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767 results for “Soil Moisture”
CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS
<p>This repository contains the dataset ‘CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS’ which is a result from the Transnational Access, within Co-UDlabs project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626. </p> <p>These experiments introduce a new approach which uses low-cost multi-spectral camera (Plant-o-Meter) to estimate plant health (through various parameters) to link with soil moisture of the bioretention systems (biofilters and green roof) as a common element of nature-based solutions (NBS). Soil moisture is used as a proxy for polluted water retention and pollutant removal within the system. The aim of this work is to provide a proof of concept for indirect measurement of urban polluted water treatment through different types of NBS. The experiments have been done on real stormwater biofilters, under controlled conditions (artificial irrigation). Data from the experimental campaign presented here will help to understand fundamental correlations between plant health parameters and the change in soil moisture, giving us a tool for quick assessment of the need for maintenance of various NBS as a crucial parts of UD systems.</p> <p>The authors acknowledge financial support from the European Union under the Horizon 2020 program within a contract for Integrating Activities for Starting Communities (Ref. 101008626)</p> <p>>>>For detailed description of the dataset, please see <a href="https://zenodo.org/api/records/14191602/draft/files/README_NBS-RECON_dataset.txt/content" target="_blank" rel="noopener noreferrer">README_NBS-RECON_dataset.txt</a> and <a href="https://zenodo.org/api/records/14191602/draft/files/CoUDlabsDataStorageReport_NBS-RECON.docx/content" target="_blank" rel="noopener noreferrer">CoUDlabsDataStorageReport_NBS-RECON.docx</a></p>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
<p>A daily 0.1<sup>°</sup> dataset of precipitation (<em>P</em>), soil moisture (SM), and snow water equivalent (SWE) in 1981-2017 across China.</p>
Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities
<p>These datasets were generated to assess linear and nonlinear Granger causalities in the submitted manuscript, Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities by Bhatti et al. submitted to AGU-GRL. Nonlinear GC here is achieved with the Kernel Granger causality by Marinazzo et al. (2008). The data was used to develop theoretical experiments that help validate the strengths and limitations of both the linear Granger causality and the Kernel Granger causality before applying to real world datasets</p>
PrISM satellite rainfall product (2010-2021) based on SMOS soil moisture measurements in Africa (3h, 0.1°)
<p>The PrISM product is a satellite precipitation product available for Africa over a regular grid at 0.1° (about 10x10 km²) and every 3 hours. It is obtained from the synergy of SMOS satellite soil moisture measurements and IMERG-Early Run precipitation product through the PrIMS algorithm (<em>Pellarin et al., 2009, 2013, 2020, 2022, Louvet et al., 2015, </em>Román-Cascón et al. 2017). </p>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
Soil Moisture and Sea Surface Temperature Data for Wikle et al. (2022)
<p>Raw data (.nc) in NetCDF4 format, and formatted and rearranged data (.csv) in CSV format. With R Markdown document detailing the steps taken. All data obtained originally from NOAA's NCEP and NCDC data store systems. </p> <p>Data used in developing and demonstrating explainable AI models for <em>An Overview of Model Agnostic Explainability Methods for Machine Learning Applied to Environmental Data</em>, Wikle et al. (2022), for the <em>Special Issue on Environmental Data Science</em> for <em>Environmetrics. </em>See https://zenodo.org/record/6353636 for the corresponding model codebase. </p>
Dataset - Spatially explicit linkages between redox potential cycles and soil moisture fluctuations
