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

2019 ◽  
Author(s):  
Niels H. Batjes ◽  
Eloi Ribeiro ◽  
Ad van Oostrum
Keyword(s):  
Soil Profile ◽  
Soil Property ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Total Carbon ◽  
System Modelling ◽  
Profile Data ◽  
Wide Range ◽  
Global Soil ◽  
Soil Information

Abstract. The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broad scale 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) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable. Further, for each profile, we provide the original soil classification (FAO, WRB, USDA, and 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) comprises 196,498 geo-referenced profiles originating from 173 countries. They represent over 832 thousand 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, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to fill gradually gaps in the geographic and feature space, 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).

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

2020 ◽  
Vol 12 (1) ◽  
pp. 299-320 ◽  
Author(s):  
Niels H. Batjes ◽  
Eloi Ribeiro ◽  
Ad van Oostrum
Keyword(s):  
Soil Profile ◽  
Soil Property ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Total Carbon ◽  
System Modelling ◽  
Profile Data ◽  
Wide Range ◽  
Global Soil ◽  
Soil Information

Abstract. 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).

Digital soil mapping: the challenge to obtain the best soil dataset and create a precise environmental model to support land use management at a national level (Ecuador).

2020 ◽  
Author(s):  
Daphne Armas ◽  
Mário Guevara ◽  
Fernando Bezares ◽  
Rodrigo Vargas ◽  
Pilar Durante ◽  
...  
Keyword(s):  
Land Use ◽  
Information System ◽  
Soil Properties ◽  
Soil Profile ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Land Use Management ◽  
National Scale ◽  
Global Soil ◽  
Soil Information

<p>One of the biggest challenges for digital soil mapping is the limited of field soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil Partnership (GSP) and the development of a <strong>Global Soil Information System</strong> (GloSIS), World Soil Information Service (WoSis) or SoilGrids250m for global pedometric mapping highlight new opportunities but the crescent need of new and better soil datasets across the world. Soil datasets are increasingly required for the development of soil monitoring baselines, soil protection and sustainable land use strategies, and to better understand the response of soils to global environmental change.  However, soil surveys are a very challenging task due to their high acquisition costs such data and operational complexity. The use of legacy soil data can reduce these sampling efforts.</p><p>The main objective of this research was the rescue, synthesis and harmonization of legacy soil profile information collected between 2009 and 2015 for different purposes (e.g., soil or natural resources inventory) across Ecuador. This project will support the creation of a soil information system at the national scale following international standards for archiving and sharing soil information (e.g., GPS or the GlobalSoilMap.net project). This new information could be useful to increase the accuracy of current digital soil information across the country and the future development of digital soil properties maps.</p><p>We provided an integrated framework combining multiple data analytic tools (e.g., python libraries, pandas, openpyxl or pdftools) for the automatic conversion of text in paper format (e.g., pdf, jpg) legacy soil information, as much the qualitative soil description as analytical data,  to usable digital soil mapping inputs (e.g., spatial datasets) across Ecuador. For the conversion, we used text data mining techniques to automatically extract the information. We based on regular expressions using consecutive sequences algorithms of common patterns not only to search for terms, but also relationships between terms. Following this approach, we rescued information of 13.696 profiles in .pdf, .jpg format and compiled a database consisting of 10 soil-related variables.</p><p>The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.</p><p>As a result, we substantially improved the representation of soil information in Ecuador that can be used to support current soil information initiatives such as the WoSis, Batjes et al. 2019, with only 94 pedons available for Ecuador, the Latin American Soil Information System (SISLAC, http://54.229.242.119/sislac/es),  and the United Nations goals  towards increasing soil carbon sequestration areas or decreasing land desertification trends.  In our database there are almost 13.696 soil profiles at the national scale, with soil-related (e.g., depth, organic carbon, salinity, texture) with positive implications for digital soil properties mapping. </p><p>With this work we increased opportunities for digital soil mapping across Ecuador. This contribution could be used to generate spatial indicators of land degradation at a national scale (e.g., salinity, erosion).</p><p>This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.</p><p> </p>

