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767 results for “Soil Moisture”
Figure 1 in Effect of degree of water stress on growth and fecundity of velvetleaf (Abutilon theophrOsti) using soil moisture sensors
Figure 1. Soil moisture content in pots was measured using (A) Meter Group 5TM moisture sensors and (B) Em50 data loggers to determine degree of water stress on Abutilon threophrasti in a greenhouse study conducted at the University of Nebraska–Lincoln.
Fig. 1 in Effect of soil moisture on Plectris aliena (Coleoptera: Scarabaeidae) oviposition
Fig. 1. Mean numbers of eggs laid by Plectris aliena in laboratory cages with soil moisture levels of 2%, 11%, and 20%. Means with the same letter are not significantly different from one another (Tukey's multiple comparison test, 5% level). Vertical bars are standard errors of the means.
Database Manuscript Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics - Gonzalez Dominguez et al
<p>The database contained the data used in the manuscript <strong>Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, by Gonzalez Dominguez et al. </strong></p>
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>
SOIL-WATERGRIDS v1, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014, dataset and modelling
<p>SOIL-WATERGRIDS is a comprehensive data product of the monthly estimates of volumetric soil water content at three depths within the root zone and the depth of the water table globally gridded at a resolution of 0.25x025 degree per grid cell from 1970 to 2014. The SOIL-WATERGRIDS data product also provides the full-scale global model (BRTSim, https://sites.google.com/site/thebrtsimproject/home) that allows third party users to assess the entire volumetric soil water content and water table dynamics from land surface to 50 m depth. </p> <p>This package includes a Technical Documentation with the details about the use of the data product.</p>
SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States
<p><a href="https://waterai.earth/smaphb/">SMAP-HydroBlocks (SMAP-HB)</a> is a hyper-resolution satellite-based surface soil moisture product that combines NASA's Soil Moisture Active-Passive (SMAP) L3 Enhance product, hyper-resolution land surface modeling, radiative transfer modeling, machine learning, and in-situ observations. The dataset was developed over the continental United States at 30-m 6-hourly resolution (2015–2019), and it reports the top 5-cm surface soil moisture in volumetric units (m3/m3).</p> <p>This repository contains the following two versions of the SMAP-HydroBlocks dataset:</p> <ol> <li><strong>SMAP-HB_hru_6h.zip</strong>: SMAP-HydroBlocks data in the Hydrological Response Unit (HRU) space. Storing the data in the HRU space enables the entire 30-m 6-h dataset to be compressed to 33.8 GB. A python script and instructions to post-process and remap the data from the HRU-space into geographic coordinates (latitude, longitude) is provided at <a href="https://github.com/NoemiVergopolan/SMAP-HydroBlocks_postprocessing">GitHub</a>. After post-processed, files are stored in netCDF4 format with a Plate Carrée projection.</li> <li><strong>SMAP-HB_1km_6h.zip</strong>: SMAP-HydroBlocks data at 1-km 6-h resolution. This aggregated version is already post-processed, and thus it is already in geographic coordinates (latitude, longitude), stored in netCDF4 format, with a Plate Carrée projection, and comprising 31.5 GB of data. </li> </ol> <p>Different subsets of the original dataset can be made available on request from Noemi Vergopolan (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at <a href="http://waterai.earth/smaphb/">https://waterai.earth/smaphb/</a> </p> <p> </p> <p>Please cite the following paper when using the dataset in any publication:</p> <p>Vergopolan, N., Chaney, N.W., Pan, M. <em>et al.</em> SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. <em>Sci Data</em> 8<strong>, </strong>264 (2021). <a href="https://doi.org/10.1038/s41597-021-01050-2">https://doi.org/10.1038/s41597-021-01050-2</a></p> <p>Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., & Wood, E. F. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, 111740. <a href="https://doi.org/10.1016/j.rse.2020.111740">https://doi.org/10.1016/j.rse.2020.111740</a></p> <p> </p> <p>To download all the files via the command line, please try <a href="https://zenodo.org/record/1261813">zenodo_get</a>:</p> <pre><code>pip install zenodo-get zenodo_get 5206725</code></pre>
High-resolution soil moisture data (1km)
<p>High-resolution soil moisture data based on ESA CCI surface soil moisture data in southwestern Europe (Iberia Peninsula).</p> <p>Refs:</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
Grid-to-Grid daily simulated soil moisture 1964-2018, at selected UK Soil Moisture Databank sites.
