Impacts of national scale digital soil mapping programs in France

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

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Digital Soil Mapping at a National Scale: A Knowledge and GIS Based Approach to Improving Parent Material and Property Information

Digital Soil Mapping with Limited Data ◽

10.1007/978-1-4020-8592-5_14 ◽

2008 ◽

pp. 173-182 ◽

Cited By ~ 2

Author(s):

R. Lawley ◽

B. Smith

Keyword(s):

Parent Material ◽

Soil Mapping ◽

Digital Soil Mapping ◽

National Scale

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Digital Soil Mapping - An Introductory Perspective

10.1016/s0166-2481(06)x3100-8 ◽

2006 ◽

Keyword(s):

Soil Mapping ◽

Digital Soil Mapping

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

Agrokémia és Talajtan ◽

10.1556/agrokem.63.2014.1.9 ◽

2014 ◽

Vol 63 (1) ◽

pp. 79-88 ◽

Cited By ~ 3

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.

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

Remote Sensing ◽

10.3390/rs11141683 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1683 ◽

Cited By ~ 6

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.

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Digital soil mapping of arable land in Sweden – Validation of performance at multiple scales

Geoderma ◽

10.1016/j.geoderma.2017.10.049 ◽

2019 ◽

Vol 352 ◽

pp. 342-350 ◽

Cited By ~ 13

Author(s):

Kristin Piikki ◽

Mats Söderström

Keyword(s):

Multiple Scales ◽

Arable Land ◽

Soil Mapping ◽

Digital Soil Mapping

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

Geophysics ◽

10.1190/geo2012-0053.1 ◽

2012 ◽

Vol 77 (4) ◽

pp. WB201-WB211 ◽

Cited By ~ 40

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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