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46 results for “global soil moisture”
SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture
<p><strong>SM2RAIN-ASCAT is a new global scale rainfall product</strong> obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over a regular grid at 0.1-degree sampling (3600x1801) on a global scale. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (<em>Wagner et al., 2013</em>) for the period from January 2007 to December 2022 (16 years), for version 2.1.2n.</p> <p>The rainfall dataset is provided in NetCDF format. A total of 16 NetCDF files, one per year, are provided. The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests. In addition to the daily accumulated rainfall value, also the rainfall noise (mm/day) is provided for every day.</p> <p><strong>Version 2.1.2 should not be used due to an error in the precipitation data. Version 2.1.2n with respect to version 2.1 is calibrated anew and extended to December 2022.</strong></p> <p>A GeoTIFF version of the dataset (v1.5) is available here: <a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a></p> <p>A monthly version at 0.25- and 0.5-degree resolution (v1.4) is available here: <a href="https://doi.org/10.5281/zenodo.4570191">https://doi.org/10.5281/zenodo.4570191</a></p> <p>A sample dataset that can be used for testing SM2RAIN algorithm is available here: <a href="../record/2580285#.XLrYDugzbIU">https://zenodo.org/record/2580285</a></p> <p>Details on the dataset development and its assessment with ground and reanalysis observations are provided as:</p> <p><strong>Brocca, L.</strong>, Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p><strong>Simple Python and Matlab codes for the extraction of SM2RAIN-ASCAT rainfall at one and multiple station(s)\location(s) are available at (note that reader for versions <1.3 are not usable for version >1.3): </strong><a href="https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader">https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader</a></p> <p><strong>The SM2RAIN code in Python is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rain">https://github.com/IRPIhydrology/sm2rain</a><br><strong>The SM2RAIN code in Matlab is available here</strong>: <a href="https://github.com/IRPIhydrology/SM2RAIN_Matlab">https://github.com/IRPIhydrology/SM2RAIN_Matlab</a><br><strong>The SM2RAIN code in R is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rainR">https://github.com/IRPIhydrology/sm2rainR</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li>EUMETSAT Global SM2RAIN project (contract n° EUM/CO/17/4600001981/BBo)</li> <li>EUMETSAT "Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)" CDOP 3 (EUM/C/85/16/DOC/15).</li> </ul>
SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.</strong></p> <p><strong>Description:</strong></p> <ul> <li>A <strong>seamless global daily</strong> (<strong>SGD</strong>) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include <strong>2553</strong> global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about <strong>20GB</strong> memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <strong><a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a></strong>.</li> <li>An example Python code of extracting this dataset is also available at <strong><a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install <strong>netCDF4</strong> toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre> <p> </p> </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre> <p> </p> </li> </ul>
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>
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>
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>
Long-Term Global Satellite Soil Moisture from Maximized Temporal Correlations (1998-2015)
<p>A long-term merged satellite soil moisture product spanning 1998 to 2015. An existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products within the period. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth is the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, which is being used for the first time in a merging scheme. </p> <p>For more details on the quality of the data and the methodology used, please refer to the references listed:</p> <p>Hagan, D.F.T.; Wang, G.; Kim, S.; Parinussa, R.M.; Liu, Y.; Ullah, W.; Bhatti, A.S.; Ma, X.; Jiang, T.; Su, B. Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging. Remote Sens. 2020, 12, 2164.</p> <p>Kim, S.; Parinussa, R.M.; Liu, Y.Y.; Johnson, F.M.; Sharma, A. A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation. Geophys. Res. Lett. 2015, 42, 6662–6670.</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>
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>
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>
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>
A seamless global 5 km surface soil moisture product from 1982 to 2021
<p><span>Soil moisture (SM) is an essential climate-sensitive variable that exhibits high spatial and temporal variability. Long-term SM data records (> 30 years) can benefit a range of climate change-related applications. A four-decade global 5-km daily SM product has been generated from 1982 to 2021, as part of the Global Land Surface Satellite (GLASS) products suite. This product (GLASS-AVHRR SM) was derived mainly from the GLASS-AVHRR albedo and LST products, the ERA5-Land reanalysis SM product, and auxiliary datasets, using an attention-based deep learning model. The GLASS-AVHRR SM product has the advantages of long-term coverage, spatial and temporal integrity, reliable accuracy and consistency.</span></p> <p><span>The data values in the GLASS-AVHRR SM product represent the volumetric water content of the uppermost soil layer (0–5 cm). The files are stored in geographic projection and provided in Geo Tiff format, with "No Data" values set to -9999.</span></p>
Global soil moisture–atmosphere feedback and N2O emission dataset
<p><span>Soil moisture is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that soil moisture–atmosphere feedbacks dominate the changes in land carbon fluxes. </span><span>However, the influence of soil moisture–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. </span><span>To fill this gap, we utilized in situ observation coupled with gridded and remote sensing data to analyze N<sub>2</sub>O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N<sub>2</sub>O fluxes, as the result of soil moisture–atmosphere feedback impact on N fluxes. We found that soil moisture–temperature feedback dominates land N<sub>2</sub>O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces soil moisture, which increases the dominant N<sub>2</sub>O production nitrifier (containing <em>amoA </em>AOB gene) and decreases the N<sub>2</sub>O consumption denitrifier (containing the<em> nosZ</em> gene), consequently will potential increasing N<sub>2</sub>O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of soil moisture to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N<sub>2</sub>O emissions. Our data-driven assessment deepens the understanding of the impact of soil moisture-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between soil moisture and atmospheric temperature when estimating the response of the N<sub>2</sub>O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.</span></p>
Supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale
<p>This file contains the supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale (<em>submitted</em>). </p> <p>For more details see the corresponding manuscript (once it is published) and the github repository (https://github.com/SophievF/Global_Mox_analysis/tree/main). </p>
Improved estimation of global soil moisture of different shared socioeconomic pathways for 2016-2099
<p>We design a novel Transformer SM Simulation Net (TSMSNet) to conduct global monthly 0.5°×0.5° SM simulation of SSP1-2.6, SSP2-4.5, and SSP5-8.5 from 2016 to 2099. Nine qualified future SM datasets, along with corresponding spatial distribution dataset of error parameters, and geographic background data are selected as model inputs. The learning target is calculated through merging the merits from Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM. The results indicate the TSMSNet simulated SM (R = 0.68, ubRMSE = 0.045 m<sup>3</sup>/m<sup>3</sup>) performs notable superiority in matching both temporal variation and absolute value of in-situ measurements across different landcover and climate regions. Besides, the TSMSNet simulated SM could favorably match the long-term trend of learning target. TSMSNet simulated SM has an overwhelming drying trend during 2016 to 2099. The decline magnitude rises accompanied by SSP changing from sustainable pathway to fossil-fueled development. The areas with significant drying trend mainly distributed in plateau and mid-latitude region. In terms of land cover types, evident drying trends are found in cropland and forest. SM shows faster descent rate in livable areas than unlivable areas. In summary, we develop a reliable future SM dataset, that is expected to act as a valuable reference for understanding future water cycle patterns.</p>
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at global scale (v1.0)
<p>This study investigates the estimation of daily SSM using eight optimised ML algorithms and ten ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using the international soil moisture network (ISMN) data collected from 1722 stations distributed across the World. </p>
Global soil moisture–atmosphere feedback and N2O emission dataset
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