial pressure - e_sky: effective clear sky emissivity The last two columns, 'sqrt_pw' and 'e_sky' represent the input and target for linear regression, i.e. e_sky = c_1 + (c_2 * sqrt_pw). Altitude corrected sky emissivity, or expected emissivity for a station at sea-level, is found by e_sky - alt_correction. Sample code (Python v3.8) <pre>import pandas as pd site = "GWC" # or other station code df = pd.read_hdf("data.h5", key=site) # import single site</pre> Training and validation sets can be reconstructed as below. Linear regression on 'sqrt_pw' to predict 'e_sky' - 'alt_correction' in the resultant training set will reproduce results in the associated manuscript. <pre>training = [] validation = [] surfrad_sites = ['BON', 'DRA', 'FPK', 'GWC', 'PSU', 'SXF', 'TBL'] for site in surfrad_sites: # loop through sites df = pd.read_hdf("data.h5", key=site) df["site"] = site # add site name training.append(df.loc[df.tra]) # append samples marked as training validation.append(df.loc[df.val]) # append samples marked as validation # join respective set samples across sites training = pd.concat(training, ignore_index=False) validation = pd.concat(validation, ignore_index=False)</pre> Reproduce regression results <pre>from sklearn.linear_model import LinearRegression c1 = 0.6 # set intercept (c1 constant) x = training.sqrt_pw.to_numpy().reshape(-1, 1) y = training.e_sky - training.alt_correction - c1 # adjust for altitude and c1 y = y.to_numpy().reshape(-1, 1) model = LinearRegression(fit_intercept=False) model.fit(x, y) c2 = model.coef_[0][0] print(f"c1={c1:.3f}, c2={c2:.3f}") # output: c1=0.600, c2=1.652</pre>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2019): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2019. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2020): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2020. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2015): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2015. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!