In:
SOIL, Copernicus GmbH, Vol. 8, No. 2 ( 2022-09-22), p. 587-604
Abstract:
Abstract. As the largest terrestrial carbon pool, soil organic carbon (SOC) has the
potential to influence and mitigate climate change; thus, SOC monitoring is of high importance
in the frameworks of various international treaties. Therefore, high-resolution SOC maps are required. Machine learning (ML) offers new
opportunities to develop these maps due to its ability to data mine large
datasets. The aim of this study was to apply three algorithms commonly used
in digital soil mapping – random forest (RF), boosted regression trees
(BRT), and support vector machine for regression (SVR) – on the first German
agricultural soil inventory to model the agricultural topsoil (0–30 cm) SOC
content and develop a two-model approach to address the high variability in
SOC in German agricultural soils. Model performance is often limited by the
size and quality of the soil dataset available for calibration and
validation. Therefore, the impact of enlarging the training dataset was tested
by including data from the European Land Use/Cover Area frame Survey
for agricultural sites in Germany. Nested cross-validation was implemented
for model evaluation and parameter tuning. Grid search and the differential
evolution algorithm were also applied to ensure that each algorithm was
appropriately tuned . The SOC content of the German agricultural soil
inventory was highly variable, ranging from 4 to 480 g kg−1. However, only 4 % of all soils contained more than 87 g kg−1 SOC and were considered organic or degraded organic soils. The
results showed that SVR produced the best performance, with a root-mean-square error (RMSE) of 32 g kg−1 when the algorithms were trained on the full dataset. However, the
average RMSE of all algorithms decreased by 34 % when mineral and organic
soils were modelled separately, with the best result from SVR presenting an RMSE of
21 g kg−1. The model performance was enhanced by up to 1 % for
mineral soils and by up to 2 % for organic soils. Despite the ability of machine
learning algorithms, in general, and SVR, in particular, to model SOC on a
national scale, the study showed that the most important aspect for
improving the model performance was to separate the modelling of mineral and
organic soils.
Type of Medium:
Online Resource
ISSN:
2199-398X
DOI:
10.5194/soil-8-587-2022
DOI:
10.5194/soil-8-587-2022-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2022
detail.hit.zdb_id:
2834892-8
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