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.

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

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).

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>

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.

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).

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>

scholarly journals Spatial distribution of selected soil features in Hajdú-Bihar county represented by digital soil maps

2016 ◽  
Vol 10 (3-4) ◽  
pp. 203-213 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Katalin Takács ◽  
Gábor Szatmári ◽  
Gábor Illés ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Information Requirements ◽  
Soil Features ◽  
Soil Information ◽  
Mapping Techniques ◽  
Digital Soil Maps

With the ongoing DOSoReMI.hu project we aimed to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally general as well as specific national and international demands from the aspect of thematic, spatial and temporal accuracy. In addition to relevant and available auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened. In our paper we present some results in the form of brand new soil maps focusing on the territory of Hajdú-Bihar county.

Evaluating near infrared spectroscopy for field prediction of soil properties

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 664 ◽  
Author(s):  
Budiman Minasny ◽  
Alex B. McBratney ◽  
Leo Pichon ◽  
Wei Sun ◽  
Michael G. Short
Keyword(s):  
Soil Properties ◽  
Near Infrared ◽  
Agricultural Land ◽  
Diffuse Reflectance Spectroscopy ◽  
Soil Samples ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Conditions ◽  
Spectral Library ◽  
Field Samples

This paper demonstrates the application of near infrared diffuse reflectance spectroscopy (NIR-DRS) measurements as part of digital soil mapping. We also investigate whether calibration functions developed from a spectral library can be used for rapid characterisation of soil properties in the field. Soil samples were collected along 24 toposequences in the Pokolbin irrigation district, ~7 km2 of predominantly agricultural land in the Hunter Valley, NSW, Australia. Soil samples at 2 depths: 0–0.10 and 0.40–0.50 m were collected. The soil samples were scanned using NIR under 3 different conditions: field condition, dried unground, and dried ground. A separate spectral library containing soil laboratory measurements was used to develop functions to predict 3 main soil properties from NIR spectra (total C content, clay content, and sum of exchangeable cations). The absorbance spectra were found to be different for the 3 soil conditions. The field spectra appear to have higher absorbance, followed by dried unground samples and then dried ground samples. Although most spectral signatures or peaks were similar for the 3 soil conditions, field samples appear to have higher absorbance, particularly at 1400 nm and 1900 nm. The convex hull of the first 2 principal components of the soil spectra is an easy tool to evaluate the similarity of spectra from a calibration set to an observation. For field prediction, samples need to be calibrated using field samples. Finally, this study shows that NIR-DRS measurement is a useful part of digital soil mapping.

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