Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates.

中文翻译：

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Sentinel-2 春季苗床光谱对土壤有机碳浓度数字映射的贡献


土壤有机碳 （SOC） 是陆地生态系统功能的核心，具有减缓气候变化的潜力，并为土壤健康提供了多种好处。了解 SOC 的空间分布有助于制定可持续的土壤管理实践。数字土壤制图 （DSM） 使用先进的统计和地统计方法来估计大面积的土壤特性。DSM 集成了气候数据、地形特征、地质、遗留土壤地图、土地管理和遥感数据。裸土光谱可能反映特定土壤成分的存在，使卫星衍生光谱成为 SOC 的合适预测因子。来自 Sentinel-2 的裸土光谱用于估计比利时北部农业地块犁层 （0-30 cm） 中的 SOC 浓度 （SOC%） 和粒度分数。此后，比较了三个 DSM 模型的 SOC% 估计性能：一个是裸土光谱，一个是环境协变量（地形、粒度和植被），以及一个是裸土光谱和环境协变量的组合模型。使用春季苗床的裸土光谱对沙子、淤泥和粘土组分的估计性能 （R2： 0.53–0.74;RPD：1.49–2.05;RPIQ： 1.52–2.39） 高于 SOC% （R2： 0.16;RPD：1.08;RPIQ：1.32）。对于包括所有协变量的 DSM 模型（R2：0.28;RPD：1.18;RPIQ： 1.44），但春季苗床光谱对包含环境协变量的模型的贡献很小。结果为使用具有光谱和环境协变量的 DSM 优化土壤特性估计提供了有价值的见解。