Canopy chlorophyll content and LAI estimation from Sentine1-2: vegetation indices and Sentine1-2 Leve1-2A automatic products comparison

The aim of this work is to analyze different methodologies for the estimation of leaf area index (LAI) and canopy chlorophyll content (CCC), using the Sentine1-2 satellite. LAI and CCC are biophysical parameters indicator of crop health state and fundamental in the productivity prediction. The purpose is to define the most optimal LAI and CCC estimation method for operational use in the monitoring of agricultural areas. Moreover, the CCC and LAI automatic products obtained directly through the Sentinel Application Platform Software (SNAP) biophysical processor and Sentine1-2 images by means of an artificial neural network (ANN) are validated. On the other hand, common vegetation indices used to LAI and CCC retrieval are analyzed. Both methods were tested using a dataset composed of LAI and CCC in situ data, obtained in an agricultural area near Caserta (Italy). As a result, Sentine1-2 automatic products present good statistics for LAI $(\mathrm{R}^{2}=0.86$, RMSE $=0.80$) and CCC ($\mathrm{R}^{2}=0.85$, RMSE $=0.68\mathrm{g}/\mathrm{m}^{2}$), without producing saturation at high LAI values. On the other hand, the best index for LAI retrieval was the normalized SeLI index $(\mathrm{R}^{2} = 0.81$, RMSE $= 0.87$) and for CCC, the three-band TCARI index ($\mathrm{R}^{2} = 0.81$, RMSE $= 0.61 \mathrm{g}/\mathrm{m}^{2}$). But the SeLI index produces a saturation process with LAI values higher than 3.5. The main conclusion of this study, hence, is that Sentine1-2 Level 2A products, such as the LAI and CCC parameter, have great potential to be used automatically and operationally in agricultural studies, minimizing time and economic costs.