<p>This dataset contains Grid-to-Grid (G2G) daily simulated soil moisture time-series at selected UK Soil Moisture Databank (UKSMD) sites. It was created to facilitate an evaluation of G2G simulated soil moisture against the UKSMD neutron probe soil moisture observations (Bell et al., 2022). </p> <p>G2G (Bell et al., 2009) is a national-scale gridded hydrological model, which has been widely applied to simulate river flows and more recently soil moisture. Here, the model was run at 1km resolution from 01/01/1964 - 16/12/2019 across Great Britain. Simulated soil moisture time-series are provided for the 1km grid-cells closest to selected UKSMD site locations. The G2G simulates vertically-integrated soil moisture in units of mm/m. For further explanation of G2G soil moisture, please see Kay et al., 2022 (https://iopscience.iop.org/article/10.1088/1748-9326/ac7a4e). </p> <p>The data is provided as two plain text files:</p> <p>1) g2g_soilmoist_1964_2018.txt contains the simulated soil moisture values. The first three columns specify the simulation date (day, month, year). Subsequent columns are soil moisture (mm/m) time-series at each site, with the UKSMD site ID given as column headers. </p> <p>2) site_locations.csv contains the locations of the UKSMD sites. In some cases there were multiple tubes with slightly different locations within a larger site, and here we are providing the location of the specific tube used. Columns specify: SITE_NAME (the site ID), TUBE_NAME (the tube number), EASTING and NORTHING (easting and northing in British National Grid). The site ID and tube names used in this document are consistent with the UKSMD documentation. </p> <p>References:</p> <p>Bell, V. A., Kay, A. L., Jones, R. G., Moore, R. J., & Reynard, N. S. (2009). Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK. Journal of Hydrology, 377(3-4), 335-350.</p> <p>Bell, V.A.; Davies, H.N.; Fry, M.; Zhang, T.; Murphy, H.; Hitt, O.; Hewitt, E.J.; Chapman, R.; Black, K.B. (2022). Collated neutron probe measurements and derived soil moisture data, UK, 1966-2013. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/450bb14b-c711-47af-8792-f9bd88482cd4</p> <p>Kay, A. L., Lane, R. A., & Bell, V. A. (2022). Grid-based simulation of soil moisture in the UK: future changes in extremes and wetting and drying dates. Environmental Research Letters, 17(7), 074029.</p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS ecosystem sites
<p>Standardized drought indices to support research at ICOS ecosystem sites. Dataset to Nature Scientific Data submission.</p> <p>"The dataset comprises four files for each of the 101 sites: "[site_name]_input" contains the observational data extracted from E-OBS, PET estimates as well as the simulated soil water storage and actual evapotranspiration from mHM; and threemore files for each of the standardized drought indices ("SSMI_[site_name]", "SPI_[site_name]", "SPEI_[site_name]"). Details on the variables, their units and their origin are given in Tab. 1. For the SPI and SPEI, the file of each site contains the estimates for various aggregation times, ranging from 5 to 730 days in steps of 5 days from 5 to 365 and steps of 10 days from 370 to 730. Each data file has a daily temporal resolution and covers the time span from 1950 to 2021."</p>
Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau
<p>Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3%, 50.7%, 26.2%, and 56.7% compared to SMAP L4 SM while 38.5%, 52.1%, 29.5%, and 58.7% compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.</p> <p> </p> <p>The remaining data will be uploaded soon.</p>
Soil temperature, moisture, and ground heat flux measurements at LPTEG-TREES-1 site, 2019/07/01-2019/09/09
<p>This dataset includes the original measurements of soil temperature, moisture, and surface ground heat flux reconstructed from heat flux plate measurements at the LPTEG-TREES-1 site (N66°53’55’’, E66°45’27’’). Soil temperature (T_soil, °C) was measured at 2 cm below the peat layer surface. Soil liquid water content (theta_liq, m<sup>3</sup>/m<sup>3</sup>) was measured 2 cm below the mineral soil layer surface. Observation for ground heat flux at the soil surface (G_obs, W/m<sup>2</sup>) was reconstructed from the heat flux plate (buried 6 cm below the mineral soil surface) measurement plus the energy storage above the heat flux plate calculated based on soil temperature and soil heat capacity.</p>
A 1 km daily soil moisture dataset over the Qinghai-Tibet Plateau (2001-2010)