<p>This repository holds data collected during three lysimeter experiments where laboratory column scale lysimeters have been subjected to different wetting/drainage cycles. The lysimeters have been filled with forest soil in the Lausanne forest characterised by the SwissMEX project </p> <p>The following dataset contains soil redox potential and soil moisture measurements that have been continuously monitored in time for different depths. Pore water samples of specific dissolved chemicals have been collected at the end of each cycle. </p> <p> </p> <p>Specifically, this dataset is composed by the following files:</p> <ul> <li> "Lysimeter_configuration.png" illustrates the lysimeter used and the sensors scheme adopted.</li> <li>"METADATA.txt" contains specific information about each recorded variable and data point collected throughout the three experiments SM-B, SM-I1 and 2.</li> <li>Pore water analysis data</li> <li>Soil moisture and tension data</li> <li>Soil redox potential data</li> </ul> <p>We thank Pascal Froidevaux for providing the lysimeter used for SM-B experiment. The authors acknowledge key funding provided by the Swiss National Science Foundation through its grant number CRSII5 186422.</p> <p> </p> <p> </p>
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>
Inputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the inputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of the public 2013-2019 COSMOS-UK dataset, daily and subhourly observations and metadata for four stations: WYTH1, WADDN, SHEEP and CHIMN. These stations represent the first sites to prototype COSMOS sensors in the UK, see further details in Evans et al. (2016) and they are situated in human-intervened areas (grassland and cropland), except for one in a woodland land cover site.</p> <p>Data from COSMOS-UK up to the end of 2019 are available for download from the UKCEH Environmental Information Data Centre (EIDC). The data are accompanied by documentation that describes the site-specific instrumentation, data and processing including quality control. The full dataset is available for <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">download</a> under the terms of the Open Government License.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Doran Khamis (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/dorankhamis">@dorankhamis</a></p> </li> <li> <p>Matt Fry (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/mattfry-ceh">@mattfry-ceh</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>UK Centre for Ecology & Hydrology (creator)</p> </li> <li> <p>Natural Environment Research Council (support)</p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>S. Stanley, V. Antoniou, A. Askquith-Ellis, L.A. Ball, E.S. Bennett, J.R. Blake, D.B. Boorman, M. Brooks, M. Clarke, H.M. Cooper, N. Cowan, A. Cumming, J.G. Evans, P. Farrand, M. Fry, O.E. Hitt, W.D. Lord, R. Morrison, G.V. Nash, D. Rylett, P.M. Scarlett, O.D. Swain, M. Szczykulska, J.L. Thornton, E.J. Trill, A.C. Warwick, and B. Winterbourn. Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]. 2021. URL: <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>, <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">doi:10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>.</p> </li> </ul> <p><strong>Further references</strong></p> <ul> <li> <p>Jonathan G. Evans, H. C. Ward, J. R. Blake, E. J. Hewitt, R. Morrison, M. Fry, L. A. Ball, L. C. Doughty, J. W. Libre, O. E. Hitt, D. Rylett, R. J. Ellis, A. C. Warwick, M. Brooks, M. A. Parkes, G. M.H. Wright, A. C. Singer, D. B. Boorman, and A. Jenkins. Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk. <em>Hydrological Processes</em>, 30:4987–4999, 12 2016. <a href="https://doi.org/10.1002/hyp.10929">doi:10.1002/hyp.10929</a>.</p> </li> <li> <p>M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem. Cosmos: the cosmic-ray soil moisture observing system. <em>Hydrology and Earth System Sciences</em>, 16(11):4079–4099, 2012. URL: <a href="https://hess.copernicus.org/articles/16/4079/2012/">https://hess.copernicus.org/articles/16/4079/2012/</a>, <a href="https://doi.org/10.5194/hess-16-4079-2012">doi:10.5194/hess-16-4079-2012</a>.</p> </li> </ul>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data
<p>This repository serves as hub for the single <strong><em>GPM_API data set repositories</em></strong> related to the publication</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> <ul> <li>GPM_API 2015: <a href="https://doi.org/10.5281/zenodo.6353260">10.5281/zenodo.6353260</a></li> <li>GPM_API 2016: <a href="https://doi.org/10.5281/zenodo.6413889">10.5281/zenodo.6413889</a></li> <li>GPM_API 2017: <a href="https://doi.org/10.5281/zenodo.6413905">10.5281/zenodo.6413905</a></li> <li>GPM_API 2018: <a href="https://doi.org/10.5281/zenodo.6413907">10.5281/zenodo.6413907</a></li> <li>GPM_API 2019: <a href="https://doi.org/10.5281/zenodo.6413909">10.5281/zenodo.6413909</a></li> <li>GPM_API 2020: <a href="https://doi.org/10.5281/zenodo.6413911">10.5281/zenodo.6413911</a></li> </ul> <p> </p> <p>The related article can be found here:<br> 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>
Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects
<p>Controlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta-analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer-reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy-logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium-textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse- or fine-textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse- and medium-textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy-logic-based approach revealed that while potentially suitable areas are mostly concentrated in the well studied U. S. Midwest, these areas also exist in other regions where CDSI may warrant further study.</p>
Radiocarbon and soil properties along the Kalahari moisture gradient in Botswana
<p>This dataset presents radiocarbon data from four sites located in Botswana. The dataset first presents general information about the sites (on the tab "site"), followed by more detailed information (on the tab "profile") about all sampling locations. On the 'layer' tab, information about selected soil properties and the radiocarbon values are shown.</p> <p>The dataset is part of the International Soil Radiocarbon Database (ISRaD) and associated with the following publication: Dintwe et al. (2015) Soil organic C and total N pools in the Kalahari: potential impacts of climate change on C sequestration in savannas, Plant Soil, 396_27-44, doi: 0.1007/s11104-014-2292-5.</p> <p> </p>
Combined_ST_SM_Changes_Impacts Soil moisture observations
<p>There are 956 observational soil moisture data files with detailed information about the files in ReadMe.txt.</p>
WRF dataset for soil moisture initialization experiments
<p>Modeling results of "Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization", by Yoon et al. (submitted). Data contains the modeling outputs (500GPH, SAT and soil moisture) from CTL and LIS experiments. Detailed description can be founded in the research paper.</p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
A global 1-km surface soil moisture product from 2000 to 2020
<p>Soil moisture is one of the essential climate variables, and it controls the water, carbon, and energy exchanges between land and the atmosphere. Accurate and detailed knowledge of the spatial and temporal distribution of soil moisture is critical for various earth system applications. A long-term global 1-km daily surface soil moisture product has been generated from 2000 to 2020, as part of the Global Land Surface Satellite (GLASS) products suite. This product (GLASS SM) was generated mainly from the GLASS albedo, LST, and LAI products, ERA5-Land reanalysis soil moisture product, and auxiliary datasets based on an ensemble machine learning model. Site-independent validation results showed that the median unbiased RMSE and R for the model was 0.052 m<sup>3</sup>/m<sup>3</sup> and 0.74, respectively.</p> <p>Data values contained in the GLASS SM product represent the volumetric water content of the uppermost soil layer (0–5 cm). The files are stored in the Sinusoidal projection and provided in Geo Tiff format. “Nodata” value is set to -9999.</p>
Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States: Data
<p>The data in this repository are associated with the manuscript from Huber et al. (2024) titled "Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States" in the Journal of Geophysical Research: Atmospheres. Additional information regarding these data can be found in the attached readme file.</p>
Global soil moisture simulated by SoilClim and mHM models at 0.5° resolution for the 1980–2022 period
<p>This deposit contains two .zip archives (SoilClim_AWR_2m_1980_2022.zip and mHM_SM_2m_1980_2022.zip), each containing 1570 GeoTIFF files. </p> <p>The file SoilClim_AWR_2m_1980_2022.zip contains 10-day simulations of relative available water (AWR), where 100% represents the full field capacity and 0% represents the wilting point, produced the SoilClim water balance model for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p> <p>The file mHM_SM_2m_1980_2022.zip contains 10-day simulations of soil moisture (SM), produced the mesoscale Hydrologic Model (mHM) for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p>
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