Vegetation indices are used in precision agriculture to estimate crop aboveground biomass (AGB), and, in turn, to quantify crop needs. However, crop species and development stage affect vegetation indices limiting the setup of generalized models for AGB estimation. Some approaches to overcome this issue have combined vegetation indices and structural crop properties such as crop height. However, only a few studies have considered different herbaceous crops like forages and cover crops. A two-year field experiment was carried out on five winter cover crops with different habits at a high cover fraction (on average 93%) to study if combining vegetation indices, crop height and the fraction of soil covered by the crop could improve AGB estimation. Seven vegetation indices, crop height and cover fraction were derived from UAV-multispectral images. Species-specific and global (including all species) regression models were built and tested through cross-validation (CV). Green-based indices were the best estimators of AGB (RCV 2 = 0.56-0.93, normalized root mean square error in CV nRMSECV= 26-38%) of the five species, separately. A global linear model using crop height alone, provided good results (RCV 2 = 0.57, nRMSECV= 42%). Also, stepwise multiple regression was used to get a global model with crop height and five vegetation indices (RCV 2 = 0.75, nRMSECV= 31%). Finally, a model was proposed where AGB was estimated by a vegetation index until plants covered 97% of soil or its height was shorter than 125 mm, and by crop height for vegetation taller than 125 mm. The promising results (RCV 2 = 0.65, nRMSECV= 37%) suggested the possibility of increasing AGB estimation by considering both UAV-derived vegetation indices and structural crop properties.

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