NDVI prediction of Mediterranean permanent grassland using soil moisture
products
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 .