@misc{schiller_derivation_of_2006, 
author={Schiller, K.},
title={Derivation of Photosynthetically Available Radiation from METEOSAT data in the German Bight with Neural Nets},
year={2006},
howpublished = {journal article},
doi = {https://doi.org/10.1007/s10236-006-0058-1},
abstract = {Two different models, a Physical Model and a Neural Net (NN), are used for the derivation of the Photosynthetically Available Radiation (PAR) from METEOSAT data in the German Bight; advantages and disadvantages of both models are discussed. The use of a NN for derivation of PAR should be preferred to the Physical Model because by construction, a NN can take the various processes determining PAR on a surface much better into account than a non-statistical model relying on averaged relations.},
note = {Online available at: \url{https://doi.org/10.1007/s10236-006-0058-1} (DOI). Schiller, K.: Derivation of Photosynthetically Available Radiation from METEOSAT data in the German Bight with Neural Nets. Ocean Dynamics. 2006. vol. 56, no. 2, 79-85. DOI: 10.1007/s10236-006-0058-1}}