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• W93533991 abstract "Physically based erosion models have been widely accepted in soil erosion risk assessment in the past years but their application is still restricted due to a large disparity between required model input parameters and data availability. Hyperspectral imaging may offer an alternative to solve that problem providing high spatial and spectral information about earth surface materials. A majorproblem in image interpretation is the heterogeneous composition of each individual pixel, in the case of agricultural ecosystems a mixture of soil and vegetation signal, but also moisture conditions and illumination have a strong influence. This issue has been addressed with unmixing techniques (vii) which take individual pixel components, endmembers, into account.In this paper, we present a new algorithm which allows the derivation of synthetic spectral endmembers (SY-SEM) of soil. A systematic analysis of lab spectra from more than 100 soil samples measured at different soil moisture and illumination angles revealed stable features independent of soil moisture and illumination in SWIR I and II. Results were confirmed by an analysis of corresponding field spectra. Starting from these stable wavelength regions absolute reflectance values of all spectra were analysed adopting a linear regression analysis. Results showed coefficients of determination (R²) generally above 0.8, in the range of 1000 to 2500nm above 0.95. Based on these results, SY-SEM are derived from self-generated look-up tables containing regression parameters, and atmospherically corrected image data. In contrast to existing approaches, SY-SEM procedure is computed pixelwise over a pre-classified image data set." @default.
• W93533991 created "2016-06-24" @default.
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• W93533991 date "2007-01-01" @default.
• W93533991 modified "2023-09-27" @default.
• W93533991 title "Derivation of synthetic endmembers for linear unmixing to improve parameter estimation for soil erosion modelling in agricultural ecosystems." @default.
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