Application of legacy soil data in digital soil mapping for the elaboration of novel, countrywide maps of soil conditions

2014 ◽  
Vol 63 (1) ◽  
pp. 79-88 ◽  
Author(s):  
László Pásztor ◽  
E. Dobos ◽  
G. Szatmári ◽  
A. Laborczi ◽  
K. Takács ◽  
...  
Keyword(s):  
Object Oriented ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Conditions ◽  
Related Information ◽  
Soil Information

The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized digital soil mapping (DSM) products heavily based on legacy soil data, which still represent a valuable treasure of soil information at the present time. The paper presents three approaches for the application of Hungarian legacy soil data in object oriented digital soil mapping.

Measurement error-filtered machine learning in digital soil mapping

2021 ◽  
Author(s):  
Stephan van der Westhuizen ◽  
Gerard Heuvelink ◽  
David Hofmeyr
Keyword(s):  
Machine Learning ◽  
Measurement Error ◽  
Soil Property ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Simulation Studies ◽  
True Value ◽  
Linear Relationships ◽  
The Difference ◽  
The Relationship

<p>Digital soil mapping (DSM) may be defined as the use of a statistical model to quantify the relationship between a certain observed soil property at various geographic locations, and a collection of environmental covariates, and then using this relationship to predict the soil property at locations where the property was not measured. It is also important to quantify the uncertainty with regards to prediction of these soil maps. An important source of uncertainty in DSM is measurement error which is considered as the difference between a measured and true value of a soil property.</p><p>The use of machine learning (ML) models such as random forests (RF) has become a popular trend in DSM. This is because ML models tend to be capable of accommodating highly non-linear relationships between the soil property and covariates. However, it is not clear how to incorporate measurement error into ML models. In this presentation we will discuss how to incorporate measurement error into some popular ML models, starting with incorporating weights into the objective function of ML models that implicitly assume a Gaussian error. We will discuss the effect that these modifications have on prediction accuracy, with reference to simulation studies.</p>

IRAKA: The first Colombian soil information system with digital soil mapping products

CATENA ◽  
2021 ◽  
Vol 196 ◽  
pp. 104940
Author(s):  
Gustavo A. Araujo-Carrillo ◽  
Viviana Marcela Varón-Ramírez ◽  
Camilo Ignacio Jaramillo-Barrios ◽  
Jhon M. Estupiñan-Casallas ◽  
Elías Alexander Silva-Arero ◽  
...  
Keyword(s):  
Information System ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Information

Comparison of uncertainty quantification methods on the example of soil organic carbon stock mapping in Hungary

2020 ◽  
Author(s):  
Gábor Szatmári ◽  
László Pásztor
Keyword(s):  
Decision Making ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Uncertainty Quantification ◽  
Carbon Stock ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Organic Carbon Stock ◽  
Wide Range ◽  
Organic Carbon Stock

<p>Digital soil mapping (DSM) aims to provide spatial soil information for a wide range of studies (e.g. agro-environmental management, nature conservation, rural development, water and food security etc.). For this purpose, advanced statistical methods are in use for inferring the spatial variations of soil. Nowadays, there is a heap of evidences that researchers and stakeholders are not just interested in the maps of soil properties, functions and/or services but in their uncertainties as well. This is indispensable to support decision making process. In DSM various uncertainty quantification methods are in use, however, only a few studies have addressed the issue of comparing them. In this study, we compared the suitability of several commonly applied digital soil mapping methods to quantify uncertainty with regard to a survey of soil organic carbon stock in Hungary. To fairly represent the wide range of DSM methods, the followings were selected: universal kriging (UK), sequential Gaussian simulation (SGS), random forest plus kriging (RFK) and quantile regression forest (QRF). For RFK two uncertainty quantification methods were adopted based on kriging variance (RFK-1) and bootstrapping (RFK-2). We used a control dataset consisting of 200 independent SOC stock observations for validating not just the spatial predictions but their uncertainty quantifications as well. For validating the uncertainty quantifications we applied accuracy plots (a.k.a. prediction interval coverage probability plots) and a modified version of G-statistics. According to our results, QRF and SGS provided the best quantifications of uncertainty. UK and RFK-2 overestimated whereas RFK-1 underestimated the uncertainty. Based on our results we could draw a conclusion that there is a need to validate the uncertainty quantifications before using them for decision making. Furthermore, special attention should be paid to the assumptions made in uncertainty quantification.</p><p> </p><p>Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: KH126725) and the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).</p>

