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74 results for “deep soils”
Deep-soil carbon changes at 62 European beech stands in the Vienna Woods, Austria, 1984-2022
This dataset comprises repeated soil, vegetation, and site measurements from long-term forest monitoring in the Vienna Woods (Wienerwald), Austria, part of the UNESCO Biosphere Reserve “Wienerwald” (48.1°–48.3° N, 15.8°–16.3° E). The study focuses on pure, naturally regenerated European beech (Fagus sylvatica) stands, initially sampled in 1984 and resampled in 2012 and 2022 . Elevations range from ~180 to 800 m a.s.l., with mean annual temperatures of 8–9 °C and precipitation of 600–900 mm. Soil samples were collected from three mineral soil depths (0–5 cm, 30–40 cm, and 80–90 cm) following consistent protocols across sampling years. Variables include total, organic, and inorganic carbon, total nitrogen and sulfur, exchangeable base cations (Ca, Mg, K), pH, total Fe and Mn, fine soil mass, bulk density, rock content, soil texture, and root biomass. Stocks were calculated. Leaf nutrient concentrations (C, N, S, P, Ca, Mg, K) were determined in all sampling years. Dendrochronological measurements were conducted to determine growth trends since stand establishment, and stand-level characteristics (tree density, DBH, aboveground biomass, crown vitality, slope, aspect) were recorded. Site-level climate data (mean annual temperature, annual precipitation) from 1961 to present and atmospheric deposition data for N and S (1990, 2012, 2022) were integrated from national and European gridded datasets. The dataset supports long-term assessments of soil carbon and nutrient dynamics, forest productivity, and environmental change impacts in old-growth beech forests. Data collection is complete for the 1984, 2012, and 2022 campaigns; no ongoing sampling is planned.
Physical Characteristics and Stratigraphy of Deep Soil Sediments from Shark River Slough, Everglades National Park (FCE) from 2005 and 2006
These data represent the results of piston-coring deep (around 1m) soil cores from Shark Slough sites, including FCE LTER site SRS3 and FCE related site NE-SRS1 from November 18, 2005 to February 26, 2006. Soils from 1-cm depth increments were analyzed for bulk density and stratigraphy. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
A Deep Neural Network Based SMAP Soil Moisture Product
<p>The soil moisture datasets here are based on a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The bias is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables include polarized SMAP brightness temperatures, incidence angles, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</p>
Rainfall intensification enhances deep percolation and soil water content at the Kellogg Biological Station, Hickory Corners, MI (2015 to 2016)
Dataset AbstractData supporting the paper Hess, L., E. L. Hinckley, G. P. Robertson, S. K. Hamilton, and P. Matson. 2018. DOI: 10.2136/vzj2018.07.0128original data source http://lter.kbs.msu.edu/datasets/198
A Deep Neural Network Based SMAP Soil Moisture Product
<p>It is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas -- when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</p>
Dataset used in "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis"
<p>Please see the paper "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis" how the images were captured, manual counting was performed, training datasets were prepared and models were trained.</p>
Model configuration files and forcing data for Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
<p>This repository includes the model configuration files, input data, and forcing data used for simulations in Bieri et al. (2025) - <em>Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration.</em></p> <ul> <li>forcing.tar.gz - Compressed folder containing HRLDAS Noah-MP model forcing NetCDF files <ul> <li>These forcing files were derived from the NASA Global Land Data Assimilation System (GLDAS; Beaudoing et al. 2020)</li> <li>The compressed file contains 3-hourly forcing files for the entire simulation period (01 Jun 2000 to 31 Dec 2019)</li> </ul> </li> <li>wrfinput_d01 - NetCDF file used as HRLDAS input file in HRLDAS Noah-MP simulations <ul> <li>Generated from WRF WPS (https://github.com/wrf-model/WPS)</li> </ul> </li> <li>Namelist files <ul> <li>namelist.hrldas.ROOT - Model namelist settings used for ROOT experiment</li> <li>namelist.hrldas.SOIL - Model namelist settings used for SOIL experiment</li> <li>namelist.hrldas.GW - Model namelist settings used for GW experiment</li> <li>namelist.hrldas.CONTROL - Model namelist settings used for FD (CONTROL) experiment</li> </ul> </li> </ul>
Figure 3 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 3. Neighbour Joining DNA barcode tree of 75 endogean beetles from Guatemala. Terminal names consist of the most detailed current taxonomic identification (genus, tribe, or subfamily), followed by specimen number, family name, sample number, length of the DNA barcode fragment [with the number of ambiguously read bases in square brackets], BIN number, and GenBank accession number.
