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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #330153

Research Project: Quantifying and Monitoring Nutrient Cycling, Carbon Dynamics and Soil Productivity at Field, Watershed and Regional Scales

Location: Hydrology and Remote Sensing Laboratory

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

Author
item Mollor, Markus - Martin Luther University
item Gerstmann, Henning - Martin Luther University
item Forster, Michael - Collaborator
item Dahms, Thorsten - Ludwig-Maximilians University
item Gao, Feng

Submitted to: Catena
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/7/2016
Publication Date: 11/24/2016
Citation: Mollor, M., Gerstmann, H., Forster, M., Dahms, T., Gao, F.N. 2016. Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk. Catena. 150:192-205.

Interpretive Summary: Soil erosion by water on cropland impacts environment and agricultural production. Soil erosion is determined by many factors such as soil properties, terrain, rainfall, and vegetation coverage. Monitoring of soil erosion land at frequent intervals is needed to get a proper understanding of the soil erosion process. This paper uses remote sensing data fusion approach to generate a dense time-series of vegetation index for central Germany in 2011. The fused vegetation index data were used to assess the crop- and phase-specific temporal stability. The Germany-wide predictions of phenological phases, RapidEye imagery and parcel-specific crop type information were used to evaluate the results. Understanding soil erosion process at field scale is important for cropland management and environment protection.

Technical Abstract: Monitoring of agricultural used soils at frequent intervals is needed to get a sufficient understanding of soil erosion processes. This 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 which is controlled by phenological crop development. Parcel-specific soil coverage information can be derived by satellite imagery with high temporal and 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 like STARFM are increasingly applied to derive high resolution time series of remotely sensed biophysical parameters based on high-spatial/low-temporal resolution imagery like Landsat and low-spatial/high-temporal resolution imagery like MODIS. This study introduces an evaluation scheme for simulated vegetation index time series which enables the assessment of their crop- and phase-specific temporal as well as phenological stability. 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 by their phenological representativity. Finally, we discuss the potentials for the parcel-specific monitoring of soil erosion risk. Accordingly, the coupling of simulated index times series and corresponding phenological information enables (1) the definition of temporal windows where soils are potentially covered by sparse or dense vegetation, crop residues or are free of coverage, (2) the selective application of models predicting fractional vegetation coverage, crop residue coverage or bare soils and (3) the parametrization of soil erosion models. In doing so, an efficient and dynamic erosion mapping is possible which would support soil precaution and hazard prevention.