Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)

• Main Authors: Nieves Pasqualotto, Jesús Delegido, Shari Van Wittenberghe, Michele Rinaldi, José Moreno
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Sensors
• Subjects: crops; leaf area index; vegetation indices; remote sensing; Sentinel-2; red-edge
• Online Access: https://www.mdpi.com/1424-8220/19/4/904

The spatial quantification of green leaf area index (LAI_green), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAI_green index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAI_green measurements were used. Commonly used LAI_green indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R² lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAI_green, the new Sentinel-2 LAI_green Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R² of 0.708 (root mean squared error (RMSE) = 0.67) and a R² of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAI_green maps are presented.