Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria

The red-edge bands of Sentinel-2 allow for a greater diversity of spectral Vegetation Indices (VIs) to be calculated and used for vegetation characterization. We evaluated the utility of a selection of 40 VIs to derive Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and fraction of vegetation Cover (fCover) of winter wheat crop using regression method. We calibrated models for specific winter wheat development stages and compared the predictions with all-season models. The most useful VIs could be grouped into several types: (1) indices which use green and NIR band, (2) indices based on red edge bands, (3) indices which use red and NIR band and (4) the MCARI/OSAVIre index. It was found that fAPAR and fCover could be predicted with good accuracy using all-season models (rRMSE of 14% and 23% respectively), while LAI showed lower accuracy (rRMSE = 45%). The LAI model calibrated over the tillering stage was recommended for usage in the early stages of crop development. Compared with the existing methods for biophysical variables retrieval from Sentinel-2 data (i.e. the Level2B processor in SNAP) the regression approach based on VIs showed to be a viable alternative.

Automated summary

The study evaluated the utility of 40 spectral Vegetation Indices for deriving Leaf Area Index, fraction of Absorbed Photosynthetically Active Radiation, and fraction of vegetation Cover of winter wheat crop. It was found that using all-season models could accurately predict fAPAR and fCover of winter wheat crop, while the accuracy of predicting LAI was lower.