Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

Mahlatse Kganyago, Paidamwoyo Mhangara, Thomas Alexandridis, Giovanni Laneve, Georgios Ovakoglou, Nosiseko Mashiyi
2020 Remote Sensing Letters  
This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i. e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R 2 , i.e.,~0.6 to~0.7 between SNAPderived LAI and in-situ LAI, but with
more » ... igh errors, i.e., RMSE, BIAS, and MAE >2 m 2 m -2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R 2 of~0.55 and~0.8 respectively, and RMSE of~0.5 m 2 m -2 and~0.6 m 2 m -2 , respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAPderived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions. ARTICLE HISTORY
doi:10.1080/2150704x.2020.1767823 fatcat:5tmaoslzszarxbhoipmrztv6yu