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\begin{document}

\title{NDVI prediction of Mediterranean permanent grassland using soil moisture
products}



\author[1]{Filippo Milazzo }%
\author[2]{Luca Brocca }%
\author[2]{Tom Vanwalleghem }%
\author[2]{Andres Peñuela }%
\affil[1]{Universidad de Cordoba}%
\affil[2]{Affiliation not available}%


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  \date{May 23, 2024}


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\begin{abstract}
Vegetation indexes are widely used as a proxy of ~vegetation status,
they are often used to monitor and assess ~qualitatively and
quantitatively the growing season. The ~Normalized Vegetation Index
(NDVI) is the most widely used in ~agriculture, frequently as a proxy
for different physiological and ~agronomical aspects, such as drought
stress and crop yield losses ~evaluation. NDVI forecast is usually
correlated to precipitation ~however, in Mediterranean and arid
climates, it is not well ~correlated due to prolonged dry periods and
sparse precipitation ~events. In this study, we forecast Mediterranean
permanent ~grassland NDVI at 7 and 30 days ahead using machine learning
~and two soil moisture products as predictors, simulated soil ~moisture
values and satellite-based Soil Water Index (SWI) ~values. Results show
that both products can be used as reliable ~predictors of permanent
grassland in Mediterranean areas. ~Predictions at 7 days are more
accurate and better forecast the ~negative effect of drought on
vegetation dynamics than 30 days. ~This study shows the potential of
using a simple methodology and ~readily available data to predict the
grassland growth dynamic in ~the Mediterranean area .%
\end{abstract}%



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