Information on soil clay and organic carbon content on a regional to local scale is vital for a multitude of reasons such as soil conservation, precision agriculture, and possibly also in the context of global environmental change. The objective of this study was to evaluate the potential of multi-annual hyperspectral images acquired with the HyMap sensor (450–2480？nm) during three flight campaigns in 2004, 2005, and 2008 for the prediction of clay and organic carbon content on croplands by means of partial least squares regression (PLSR). Supplementary, laboratory reflectance measurements were acquired under standardized conditions. Laboratory spectroscopy yielded prediction errors between 19.48 and 35.55？g？kg？1 for clay and 1.92 and 2.46？g？kg？1 for organic carbon. Estimation errors with HyMap image spectra ranged from 15.99 to 23.39？g？kg？1 for clay and 1.61 to 2.13？g？kg？1 for organic carbon. A comparison of parameter predictions from different years confirmed the predictive ability of the models. BRDF effects increased model errors in the overlap of neighboring flight strips up to 3 times, but an appropriated preprocessing method can mitigate these negative influences. Using multi-annual image data, soil parameter maps could be successively complemented. They are exemplarily shown providing field specific information on prediction accuracy and image data source. 1. Introduction Soil is the uppermost, weathered layer of the earth’s crust which forms the interface of the lithosphere, biosphere, hydrosphere, and atmosphere. As such, it acts as one of the major resources available to man whose conservation must be given high priority [1]. Clay and organic carbon are two key soil attributes as they contribute many benefits to its physical, chemical, and biological properties such as soil structure, soil water holding capacity, and soil fertility. Furthermore, the question whether CO2 can be sequestered in agricultural soils, to what degree and under which circumstances [2], has dramatically increased the interest in estimating soil organic carbon stocks (e.g., [3–5]). Yet, monitoring soil nutrient supply, degradation status, or organic carbon stocks requires information on soil properties over large areas with a high spatial resolution. The rate of repetition will depend on the issue considered. In any case, conventional soil sampling strategies suffer from the lack of providing data with a high spatial and temporal resolution as they are costly, labor intensive, and time consuming. As an alternative to standard analytical methods, visible and near
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