Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

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).

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

“…Group "i" was the most represented, while group "ii" is just emerging [55]. Group "iii", referred to as a "bottom up" approach by Castaldi et al [108,109], was developed at the regional scale (e.g., in a loam belt region in Belgium and in the Gutland-Oesling region in Luxembourg [48] and Bavaria (Germany) [74]), at the national scale (e.g., Spain [75]), and then at the supranational, continental scales (e.g., European scale [57,76]) using the LUCAS alone [57,75,76] or in combination with a local database [74,77] and also at the scale of Canada using the CanSYS National Pedon Database [78]. As they are directly derived from the spectral features, we focus on the first three groups.…”

“…They found that DWT was the optimal technique for denoising spectra prior to predicting soil organic matter. For multispectral satellite images, no pretreatment was carried out except for the spatial filtering of Landsat 5 spectra [74,78]. The centering of a Sentinel-2 reflectance spectrum was preferred, but in the case of a single-date approach only [49].…”

“…However, most studies have applied multivariate calibration techniques or machine learning algorithms to extract the relevant part of the information for large datasets. Amongst the several multivariate algorithms, partial least squares regression (PLSR) [25,26,31,35,[48][49][50]57,58,74] is most widely used and offers the advantage of enabling the understanding of the influence of spectral bands on the SOC prediction. PLSR also has the advantage of being less prone to overfitting when using small sample sets.…”

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

“…Sensing System (GEOS3), which built a soil image on the multi-petabyte catalog of satell imagery with planetary-scale cloud processing architecture of the Google Earth Engi [39]. The second example is the Soil Composite Mapping Processor (ScMAP) that del ered exposed soil masks that were run on high-performance local computing clusters [4 Leveraging the application of current multispectral EO data for mapping croplan soils several applications have been recorded on the regional [43][44][45], national [46], an continental scale [30]. A significant number of these studies were implemented in Bra [47], India [48], Indonesia [49], and China [50] since detailed information about soils abundant in those countries.…”

“…Leveraging the application of current multispectral EO data for mapping cropland soils several applications have been recorded on the regional [43][44][45], national [46], and continental scale [30]. A significant number of these studies were implemented in Brazil [47], India [48], Indonesia [49], and China [50] since detailed information about soils is abundant in those countries.…”