<p>Soil moisture is the key variable of water and energy cycle, but the long-term, high-resolution soil moisture data with high accuracy is still relatively lacking on the Qinghai-Tibet Plateau. Therefore, we provide the 1 km seamless daily soil moisture data over the Qinghai-Tibet Plateau during 2001-2010 (named as BTCH). Firstly, several predictors including the vegetation index (NDVI and EVI), land surface temperature (LST), evapotranspiration, precipitation, topography (DEM, aspect, slope, TWI), soil properties and three soil moisture related indices (SWCI SIWSI VSDI) were utilized. Five machine/deep learning methods including the artificial neural network (ANN), convolutional neural network (CNN), residual neural networks (ResNet), the long short-term memory network (LSTM) and XGBoost were trained for each year taking the ESA CCI soil moisture data as target. Then the Bayesian three-cornered hat method was adopted for model integration and the final dataset was generated. Evaluaion against four in-situ measurement networks shows that the BTCH dataset has relativey high accuracy both as station and network scales with mean unbiased RMSE values of 0.048 m3/m3 and 0.034 m3/m3. The dataset can be used for various regional hydrological, meteorological, ecological analysis and modeling.</p>
NAIADES Soil moisture sensors raw data
<p>Raw data from initial soil moisture sensors (LSE-01) tests performed during the initial phases of the NAIADES project. Sensors were installed in flower boxes and flowerbeds across the city of Carouge, Switzerland, data was transmitted each 20 minutes via LoRaWAN.</p> <p>This data was exported from influxdb, unrelevant fields were omitted, device addresses were partially obfuscated.</p> <p>Units: (field:unit):</p> <p>water_SOIL: V/V%</p> <p>temp_SOIL:°C</p> <p>conduct_SOIL:uS/cm</p> <p> </p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
Figure 6 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 6 Effect of time and moisture on the emergence of flies from three soil types (sandy, sandy clay loam and clay loam).The lines in the graphs of the relation between the number of flies emerged and time (a-c) represent nonlinear models with a quadratic term [a) y = 4.12***-0.94***x+0.04x^2, b)y = 0.81*+0.5*x-0.098***x^2 and c) y = 2.47***-0.26x-0.02x^2], while the lines in the graphs of the relation between number of flies emerged and moisture (d-f) represent linear models with Poisson distributions [d) y = 0.11+0.0016x, e) y = -0.69***+0.01***x and f) y = -0.37***+0.008***x]. *:P<0.05; *** P<0.001.
Figure 5 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 5 Effect of soil depth and moisture on the number of pupae in three soil types (sandy, sandy clay loam and clay loam). The lines in the graphs of the relation between the number of pupae and depth (a-c) represent exponential models [a) y=exp(1.58-1.24***x), b) y=exp(1.42-0.21***x) and c) y=exp(1.18-0.16***x)], while the lines in the graphs of the relation between number of pupae and moisture (d-f) represent linear models with Poisson distributions [d) y = 0.56-0.0006x, e) y = 0.55-0.0017x and f) y = 0.55-0.0025x].*** P<0.001.
Figure 4 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 4 Number of pupae per centimeter for each treatment (T1 to T4 with sandy soil, T5 to T8 with sandy clay loam and T9 to T12 with clay loam) in combination with different moisture levels (0%, 30%, 60% and 90% for T1 to T4, T5 to T8 and T9 to T12, respectively). Treatments: T1 = sandy × 0% moisture, T2 = sandy x 30% moisture, T3 = sandy x 60% moisture, T4 = sandy x 90% moisture, T5 = sandy clay loam x 0% moisture, T6 = sandy clay loam x 30% moisture, T7 = sandy clay loam x 60% moisture, T8 = sandy clay loam x 90% moisture, T9 = clay loam x 0% moisture, T10 = clay loam x 30% moisture, T11 = clay loam x 60% moisture, and T12 = clay loam x 90% moisture.
Figure 3 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 3 Illustration of the steps of the experiment: A) Larvae on the soil surface; B) Containers used in the experiment; C) Removal of a 1 cm ring; D) Transfer of the soil to a plastic tray; E) Sorting and counting of the pupal cases; and F) Insects that were unable to rupture the soil layer. Photos: Eric Joel Ferreira do Amaral.
Figure 1 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 1 Representation of the steps for rearing B. carambolae: A) Oviposition container; B) Cage with adults; C) Eggs; D) Feed based on carrots in a plastic tray containing larvae; E) Paper envelope containing the plastic tray with larvae. Photos: Eric Joel Ferreira do Amaral.
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