The influence of vegetation index thresholding on EO-based assessments of exposed soil masks in Germany between 1984 and 2019

Abstract

Knowledge about the spatial and temporal distribution of exposed soils is necessary for e.g., soil erosion mitigation. Earth Observation (EO) is a valuable data source for detecting exposed soils on a large scale. In the last couple of years, the multitemporal compositing technique has been used for the generation of so-called exposed soil composites that overcome the limitation of temporarily coverage of the soils with vegetation as it is occurring at agricultural sites. The selection of exposed soil pixels from the stack of multispectral images is mainly done using spectral reflectance indices such as NDVI, NBR2 and others calculated on a per-pixel basis. The definition of the thresholds that are applicable to large areas such as regions, countries or continents is still a challenge and requires a reliable and robust sampling data base. In this study, the Soil Composite Mapping Processor (SCMaP) is used to build exposed soil masks containing all pixels in a given time period showing at least once exposed soil. For this purpose, a modified vegetation index (PV) based on the NDVI is used to separate the soils from other land cover (LC) classes by two PV thresholds. The overall goal of this study is to derive and validate exposed soil masks from multi-year Landsat data stacks for Germany from 1984 to 2019. The first focus is set on the impact of a newly developed sampling approach of LC classes such as urban areas, deciduous forests and agricultural fields that are automatically derived from Corine Land Cover (CLC) data. The spectral-temporal behavior of these LC classes in PVmin/max index composites show larger variability of the PV values compared to a manual sampling for selective LC classes such as urban areas. It reveals that the threshold definition method previously developed by Rogge et al. (2018) is not robust enough and the percentile rule used to define the Tmax threshold had to be adapted from 0.995 to 0.900. On the other hand, the sampling data base has proven to be robust across time and region. The second focus of the paper is to validate all generated exposed soil masks covering Germany for seven time periods from 1984 to 2019. A linear correlation analysis was performed comparing the SCMaP data with surveys from the Federal Statistical Office (Destatis) and the CLC inventories. The comparison with both datasets showed high regression coefficients (R2 = 0.79 to 0.90) with small regional deviations for areas in the Northern part of Germany. Strong correlation was found for time periods based on a higher number of cloud free Landsat images such as from 2000 to 2009. This demonstrates the high potential of SCMaP’s to generate exposed soil masks based on an automated sampling and a robust threshold derivation. To contribute to soil erosion studies that need information about where and when soils are bare, accurate exposed soil masks in suitable time periods can be of great value.