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767 results for “Soil Moisture”
Changes in soil moisture and temperature modify the toxicity of sodium selenite and sodium selenate for Folsomia candida (Collembola) Willem 1902
<p>Effects of sublethal concentrations of selenite and selenate were tested on parameters of mortality, reproduction, growth, and oxidative stress parameters of <em>Folsomia candida</em> (Collembola) in case of different climate scenarios. The standard 20°C and the increased 25°C temperatures were combined with three different soil moisture conditions: drought, standard water content and increased water content.</p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019
<p><strong># GPM_API 2019</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016
<p><strong># GPM_API 2016</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018
<p><strong># GPM_API 2018</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017
<p><strong># GPM_API 2017</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020
<p><strong># GPM_API 2020</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
Temperature moisture interactions soil respiration experiment
<p>These files are from a soil incubation experiment looking at combined effects of temperature and moisture on soil C fluxes. They are prepared for model run and model-data comparison. Description of the data, e.g units, is not in the files themselves.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/mtdata_model_input.csv">mtdata_model_input.csv</a></p> <p>Contains model input for simulating the experimental setup.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/mtdata_co2.csv">mtdata_co2.csv</a></p> <p>Contains the measured data with averages and standard deviation of three replicates samples for treatment.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/site_Closeaux.csv">site_Closeaux.csv </a></p> <p>Containts soil properties required as model input.</p> <p> </p>
Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software
<p>Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.</p>
Global Soil Moisture Agricultural Drought Index (SMADI)
<p><br> This repository contains the global Soil Moisture Agricultural Drought Index (SMADI) spanning from June 2010 to December 2015 at fortnightly (bi-weekly) rate. The product is gridded in a 0.05deg Climate Modeling Grid.<br> <br> The data are provided in Matlab format (.mat). Each file is a 3600x7200 matrix size, where 3600 is the number of pixels in Latitude coordinates and 7200 to Longitude coordinates, both in the WGS84 system. The center-pixel coordinates are variables "Latitude" and "Longitude". No data values (NaN) correspond to pixels where the retrieval was not possible.<br> <br> <br> SMADI is computed using satellite time series of MODIS Normalized Difference Vegetation Index (NDVI, computed from daily reflectances MOD09CMG v.6), MODIS Land Surface Temperature (LST, product MOD11C1 v.6), and SMOS BEC L3 soil moisture data v2.0 (which corresponds to SMOS L2 v.620, average of ascending and descending orbits). <br> <br> The IGBP land cover (MODIS MOD12C1 product) is used prior to SMADI calculation to select only grassland and cropland/natural vegetation mosaic pixels as representative of the primary agro-ecosystems.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Soil moisture for Limestone
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Soil moisture for Peneda-Geres
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Soil moisture for Montado
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Soil moisture for Sierra Nevada
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Soil moisture for Gran Paradiso
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Soil moisture for Har Ha`Negev
<p>Pixel values represent the surface soil moisture content (in m3m-3*100) of, approximately, the top 5 cm of soil. The retrieval was based on Sentinel-1 time-series and a Support Vector Machine based approach. L. Pasolli, C. Notarnicola, G. Bertoldi, L. Bruzzone, R. Remelgado, F. Greifeneder, G. Niedrist, S. Della Chiesa, U. Tappeiner, and M. Zebisch, “Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band,” Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 262–283, 2015.</p>
Soil moisture for Lake Ohrid
<p>H-SAF H109 Metop ASCAT</p> <p>The soil moisture product represents the water content in the upper soil layer (< 2 cm) in relative units between totally dry conditions (0%) and total water capacity (100%). The time series are available on a discrete global grid (DGG) with a spatial resolution of 25 km (grid spacing 12.5 km). The temporal sampling rate is irregular (every 1-2 days) and depends on the latitude. Each surface soil moisture estimate has an associated noise value, indicating the uncertainty. The soil moisture product has not been pre-filtered, meaning that a masking of invalid measurements (e.g. frozen ground, snow cover) by the user is highly recommended before further processing.</p>
Soil moisture in Teutoburg forest/Bielefeld dataset
