Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites

Möller, Markus; Zepp, Simone; Wiesmeier, Martin; Gerighausen, Heike; Heiden, Uta

doi:10.3390/rs14102295

Cited by 11 publications

(3 citation statements)

References 85 publications

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“…4 . Currently, statewide or regional Digital Soil Mapping products are also being generated for Germany (e.g., Broeg et al, 2023 ; Gebauer et al, 2022 ; Möller et al, 2022 ; Sakhaee et al, 2022 ; Zepp et al, 2021 ) that may act as nationwide data bases in the future.…”

Section: Discussionmentioning

confidence: 99%

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Säurich,

Möller,

Gerighausen

2024

Environ Monit Assess

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Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day$$^{-1}$$ - 1 by 2030 and to eliminate it by 2050. About 55 ha day$$^{-1}$$ - 1 of soil are damaged (average 2018–2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day$$^{-1}$$ - 1 and 0.8 ha day$$^{-1}$$ - 1 respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany’s sustainable development strategy.

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Section: Discussionmentioning

confidence: 99%

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Säurich,

Möller,

Gerighausen

2024

Environ Monit Assess

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“…Spectral indices such as the Normalized Difference Vegetation Index (NDVI) [18], the Bare Soil Index (BSI) [15], or the Normalized Burn Ratio 2 (NBR2) [18] are calculated to differentiate between covered and exposed soil and the reflectance of each pixel is averaged over a defined period. The resulting soil reflectance composite maps can be used as covariates to model SOC and other soil properties in cropland soils [16,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

et al. 2023

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Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R2 = 0.68/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models.

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“…Instead of averaging long time series, they selected the 'greening-up' period based on a time series of normalised difference vegetation index (NDVI) values and conclude that the best SOC prediction models (R 2 = 0.54) could be obtained in the greening-up period using a strict normalised burn ratio 2 (NBR2) threshold. Möller et [7] provided a new method for increasing the spatial resolution of SOC prediction maps. They used multi-temporal soil reflectance composites (SRC) as an explanatory variable in order to generate aggregation levels on which to apply a random forest SOC prediction model.…”

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confidence: 99%