Figure 2 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 2. Sampling methods of the deep soil Guatemala beetles. (A–C) pits producing samples GT12, GT16, and GT25, respectively (note that sample GT16 is from an extremely dry habitat, while sample GT25 is twice as large in volume); (D) a floating soil sample in a barrel with water; (E) scooping floating organic foam containing live beetles on a fine mesh; (F) wet samples prior to specimen extraction; (G) two aluminium thermoeclectors of the novel larger and lighter design; (H) thermoeclectors exposed to the Sun.
Figure 7 in Deep soil floatation in Chile reveals diverse and mainly nameless fauna of endogean beetles (Coleoptera)
Figure 7. Maximum Likelihood DNA barcode tree of 102 endogean rove beetles (Staphylinidae) of Chile. Subfamilies are colour coded. Terminal names consist of the specimen number, the most detailed current taxonomic identification (species, genus, tribe, or subfamily), sample number, length of the DNA barcode fragment [with the number of ambiguously read bases in angle brackets], BIN number [if applicable, also denoted on the tree with black dots], and GenBank accession number. Digits at internodes are rapid bootstrap values of 50 % and above.
Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model
<p>This repository contains:</p> <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> <p>Summary:</p> <p>The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.</p> <p>The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. </p> <p>Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.</p> <p>The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.</p> <p>For more details, please contact me (wotp12@unist.ac.kr)</p>
FIG. 7. — Paulianacarus vietnamese n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 7. — Paulianacarus vietnamese n. sp. adult: A, leg I, antiaxial; B, palp, antiaxial; C, leg II, antiaxial; D, microsculpture. Abbreviations: see Material and methods. Scale bars: A, C, 350 μm; B, 100 μm; D, 2 μm.
FIG. 6. — Paulianacarus vietnamese n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 6. — Paulianacarus vietnamese n. sp. adult female: A, lateral view; B, leg III antiaxial; C, bothridium, lateral view; D, genu, tibia III, dorsal view; E, leg IV, antiaxial. Abbreviations: see Material and methods. Scale bars: A, 350 μm; B, E, 270 μm; C, 23 μm; D, 45 μm.
FIG. 5. — Paulianacarus vietnamese n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 5. — Paulianacarus vietnamese n. sp. adult: A, dorsal view; B, ventral view. Abbreviations: see Material and methods. Scale bar: 170 μm.
FIG. 4. — Bedoslohmannia anneae n. gen., n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 4. — Bedoslohmannia anneae n. gen., n. sp. adult, leg folding: A, leg I; B, leg II; C, leg III; D, leg IV. Scale bar: 70 μm.
FIG. 2. — Bedoslohmannia anneae n. gen., n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 2. — Bedoslohmannia anneae n. gen., n. sp. adult: A, lateral view; B, ventral view (genital and ano-adanal zone, semi–schematic); C, genital opening, preanal plate and adanal plate without setae; D, genital opening, preanal plate and adanal plate with setae; E, subcapitulum; F, adoral setae. Abbreviations: see Material and methods. Scale bars: A-D, 100 μm; E, 60 μm; F, 20 μm.
FIG. 3. — Bedoslohmannia anneae n. gen., n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 3. — Bedoslohmannia anneae n. gen., n. sp. adult: A, leg I, antiaxial; B, leg II, antiaxial; C, leg III, antiaxial; D, leg IV, antiaxial. Abbreviations: see Material and methods. Scale bar: 70 μm.
FIG. 1. — Bedoslohmannia anneae n. gen., n in Oribatid mites from deep soils of Hòn Chông limestone hills, Vietnam: the family Lohmanniidae (Acari: Oribatida), with the descriptions of Bedoslohmannia anneae n. gen., n. sp., and Paulianacarus vietnamese n. sp.
FIG. 1. — Bedoslohmannia anneae n. gen., n. sp. adult: A, dorsal view, without setae; B, dorsal view with setae; C, setal neotrichy disposition; D, prodorsum; E, chelicera; F, fixed digit. Abbreviations: see Material and methods. Scale bars: A, B, 100 μm; C, E, 50 μm; D, F, 150 μm.
Use of Deep Learning for structural analysis of CT-images of soil samples
Open the record for dataset details and reuse information.
Near-total Element Concentrations for deep soils (0-100 cm) collected from Murphy Dome study site in 2013
This file contains near-total element digestion for deeper soils (0-100cm) for black spruce and Alaska paper birch forest in the Murphy Dome fire scar near Fairbanks, AK. All information was collected in summer 2013 and subsequently processed and analyzed in the laboratory.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.