Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2941-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2941-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Jun 2024
Data description paper |
| 25 Jun 2024
| 25 Jun 2024
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
Anatol Helfenstein
CORRESPONDING AUTHOR
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Vera L. Mulder
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Mirjam J. D. Hack-ten Broeke
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Maarten van Doorn
Nutriënten Management Instituut, Nieuwe Kanaal 7C, 6709 PA, Wageningen, the Netherlands
Environmental Systems Analysis Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, the Netherlands
Kees Teuling
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Dennis J. J. Walvoort
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Gerard B. M. Heuvelink
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
ISRIC – World Soil Information, P.O. Box 353, 6700 AJ, Wageningen, the Netherlands
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These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
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Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
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The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
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Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
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We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
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Short summary
Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Earth system models and decision support systems greatly benefit from high-resolution soil...
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