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Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning
Cristian Valeriu Patriche1, Bogdan Roşca1, Radu Gabriel Pîrnău1
1Geographic Research Center, Romanian Academy, Iaşi Branch, Iaşi, Romania.
Plos One
|August 23, 2023
Summary
Digital soil mapping in Romania effectively produced high-resolution soil property maps using geostatistical and machine learning methods. Regression-kriging and machine learning, particularly Support Vector Machines and Random Forests, showed optimal performance for predicting soil characteristics.
Area of Science:
- Soil Science
- Digital Soil Mapping (DSM)
- Geostatistics
- Machine Learning
Background:
- Digital maps of soil properties are crucial for various agricultural and soil science research applications.
- Digital Soil Mapping (DSM) techniques have advanced significantly, enabling the creation of these essential data layers.
- Romania's soil property data requires high-resolution digital mapping for improved agricultural management and research.
Purpose of the Study:
- To apply geostatistical and machine learning methods for producing high-resolution digital maps of topsoil properties in Romania.
- To evaluate the performance of different DSM techniques, including ordinary kriging, regression-kriging, and machine learning algorithms.
- To identify the optimal methods for spatial prediction of key soil chemical properties and particle-size fractions.
Main Methods:
- Employed geostatistical methods (ordinary kriging, regression-kriging, geographically weighted regression) and machine learning algorithms.
- Utilized six continuous predictors: digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude, and longitude.
- Validated methods using independent sample datasets, including LUCAS soil data and legacy soil profiles, and the 200k Romania soil map.
Main Results:
- Regression-kriging and machine learning algorithms, especially Support Vector Machines and Random Forests, were identified as optimal DSM methods.
- Geographically weighted regression performed well for predicting pH and calcium carbonates.
- Good prediction accuracy (R-squared) was achieved for pH (0.417-0.469), organic carbon (0.302-0.443), and calcium carbonates (0.300-0.330).
Conclusions:
- Machine learning and regression-kriging are highly effective for digital soil mapping in Romania.
- The LUCAS database is a reliable source for soil property data, supporting accurate spatial predictions.
- The generated digital soil maps provide valuable data for national and regional soil studies and agricultural applications.
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