<p>Daten von Bodenfeuchte-Sensoren aus der Region des Teutoburger Waldes in Ostwestfalen-Lippe (Deutschland), die im Rahmen eines ehrenamtlichen Citizen Science Projektes von Code for Bielefeld e.V. gesammelt werden. Visualisierung auf <a href="https://bodenfeuchte.org" target="_blank" rel="noopener">bodenfeuchte.org</a></p> <p>Version 1 enthält Daten von August 2023 bis September 2024.</p> <h1><strong>Spaltenbeschreibung</strong></h1> <h2>Geräte-ID (device)</h2> <p>Diese Spalte enthält eine eindeutige Kennung für jedes Gerät. Die Geräte-ID ist ein alphanumerischer Code, der verwendet wird, um die Daten eines bestimmten Sensors eindeutig zu bestimmen</p> <h2>Gerätemarke (device_brand)</h2> <p>Marke des Bodenfeuchtesensors (z.B. dragino)</p> <h2>Gerätemodell (device_model)</h2> <p>Modell des Bodenfeuchtesensors (z.B. lse01)</p> <h2>Breitengrad (latitude)</h2> <p>Der Breitengrad des Standorts, an dem das Gerät installiert ist.</p> <h2>Längengrad (longitude)</h2> <p>Der Längengrad des Standorts, an dem das Gerät installiert ist.</p> <h2>Bodenfeuchtigkeit (soil_moisture)</h2> <p>Diese Spalte enthält den aktuellen Wert der Bodenfeuchtigkeit, gemessen in Prozent. Sie gibt an, wie viel Wasser im Boden enthalten ist, wobei höhere Werte auf eine höhere Bodenfeuchtigkeit hinweisen.</p> <h2>Bodenleitfähigkeit (soil_conductivity)</h2> <p>Dieser Wert gibt die elektrische Leitfähigkeit des Bodens in Mikrosiemens pro Zentimeter (µS/cm) an, was ein Indikator für die Menge an gelösten Salzen oder Nährstoffen im Boden ist.</p> <h2>Bodentemperatur (soil_temperature)</h2> <p>Die aktuelle Temperatur des Bodens in Grad Celsius gemessen.</p> <h2>Batteriestatus (battery)</h2> <p>Diese Spalte gibt die aktuelle Spannung der Batterie des Geräts in Volt an. Eine ausreichende Batteriespannung ist notwendig, um sicherzustellen, dass das Gerät weiterhin ordnungsgemäß funktioniert. </p> <h2>Letztes Update (last_update)</h2> <p>Das Datum und die Uhrzeit des letzten Datenupdates für den jeweiligen Sensor. Der Zeitstempel folgt dem ISO 8601-Format und enthält Informationen über das Datum, die Uhrzeit und die Zeitzone.</p> <h2>Tiefe der Messung (depth_cm)</h2> <p>Diese Spalte gibt an, in welcher Tiefe unter der Erdoberfläche der Sensor installiert wurde. Die Tiefe wird in Zentimetern angegeben.</p> <h2>Beschattung (shaded)</h2> <p>Ein boolescher Wert, der angibt, ob der Sensor in einem beschatteten Bereich installiert ist (`true` für ja, `false` für nein). Die Beschattung kann die gemessenen Temperaturwerte beeinflussen.</p> <h2>Versiegelter Boden (sealed_ground)</h2> <p>Diese Spalte gibt an, ob der Sensor in einem versiegelten Bodenbereich installiert ist (`true` für ja, `false` für nein). Versiegelter Boden beeinflusst Wasserinfiltration und Temperatur.</p> <h2>Notizen (note)</h2> <p>Freitextfeld für zusätzliche Informationen über den Standort oder die Umstände der Sensorinstallation. Hier können beispielsweise Hinweise auf besondere Bedingungen oder Standorte vermerkt sein.</p> <h2>Bodennotizen (soil_note)</h2> <p>Freitextfeld für zusätzliche Informationen über die Bodenbeschaffenheit.</p>
Infiltration and soil moisture Saugraben and Weiherbach experimental sites
<p><em>Infiltration experiments at Saugraben infiltration experimental site and measured and modelled soil moisture at Weiherbach hydrological experimental catchment</em></p>
Satellite-driven 10km global root-zone soil moisture analysis for drought monitoring
<p>Root-zone soil moisture condition is an important component of water cycle at all spatial scales, as it controls various hydrological, biological and meteorological processes such as plant transpiration and hydraulic redistribution. Passive microwave remote sensing offers the possibility to access to near-surface (0 - 5cm) soil moisture measurements over large areas, providing valuable information for agricultural and water resource management. However, the spatial resolution of satellite soil moisture estimates from passive microwave sensor is relatively coarse (25 to 50km) and infrequent in time. Data assimilation algorithms are widely used to obtain spatially complete and daily continuous soil moisture estimates from intermittent remotely sensed soil moisture data and numerical models. </p> <p>The dataset contains the most recent global surface and root-zone soil moisture conditions at 10km generated from the Satellite-Guided Root-zone moisture Analysis and Forecasting System (S-GRAFS) from 2015 to 2022. S-GRAFS is a near-real time data assimilation system that combines complementary information from model simulations and satellite observations to provide soil moisture estimates at near surface and root-zone. In S-GRAFS, satellite soil moisture observations from Soil Moisture Active Passive (SMAP) are assimilated into a simple first-order autoregressive model that captures the soil moisture conditions in response to precipitation. Satellite precipitation from Global Precipitation Measurement (GPM) is used to drive the model to simulate near surface soil moisture at 5cm. The assimilation of SMAP data relies on the four-dimensional variational (4DVAR) method to adjust the modelled surface soil moisture towards observations within a 4-day assimilation window. Soil Water Index (SWI) is then derived from the analysed surface soil moisture using an exponential filter and represents the root-zone soil wetness at approximate 1m depth. The surface and root-zone wetness from S-GRAFS can be converted into absolute soil moisture content using soil physical properties to provide essential support for a wide variety of hydrological and agricultural applications.</p>
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