Testing Multi-Sensors Time Series of Lai Estimates to Monitor Rice Phenology: Preliminary Results

Timely and accurate information on crop growth and seasonal dynamics are increasingly needed to develop monitoring systems aimed to detect seasonal anomalies, support site specific management and estimate crop yield at the end of the season. In particular, frequent decametric information nowadays being provided exploiting the new generation of Earth Observation (EO) platforms are fundamental for farm level monitoring. This study presents an analysis aimed at fully exploiting dense time series of EO data derived from the combined use of ESA Sentinel-2A and NASA Landsat-7/8 imageries for crop phenological monitoring. Decametric Leaf Area Index (LAI) maps were generated for the year 2016 by inversion of the PROSAIL radiative transfer model with Gaussian process regression for a study area located in northern of Italy, and used to derive rice crop stages information by exploiting an adaptation of phenological algorithm. Preliminary results showed that occurrence of the main crop development stages can be estimated at parcel level with reasonable accuracy (7 to 15 days mean error depending on the phenological phase) and phenometrics analysis provide information in agreement with the different cultivated varieties and adopted agro-management practices.