scholarly journals Digital soil mapping from conventional field soil observations

2013 ◽  
Vol 8 (No. 1) ◽  
pp. 13-25 ◽  
Author(s):  
J. Balkovič ◽  
Z. Rašeková ◽  
V. Hutár ◽  
J. Sobocká ◽  
R. Skalský
Keyword(s):  
Soil Profile ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Estimation Methods ◽  
Linear Regression Method ◽  
Regression Kriging ◽  
Field Methods ◽  
Leave One Out ◽  
Relative Differences ◽  
Conventional Field

We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Ri&scaron;ňovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A&ndash;D: (i) Luvic Phaeozems (Anthric), (ii)&nbsp;Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A&ndash;D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R<sup>2</sup> ranging from 23% to 47% (P &lt; 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R<sup>2</sup> = 14%, P &lt; 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A &ldquo;leave-one-out&rdquo; cross-validation was calculated for the output maps yielding R<sup>2</sup> of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P &lt; 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations.

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114253 ◽  
Author(s):  
Yakun Zhang ◽  
Wenjun Ji ◽  
Daniel D. Saurette ◽  
Tahmid Huq Easher ◽  
Hongyi Li ◽  
...  
Keyword(s):  
Soil Properties ◽  
Three Dimensional ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Regression Kriging ◽  
Field Scale

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

&lt;p&gt;One of the biggest challenges for digital soil mapping is the limited of&amp;#160;field&amp;#160;soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil&amp;#160;Partnership&amp;#160;(GSP) and the development of a &lt;strong&gt;Global Soil Information System&lt;/strong&gt;&amp;#160;(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.&amp;#160; 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.&lt;/p&gt;&lt;p&gt;The main objective of this research was the rescue, synthesis and harmonization of legacy&amp;#160;soil profile information&amp;#160;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.&lt;/p&gt;&lt;p&gt;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, &amp;#160;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.&lt;/p&gt;&lt;p&gt;The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.&lt;/p&gt;&lt;p&gt;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),&amp;#160; and the United Nations goals&amp;#160; towards increasing soil carbon sequestration areas or decreasing land desertification trends.&amp;#160; 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.&amp;#160;&lt;/p&gt;&lt;p&gt;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).&lt;/p&gt;&lt;p&gt;This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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).

Comparing traditional and digital soil mapping at a district scale using residual maximum likelihood analysis

Soil Research ◽  
2018 ◽  
Vol 56 (5) ◽  
pp. 535 ◽  
Author(s):  
E. Zare ◽  
M. F. Ahmed ◽  
R. S. Malik ◽  
R. Subasinghe ◽  
J. Huang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Soil Profile ◽  
Traditional Approach ◽  
Clay Content ◽  
Soil Mapping ◽  
Classification Systems ◽  
Digital Soil Mapping ◽  
Likelihood Method ◽  
Ancillary Data ◽  
Residual Maximum Likelihood

Conventional soil mapping uses field morphological observations to classify soil profiles into predefined classification systems and extrapolates the classified soils to make a map based on aerial photographs and the experience of the surveyor. A criticism of this approach is that the subjectivity of the surveyor leads to non-reproducible maps. Advances in computing and statistical analysis, and an increased availability of ancillary data have cumulatively led to an alternative, referred to as digital soil mapping (DSM). In this research, two agriculturally productive areas (i.e. Warren and Trangie) located in central New South Wales, Australia, were considered to evaluate whether pedoderms and soil profile classes defined according to the traditional approach can also be recognised and mapped using a DSM approach. First, we performed a fuzzy k-means analysis to look for clusters in the ancillary data, which include data from remote-sensed gamma-ray (γ-ray) spectrometry and proximal-sensed electromagnetic (EM) induction. We used the residual maximum likelihood method to evaluate the maps for various numbers of classes (k = 2–10) to minimise the mean square prediction error (σ2p,C) of soil physical (i.e. clay content, field capacity (FC), permanent wilting point (PWP) and available water content (AWC)) and chemical (pH, EC of 1 : 5 soil water extract (EC1:5) and cation exchange capacity (CEC)) properties of topsoil (0–0.3 m) and subsoil (0.6–0.9 m). In terms of prediction, the calculated σ2p,C was locally minimised for k = 8 when accounting for topsoil clay, FC, PWP, pH and CEC, and subsoil FC, EC1:5 and CEC. A comparison of σ2p,C of the traditional (seven pedoderm components) and DSM approach (k = 8) indicated that only topsoil EC1:5 and subsoil pH was better accounted for by the traditional approach, whereas topsoil clay content, and CEC and subsoil clay, EC1:5 and CEC were better resolved using the DSM approach. The produced DSM maps (e.g. k = 3, 6 and 8) also reflected the pedoderm components identified using the traditional approach. We concluded that the DSM maps with k = 8 classes reflected the soil profile classes identified within the pedoderms and that soil maps of similar accuracy could be developed from the EM data independently.

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.

scholarly journals Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

2019 ◽  
Vol 11 (14) ◽  
pp. 1683 ◽  
Author(s):  
Yangchengsi Zhang ◽  
Long Guo ◽  
Yiyun Chen ◽  
Tiezhu Shi ◽  
Mei Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Time Series Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Series Data ◽  
Landsat 8 ◽  
Auxiliary Variables ◽  
Jianghan Plain ◽  
Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB201-WB211 ◽  
Author(s):  
S. Buchanan ◽  
J. Triantafilis ◽  
I. O. A. Odeh ◽  
R. Subansinghe
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Soil Mapping ◽  
Additive Models ◽  
Digital Soil Mapping ◽  
Unbiased Estimation ◽  
Ancillary Data ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Log Ratio

The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.

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