Abstract
For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).
Highlights
Introduction distributed under the terms andPrecise knowledge about the distribution of soil organic carbon (SOC) contents in agricultural soils is a valuable information for, e.g., food security issues [1] or global climate change [2]
To capture and quantify SOC contents in agricultural soils for efficient and sustainable land use, data with high spatial resolution is needed in order to understand the impacts of climate change on soil quality [12]
A few pixel clusters showed spectra (Figure 4b). These heterogenous pixel clusters with deviating individual spectra deviating spectra (Figure 4b). These heterogenous pixel clusters with deviating individual are represented by high standard deviations (STD) and need to be filtered, as here the spectra areofrepresented byinfluence high standard (STD)
Summary
Precise knowledge about the distribution of soil organic carbon (SOC) contents in agricultural soils is a valuable information for, e.g., food security issues [1] or global climate change [2]. 2021, 13, 3141 high [6,7] and balanced SOC contents are considered healthy soils [8,9] and are less prone to impacts of climate change [7]. To capture and quantify SOC contents in agricultural soils for efficient and sustainable land use, data with high spatial resolution is needed in order to understand the impacts of climate change on soil quality [12]. The European Soil Data Center (ESDAC) provides several pan-European SOC maps.
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