. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.

/pdf/soilgrids-2-0-producing-soil-information-for-the-globe-with-egkdd930cv.pdf

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty

METIS-ID: 167825 The Harmonized World Soil Database is a 30 arc-second raster database with over 15000 different soil mapping units that combines existing regional and national updates of soil information worldwide (SOTER, ESD, Soil Map of China, ISRIC-WISE) with the information contained within the 1:5 000 000 scale FAO-UNESCO Soil Map of the World (FAO, 1971-1981). The resulting raster database consists of ... 21600 rows and 43200 columns, which are linked to harmonized soil property data. The use of a standardized structure allows for the linkage of the attribute data with the raster map to display or query the composition in terms of soil units and the characterization of selected soil parameters (organic Carbon, pH, water storage capacity, soil depth, cation exchange capacity of the soil and the clay fraction, total exchangeable nutrients, lime and gypsum contents, sodium exchange percentage, salinity, textural class and granulometry). Reliability of the information contained in the database is variable: the parts of the database that still make use of the Soil Map of the World such as North America, Australia, West Africa and South Asia are considered less reliable, while most of the areas covered by SOTER databases are considered to have the highest reliability (Southern Africa, Latin America and the Caribbean, Central and Eastern Europe). Further expansion and update of the HWSD is foreseen for the near future, notably with the excellent databases held in the USA (Natural Resources Conservation Service US General Soil Map, STATSGO), Canada (Agriculture and AgriFood Canada: The National Soil Database NSDB), and Australia (CSIRO, ACLEP, Nnatural Heritage Trust and National Land and Water Resources Audit: ASRIS), and with the recently released SOTER database for Central Africa (FAO/ISRIC/Univ. Gent, 2007)

Harmonized World Soil Database (version 1.0)

. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries,
(b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous),
and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships.
From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

/pdf/machine-learning-and-soil-sciences-a-review-aided-by-machine-253mse1nt2.pdf

Machine learning and soil sciences: a review aided by machine learning tools

. The World Soil Information Service (WoSIS) provides
quality-assessed and standardised soil profile data to support digital soil
mapping and environmental applications at broadscale levels. Since the
release of the first “WoSIS snapshot”, in July 2016, many new soil data were
shared with us, registered in the ISRIC data repository and subsequently
standardised in accordance with the licences specified by the data
providers. Soil profile data managed in WoSIS were contributed by a wide
range of data providers; therefore, special attention was paid to measures
for soil data quality and the standardisation of soil property definitions,
soil property values (and units of measurement) and soil analytical method
descriptions. We presently consider the following soil chemical properties:
organic carbon, total carbon, total carbonate equivalent, total nitrogen,
phosphorus (extractable P, total P and P retention), soil pH, cation
exchange capacity and electrical conductivity. We also consider the following physical properties:
soil texture (sand, silt, and clay), bulk density, coarse fragments and
water retention. Both of these sets of properties are grouped according to analytical procedures that are
operationally comparable. Further, for each profile we provide the original
soil classification (FAO, WRB, USDA), version and horizon designations,
insofar as these have been specified in the source databases. Measures for
geographical accuracy (i.e. location) of the point data, as well as a first
approximation for the uncertainty associated with the operationally defined
analytical methods, are presented for possible consideration in digital soil
mapping and subsequent earth system modelling. The latest (dynamic) set of
quality-assessed and standardised data, called “wosis_latest”, is freely accessible via an OGC-compliant WFS (web feature
service). For consistent referencing, we also provide time-specific static
“snapshots”. The present snapshot (September 2019) is comprised of 196 498
geo-referenced profiles originating from 173 countries. They represent over
832 000 soil layers (or horizons) and over 5.8 million records. The actual
number of observations for each property varies (greatly) between profiles
and with depth, generally depending on the objectives of the initial
soil sampling programmes. In the coming years, we aim to fill gradually gaps
in the geographic distribution and soil property data themselves, this
subject to the sharing of a wider selection of soil profile data for so far
under-represented areas and properties by our existing and prospective
partners. Part of this work is foreseen in conjunction within the Global
Soil Information System (GloSIS) being developed by the Global Soil
Partnership (GSP). The “WoSIS snapshot – September 2019” is archived and
freely accessible at https://doi.org/10.17027/isric-wdcsoils.20190901 
(Batjes et al., 2019).

/pdf/standardised-soil-profile-data-to-support-global-mapping-and-x02esfqak0.pdf

Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019)

Digital mapping of GlobalSoilMap soil properties at a broad scale: A review

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives

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Soil legacy data rescue via GlobalSoilMap and other international and national initiatives