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Report on the classification into FAO-Unesco soil units of profiles selected from the NRCS pedon database for IGBP-DIS
... Appendix 1 lists the countries from where the soil profiles originate. The classification of these soils is presented in Appendix 2. All profiles from the NRCS data set have been classified at ISRIC into the original (FAO-Unesco, 1974) and revised (FAO, 1990a) legend (see Spaargaren and Batjes, 1995). About 94 % of the 1,125 profiles are classified in the Revised Legend (FAO, 1990a) and about 88 % according to Soil Taxonomy (Soil Survey Staff, 1994 and earlier versions). ...
... In some cases, profiles held in the source data files differed from those published elsewhere for the same profiles. This was the case for some NRCS profiles from Brazil, Korea and Zambia (see Spaargaren and Batjes, 1995), some SDB profiles from Botswana (see FAO, 1990b), and some ISIS profiles. This aspect illustrates the difficulty in preserving data integrity in digital files since their contents can easily be corrupted. ...
... Based on the 2017/2018 crop cover map (https://lcviewer.vito.be/2018) (Copernicus:Europe's eyes on Earth, 2020) and the FAO 1974 soil classification (Spaargaren and Batjes, 1995), Calcic Kastanozems (Kk), Calcic Cambisols (Bk) and Chromic Luvisols (Lc) dominate the cropped areas of Morocco in this order of importance (Supp. Doc. ...
A soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. However, achieving this higher accuracy comes with associated costs, complexity, and resource requirements, thus limiting widespread adoption. Furthermore, there is a lack of comprehensive frameworks for developing and utilizing soil spectral libraries (SSLs) to make predictions for specific samples. While calibration samples are necessary, there is the need to optimize SSL development through strategically determining the quantity, location, and timing of these samples based on the quality of the information in the library. This research aimed to develop a spatially optimized SSL and propose a use-framework tailored for predicting soil properties for a specific farmland context. Consequently, the Moroccan SSL (MSSL) was established utilizing a stratified spatially balanced sampling design, using six environmental covariates and FAO soil units. Subsequently, various criteria for calibration sample selection were explored, including a spatial autocorrelation of spectra principal component (PC) scores (spatial calibration sample selection), spectra similarity memory-based learner (MBL), and selection based on environmental covariate clustering. Twelve soil properties were used to evaluate these calibration sample selections to predict soil properties using the near infrared (NIR) and mid infrared (MIR) ranges. Among the methods assessed, we observed distinct precision improvements resulting from spatial sample selection and MBL compared to the use of the entire MSSL. Notably, the Lin's Concordance Correlation Coefficient (CCC) values using the spatial calibration sample selection was improved for Olsen extractable phosphorus (OlsenP) by 41.3% and Mehlich III extractable phosphorus (P_M3) by 8.5% for the MIR spectra and for CEC by 25.6%, pH by 13.0% and total nitrogen (Tot_N) by 10.6% for the NIR spectra in reference to use of the entire MSSL. Utilizing the spatial auto-correlation of the spectra PC scores proved beneficial in identifying appropriate calibration samples for a new sample location, thereby enhancing prediction performance comparable to, or surpassing that of the use of the entire MSSL. This study signifies notable advancement in crafting targeted models tailored for specific samples within a vast and diverse SSL.
... The soil depths and soil texture maps are taken from the INRA soil database for the Ardeche and Languedoc-Roussillon regions (Robbez- Masson et al., 2000). The parameters of the pedotransfer function are computed based on the USDA soil classification (Spaargaren and Batjes, 1995). Land cover is provided by the Corine Land Cover 2006 database (Aune-Lundberg and Strand, 2010). ...
The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.
... The soil depths and soil texture maps are taken from the INRA soil data base for the Ardèche and Languedoc-Roussillon regions (Robbez-Masson et al., 2000). The parameters of the pedotransfer function are computed based 315 on the USDA soil classification(Spaargaren, 1995). Land cover is provided by the Corine Land Cover 2006 data base (Aune-Lundberg and Strand, 2010). ...
The MARINE hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are exploited in this work: on the one hand, formerly relying on water height, transfers of water through the subsurface now take place in a homogeneous soil column based on the volumetric soil water content (SSF model). On the other hand, the soil column is divided into two layers, which represent respectively the upper soil layer and the deep weathered rocks (SSF-DWF model). The aim of the present work is to assess the performances of these new representations for the simulation of soil saturation during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The performances of the models are estimated with respect to several soil moisture products, either at the local scale or spatially extended: i) The gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; ii) The gridded soil moisture product provided by the LDAS-Monde assimilation chain, based on the ISBA-a-gs land surface model and assimilating satellite derived data; iii) the upper soil moisture hourly measurements taken from the SMOSMANIA observation network; iv) The Soil Water Index provided by the Copernicus Global Land Service (CGLS), derived from Sentinel1/C-band SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data lead to the same conclusions: both the dynamics and the amplitudes of the soil moisture simulated with the SSF and SSF-DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. The opportunity of improving the two-layers model calibration is then discussed. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil moisture simulation during flash floods, with respect to both gridded data and local soil moisture measurements.
