Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database

Turek, Maria Eliza; Poggio, Laura; Batjes, N.H.; Armindo, Robson André; Lier, Quirijn de Jong van; Sousa, Luís Moreira de; Heuvelink, G.B.M.

doi:10.1016/j.iswcr.2022.08.001

Cited by 16 publications

(10 citation statements)

References 68 publications

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“…However, as the authors discuss, their approach seems to lack observational validation. Additionally, Turek et al (2023) introduced a global 250 m point‐based mapping approach for global SWR using data from the WoSIS Soil Profile Database, resulting in improved soil data availability and quality. The point‐based mapping method demonstrated higher accuracy than map‐based approaches for 330 and 15,000 cm suction, with similar results for 100 cm suction, potentially due to limited SWR observations.…”

Section: Discussionmentioning

confidence: 99%

“…Combined with existing soil properties maps, a number of global SWR and/or AWC maps were derived using these PTFs (Turek et al, 2023). However, the increased availability of Earth Observation datasets and their use as covariates led to the development of new digital soil mapping (DSM) methods.…”

Section: Introductionmentioning

confidence: 99%

“…Identifying the most generic and accurate PTF for a specific region and soil properties dataset can be challenging, as PTF accuracy and applicability depend on factors such as regional soil characteristics, the range of soil properties in the development dataset and the particular hydraulic properties of interest. Turek et al (2023) emphasize that PTF development must occur alongside the development of suitable extrapolation and upscaling techniques to accurately represent soil spatial heterogeneity. Among the many PTFs available, the USDA model Rosetta3 (Zhang & Schaap, 2017) was developed against an extensive dataset of US soils, and stands out due to its widespread use and validation across different regions.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, no other paper citing this dataset has had more reference points. A recent review showed that the highest resolution so far for AWC products was obtained using the Africa Soil Information Service (AfSIS, https://africasoils.net) 250 m world soil dataset (Turek et al, 2023). According to Hengl et al 2021, the original AfSIS 250 m maps had limitations, including data harmonization issues and a lack of satellite data (particularly Sentinel-2 data).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

High‐resolution mapping of available water content in Senegal using iSDA Africa dataset and USDA Rosetta3 model

Lavarenne,

Leroux

2024

European J Soil Science

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This data article presents a high‐resolution map of available water content (AWC) for Senegal, derived from the iSDA Africa dataset and the USDA Rosetta3 model, as well as the method used for its production. The map covers the entire country at a resolution of 30 m and provides a valuable resource for hydrological studies and spatialized crop model simulations in the region where water is a limiting factor for crop production. The dataset is based on existing soil properties data and leverages pedotransfer functions (PTFs) to estimate water retention capabilities from soil properties. This AWC map derived from datasets with enhanced accuracy offers a more precise estimate of soil water retention capacity, which can be instrumental in informing water and agricultural management, policy decisions and investments. The dataset, including intermediate variables, is available in geotiff format at Cirad Dataverse under the DOI 10.18167/DVN1/SGNSII and complies with FAIR data principles, allowing its broad reuse. The code used to produce this dataset is also made available under the DOI 10.5281/zenodo.10078399, so that AWC maps can be produced for any territory covered by the iSDA Africa product.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High‐resolution mapping of available water content in Senegal using iSDA Africa dataset and USDA Rosetta3 model

Lavarenne,

Leroux

2024

European J Soil Science

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“…Finally, we retrieved data on plant-available soil water holding capacity in the rst and second meter of soil depth to account for the capacity of the soil to supply water during rain-free periods 41 . For model training and validation, we considered the climatic conditions at the GYGA Ypot sites and the average soil and cropping system properties within a 100-km radius buffer around each site.…”

Section: Selection Of Gridded Environmental Predictors For the Metamodelmentioning

confidence: 99%

Integrating agronomy and machine learning to generate high-resolution global maps of yield potential with local relevance

Grassini,

Aremburu-Merlos,

Loon

et al. 2024

Preprint

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Reliable data on yield potential is crucial for identifying areas with opportunities for production improvement. Here, we integrated an agronomically robust bottom-up approach with machine learning to generate high-resolution global maps of yield potential for maize, wheat, and rice. Our machine learning metamodel leverages site-specific yield potential derived from locally evaluated crop growth simulations and gridded climate, soil, and cropping system global databases. The metamodel showed high accuracy in predicting yield potential for the three crops, but the prediction uncertainty was higher in regions where local estimates of yield potential were missing. Our work demonstrates the benefits of integrating bottom-up and machine learning methods to achieve global coverage at high spatial resolution and ensure local relevance. The novel global yield potential maps can help to identify areas with large room to increase crop yields and serve studies assessing food security, land use, and climate change from local to global levels.

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Hand-feel soil texture classes and particle-size distribution as predictors of soil water content at field capacity. Further insights into the sources of uncertainty

Richer-de-Forges,

Chen,

Arrouays

et al. 2024

CATENA

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Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database

Cited by 16 publications

References 68 publications

High‐resolution mapping of available water content in Senegal using iSDA Africa dataset and USDA Rosetta3 model

High‐resolution mapping of available water content in Senegal using iSDA Africa dataset and USDA Rosetta3 model

Integrating agronomy and machine learning to generate high-resolution global maps of yield potential with local relevance

Hand-feel soil texture classes and particle-size distribution as predictors of soil water content at field capacity. Further insights into the sources of uncertainty

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