Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk

Monitoring of soils used for agriculture at frequent intervals is crucial to support decision making and refining soil policies especially in the context of climate change. Along with rainfall erosivity, soil coverage by vegetation or crop residues is the most dynamic factor affecting soil erosion. Parcel-specific soil coverage information can be derived by satellite imagery with high geometric resolution. However, their usable number is mostly, due to cloud cover, not representative for the phenological characteristics of vegetated classes. To overcome temporal constraints, spatial and temporal fusion models, such as STARFM, are increasingly applied to derive high-resolution time series of remotely sensed biophysical parameters, based on fine spatial/coarse temporal resolution imagery, such as Landsat, and coarse spatial/fine temporal resolution imagery, such as MODIS. In this context, the current study introduces an evaluation scheme for simulated vegetation index time series which enables the assessment of their performance during multiple phenological phases. The evaluation scheme is based on Germany-wide available spatial predictions of phenological phases as well as RapidEye imagery and parcel-specific crop-type information. The evaluation results show that the simulation accuracy is basically controlled by the temporal distance between MODIS and Landsat base pairs, as well as the ability of the actual Landsat image to properly represent the phenological phase of the Landsat image simulated by MODIS. In addition, we discuss the potential of simulated index times series and corresponding phenological information for the dynamic (1) definition of temporal windows where soils are potentially covered by no, sparse or dense vegetation or crop residues and (2) parameterization of soil erosion models. The database thus obtained opens up new possibilities for an efficient and dynamic erosion monitoring, which can support soil protection and hazard prevention