... This included re-classification of the original USDA Soil Taxonomy names to the FAO (1988) Legend. For some soil profiles the correlations were imprecise due to a lack of specific information, examples of which may be found in Spaargaren and Batjes (1995). ...
... All classifications refer to an entry in the dataset profile link table (that is, a profile in a particular dataset), thus enabling one classification per profile and dataset. In some cases, the USDA Soil Taxonomy coding is inconsistent between editions as different standard notations have been used in successive versions (e.g., Soil Survey Staff 1975,1992, 2003; examples are given elsewhere (Spaargaren and Batjes, 1995). Alternatively, the original (FAO-Unesco, 1974) and revised Legend (FAO, 1988) to the FAO Soil Map of the World use a well-established coding scheme. ...
To enable the use of new (web) technologies that permit faster and new forms of soil information delivery, ISRIC has developed a centralized and user–focused server database, known as ISRIC World Soil Information Service (WoSIS). This system will serve the international community with validated and authorized data derived from a growing range of 'shared' data sources. Ultimately, upon evaluation, all standardized/harmonized data managed in WoSIS will be made available on-line in one uniform OGC-compliant format that will allow accessing, processing and visualization through one set of tools.
So far some 98,000 profiles have been imported into WoSIS 2 from disparate soil databases; some 76,000 of these are georeferenced within defined limits. The number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Further, in most source data sets, there are fewer data for soil physical as opposed to soil chemical attributes and there are fewer measurements for deeper than for surficial horizons. Generally, limited quality information is associated with the various source data.
Special attention has been paid to the standardization of soil analytical method descriptions with focus on the set of soil properties considered in the GlobalSoilMap specifications. Newly developed procedures for the above, that consider the soil property, analytical method and unit of measurement, have been applied to the present set of geo-referenced soil profile data.
... The NCSS and WISE were used to derive the soil parameter estimates for the DSMW (p3 of Figure 2). The NCSS profiles with a ST classification were correlated to the 26 major soil groups of the FAO74 according to a tentative approximation Spaargaren and Batjes, 1995]. The use of the NCSS made more soil properties available, which were not included in the WISE, and improved the representation of the major soil groups of the FAO74. ...
We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications. The GSDE provides soil information, such as soil particle-size distribution, organic carbon, and nutrients, and quality control information in terms of confidence level at 30″× 30″horizontal resolution and for eight vertical layers to a depth of 2.3 m. The GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e., taxotransfer rules) and a polygon linkage method to derive the spatial distribution of the soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: the area-weighting method, the dominant soil type method and the dominant binned soil attribute method. The dataset can also be aggregated to a lower resolution. In this paper, we only show the vertical and horizontal variations of sand, silt and clay contents; bulk density; and soil organic carbon as examples of the GSDE. The GSDE estimates of global soil organic carbon stock to the depths of 2.3 m, 1 m and 0.3 m are 1,922.7, 1,455.4 and 720.1 Gt, respectively. This newly developed dataset provides more accurate soil information and represents a step forward to advance Earth system modeling.
... Soil unit classifications (FAO 1988), as presented in the primary SOTER_UT database, were taken at face value. Soil experts, however, may classify the same soil profile differently when the available soil morphological and soil analytical data are ‗limited' and subjective assumptions have to be made (e.g., Goyens et al. 2007;Kauffman 1987;Spaargaren and Batjes 1995). The soil classification code, however, is the primary driver of the taxotransfer procedure (see 2.2.2). ...
This data set describes a harmonized set of soil property estimates for the Upper Tana river catchment, Kenya. The data set was derived from the 1:250 000 scale Soil and Terrain Database for the Upper Tana (SOTER_UT, ver. 1.1; Dijkshoorn et al. 2011) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures.
... Soil unit classifications (FAO 1988), as presented in the primary SOTER_UT database, were taken at face value. Soil experts, however, may classify the same soil profile differently when the available soil morphological and soil analytical data are 'limited' and subjective assumptions have to be made (e.g., Goyens et al. 2007;Kauffman 1987;Spaargaren and Batjes 1995). The soil classification code, however, is the primary driver of the taxotransfer procedure (see 2.2.2). ...
(Superceded by version 3.0, ISRIC Report 2005/08)
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