scholarly journals Mapping of Soils and Land-Related Environmental Attributes in France: Analysis of End-Users’ Needs

2019 ◽  
Vol 11 (10) ◽  
pp. 2940 ◽  
Author(s):  
Anne C. Richer-de-Forges ◽  
Dominique Arrouays ◽  
Marion Bardy ◽  
Antonio Bispo ◽  
Philippe Lagacherie ◽  
...  
Keyword(s):  
End Users ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
User Needs ◽  
Environmental Attributes ◽  
Uncertainty Estimates ◽  
Soil Information ◽  
The Cost ◽  
Systematic Program ◽  
Mapping Program

The 1:250,000 soil mapping program of France is nearly complete. Although mapping has been conducted using conventional methods, there is a discernible need to obtain more precise soil data using other methods, and this is attracting considerable attention. However, it is currently not possible to implement a conventional and systematic program throughout the French territory, as the cost of acquiring new data on a finer scale is too high. In light of this, the Ministry of Agriculture commissioned a national survey to determine the needs of soil mapping in France, which asked questions to soil data producers and end-users. The results presented here summarize the main needs expressed by end-users. The main topics covered by soil mapping applications are identified in addition to the main mismatches between topics currently covered using available soil maps and the needs of end-users. Certain priorities for producing new soil information are identified in relation to geographical environments and settings, soil attributes, the spatial resolution of maps and the use of uncertainty estimates. Digital Soil Mapping is identified as a method that can bridge economic, scientific and practical considerations, but it requires dedicated efforts in order to build capacity. Finally, there is discussion of how the consideration of user needs can be employed to enhance the contribution of a new Digital Soil Mapping era, and to launch an operational soil security paradigm in France.

Semi-supervised learning for increasing transferability of machine learning in digital soil mapping 

2021 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Razieh Sheikhpour ◽  
Norair Toomanian ◽  
Thomas Scholten
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Science ◽  
Target Area ◽  
Reference Area ◽  
Environmental Covariates ◽  
Soil Information

<p>The most critical aspect of application of digital soil mapping is its limited transferability. Modelling soil properties for regions where no or only sparse soil information is available is highly uncertain, when using the low-cost geo-spatial environmental covariates alone. To overcome this drawback, transfer learning has been introduced in different environmental sciences, including soil science. The general idea behind extrapolation of soil information with transfer learning in soil science is that the target area to transfer to is alike, e.g. in terms of soil-forming factors, and the same machine learning rules can be applied. Supervised machine learning, so far, has been used to transfer the soil information from the reference to the target areas with very similar environmental characteristics between both. Hence, it is unclear how machine learning can perform for other target regions with different environmental characteristics. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data (reference area) with a large amount of unlabeled data (target area) during training. In this study, we explored if semi-supervised learning could improve the transferability of digital soil mapping relative to supervised learning methods. Soil data for two arid regions and associated environmental covariates were obtained. Semi-supervised learning and supervised learning models were trained based on the data in the reference area and then tested based on the data in the target area. The results of this study indicated the higher power of semi-supervised learning for transferring soil information from one area to another in comparison to the supervised learning method.   </p>

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