# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 4 Results
### Validation of the Sentinel-2 LAI Product  
The Sentinel-2 LAI showed high synchronization with the LAINet LAI during the first half of the growing season (Figure 3). However, Sentinel-2 and LAINet LAI decouple from each other around DOY 211: Sentinel-2 decreases gradually while LAINet LAI values remain high. This decoupling may result from the different definitions of LAI: Only the green elements of plant, i.e., green area index (GAI) are considered when calculating Sentinel-2 LAI [39]. On the other hand, LAINet computes the total plant area index (PAI) [38], i.e., the area index of all vegetated elements including both photosynthetic active and inactive elements. To confirm this hypothesis, the leaf chlorophyll content (CC) of the 5 km \(\times\) 5 km study area as retrieved from Sentinel-2 by SL2P was assessed (Figure 4). It clearly revealed that CC gradually increased before DOY 211 and monotonically decreased  
Figure 3: Time series of original Sentinel-2 LAI product (points) and LAINet (solid line) measurements for the six elementary sampling units (**a–f**) as indicated in Figure 1. The vertical dashed line at day of year (DOY) 211 indicates the timing of the maximum leaf development in our study area.  
after DOY 211. It implies that leaves began to turn yellow after DOY 211 and this explains why Sentinel-2 LAI and LAINet LAI decouple after then.  
Because of the definition discrepancy between the Sentinel-2 LAI product and LAINet LAI, they revealed medium consistency over the entire implementation period of LAINet (\(R^{2}=0.47\), \(RMSE=1.00\) and \(BIAS=-0.31\), Figure 5a). To give a physically sound comparison, we eliminated the LAI-pairs after DOY 211 as shown in Figure 5b. As expected, the consistence between the SL2P-based Sentinel-2 LAI and LAINet LAI was substantially improved: \(R^{2}=0.76\), \(RMSE=0.67\) and \(BIAS=0.07\), with most (>80%) of the points in the scatter lie in the GCOS uncertainty requirement (max (0.5, 20%). Our results are in accordance with the studies by [22], which also demonstrated the high accuracy of SL2P-based Sentinel-2 LAI product over crops. Since the definition of hectometric and kilometric LAI products involved in this study also corresponds to GAI, we used only the LAINet values before DOY 211 for recalibrating the Sentinel-2 LAI. The resulting recalibration regression line (y = 0.96\(\times\) + 0.21) is shown in Figure 5b.  
Figure 4: Time series of leaf chlorophyll content (CC) retrieved from Sentinel-2 by the Simplified Level 2 Product Prototype Processor (SL2P). The dashed line indicates day of year (DOY) 211, when CC began to decrease, i.e., leaf began to turn yellow in our study area.  
Figure 5: Comparison between the Sentinel-2 LAI and LAINet for (**a**) the entire time period (from day of year (DOY) 152 to 263) and (**b**) for the green-up period (from DOY 152 to 211). The black dashed line indicates 1:1 line, and solid line indicates linear regressions between Sentinel-2 LAI and LAINet. The gray dashed lines show the Global Climate Observing System uncertainty requirements for LAI (max (0.5, 20%)).  
Figure 6 shows the cross-validation results of two types of reference LAI maps. The empirically based LAI yielded an accuracy of \(R^{2}\) = 0.59, \(RMSE\) = 0.80 and \(BIAS\) = 0.04 for all sites. Approximately 55% of the points in the scatter lie in the GCOS uncertainty requirement (max (0.5, 20%)). The recalibrated SL2P-based LAI showed a better performance than the empirically based one, with \(R^{2}\) of 0.71, \(RMSE\) of 0.79 and \(BIAS\) of 0.02; most (>70%) of the points in the scatter lie in the GCOS uncertainty requirement (max (0.5, 20%)).