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8 results for “Bare soil”
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the 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. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 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.</p> <p>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.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with 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). </p> <p>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. </p>
Evaluating Nitrate Removal and Travel Times in a Bare Deciduous Forest Soil Using a Tracer-Based Soil Column Experiment
<p> </p> <p>This dataset summarizes the experimental data from a study investigating the relationship between hydrodynamic properties, transport age characteristics (traced using the conservative tracer bromide), and nitrate consumption by native microbial communities in a deciduous forest soil near Lausanne, Switzerland.</p> <h4><strong>Experimental Setup</strong></h4> <ul> <li>Native soil was <strong>collected, sieved, and packed</strong> into homogeneous soil columns (lysimeters) with a porosity of <strong>0.4</strong>.</li> <li>To stimulate microbial activity, the soil underwent <strong>carbon amendment</strong> for several weeks before the experiment.</li> <li>On <strong>June 10, 2021</strong>, a <strong>pulse of bromide and nitrate</strong> (referred to as <strong>SPIKE</strong>) was applied to the bare soil that had undergone carbon amendment.</li> <li>The dataset covers experimental conditions recorded from <strong>May 25, 2021, to August 12, 2021</strong>.</li> </ul> <h4><strong>Included Data & Models</strong></h4> <ol> <li><strong><strong>SPIKE experiment (<strong>pulse of bromide and nitrate</strong>)</strong></strong> <ul> <li> <ul> <li><strong><strong>all other files (see table_of_contents.html and overview.pdf)</strong></strong></li> </ul> </li> </ul> </li> <li> <p><strong>HYDRUS-1D Simulations</strong></p> <ul> <li>The dataset includes HYDRUS-1D simulation files to reproduce <strong>nitrate and bromide breakthrough curves</strong>: <ul> <li><strong>0_Nitrate-double-compartment.zip</strong></li> <li><strong>0_Bromide-double-compartment.zip</strong></li> </ul> </li> </ul> </li> <li> <p><strong>Soil Characterization using Multi-Step Outflow (MSOM) Method</strong></p> </li> </ol> <ul> <li> <ul> <li>Before the main experiment, the <strong>multi-step outflow method</strong> was performed to characterize the soil. The original experimental data was collected at a <strong>15-minute resolution</strong> but has been <strong>upscaled to hourly resolution</strong> to reduce noise and facilitate data handling.<br> <ul> <li><strong>multi-step_outflow_method.csv</strong></li> </ul> </li> </ul> </li> </ul> <ol> <li> <ul> <li>The processed data (at <strong>hourly resolution</strong>) and <strong>HYDRUS-1D inverse modeling simulations</strong> for soil parameter estimation are provided under two configurations: <ul> <li><strong>config_1_MSOM.zip</strong></li> <li><strong>config_2_MSOM.zip</strong></li> </ul> </li> </ul> </li> </ol> <p> </p>
Global bare soil, photosynthetic and non-photosynthetic vegetation fraction annual at 500 m resolution
<p>Annual mean and std for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023. The dataset was obtained from: <a href="https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html">https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html</a> (monthly values; 320GB in total). Mean and std was derived using terra package in R using functions "mean" and "std" from 12 monthly values; missing values were ignored during derivation.</p> <p>Note: the Global Vegetation Fractional Cover Product (GVFCP) v3.1 (<a href="https://doi.org/10.1016/j.agee.2021.107719">Hill and Guerschman, 2022</a>) is derived from spectral unmixing of all seven optical bands from the 500 m MODIS (Moderate Resolution Imaging Spectroradiometer) Nadir BRDF (Bidirectional Reflectance Distribution Function)-adjusted Reflectance Product (NBAR, MCD43A4 Collection 6). A similar dataset has been produced by <a href="https://doi.org/10.5194/essd-16-1333-2024">Sun et al., (2024)</a>, covering period 2001–2022. Below is the sample code explaining how were the mean, max and std derived.</p> <pre><code>## Download from: https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html ## wget -e robots=off -nH --cut-dirs 4 -nc -r -l5 -A '*.nc' -R 'catalog*' -I /thredds/fileServer/,/thredds/catalog/ 'https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html' ## 6857 tiles library(terra) modis.tiles = list.files("/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", pattern = glob2rx("*.nc")) mod.lst = unique(sapply(modis.tiles, function(i){strsplit(i, "\\.")[[1]][4]})) str(mod.lst) ## 272 ## aggregate per year per tile nc_tile <- function(i, year, dir.x="/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", mc.cores=parallel::detectCores()){ require(terra) if(year == 2023 | year == 2024){ in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".061.nc") } else { in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.nc") } bs.filenames = paste0("./modis-fc/bs_", c("mean", "max", "std"), "/FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.tif") dg = terra::rast(in.filename) if(any(!file.exists(bs.filenames))){ bs = dg["bare_soil"] ## 12 months dg.m = app(bs, fun=mean, na.rm=TRUE, filename=bs.filenames[1], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.x = app(bs, fun=max, na.rm=TRUE, filename=bs.filenames[2], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.s = app(bs, fun=sd, na.rm=TRUE, filename=bs.filenames[3], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) } } ## run in parallel for(year in 2001:2024){ x = parallel::mclapply(sample(mod.lst), function(i){try( nc_tile(i, year=year, mc.cores = 2) )}, mc.cores = 40) tmpFiles(remove=TRUE) }</code></pre>
Figure 3 in Soil Oligochaeta communities after 9 decades of continuous fertilization in a bare fallow experiment
Figure 3. Feeding activity (mean of 16 bait-lamina sticks, N plots per treatment), in % of the total number of baits consumed.
Microplastics and nanoplastics barely enhance contaminant mobility in agricultural soils
<p>Dataset for OA publication "Microplastics and nanoplastics barely enhance contaminant mobility in agricultural soils" in Communications Earth & Environment</p>
Using generalized regression neural network to retrieve bare surface soil moisture from Radarsat-2 backscatter observations, regard less of roughness effect
<p>Description of soil moisture, correlation length, and RMS height from ground measurements of 147 sampling sites, full-polarized backscattering coefficients extracted from Radarsat-2 scene corresponding to those ground measurement sites.</p>
Figure 2 in Soil Oligochaeta communities after 9 decades of continuous fertilization in a bare fallow experiment
Figure 2. Boxplot representing the total abundance (expressed as individuals m-2) of enchytraeids according to fertilizers (from the left to the right): basic (alkaline) amendments, organic amendments (manure), ammonium-based nitrogen fertilizers, nitrate-based nitrogen fertilizers, phosphate fertilizers, potassium fertilizers, and control (without amendments). Fertilizer treatments with the same letter are not different at p = 0.05. N beneath treatment codes is the number of plots sampled per treatment group.
Figure 1 in Soil Oligochaeta communities after 9 decades of continuous fertilization in a bare fallow experiment
Figure 1. Graphical presentation of the treatments and layout of the Versailles 42-plot long-term bare fallow experiment. Plots with a red cross were not sampled in the present study.
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