Comparison of Inversion Methods for Maize Canopy Time-Series LAI Based on SupReME Reconstructed Images

Abstract

HighlightsThe Sentinel-2 images are reconstructed by the SupReME algorithm to obtain rich spatial features and consistent spectral reflectance.The reconstructed images are more advantageous for LAI estimation than the original images.The PROSAIL coupled RF model is verified to be an effective method for time-series LAI estimation at 10 m spatial resolution.Abstract. Accurate time-series crop leaf area index (LAI) monitoring can provide data support for field management and early yield estimation. The Sentinel-2 satellite has a high spatial, temporal, and spectral resolution, and its unique three red-edge bands provide an ideal data source for LAI estimation. However, the inconsistent spatial resolution of different bands hinders the application potential of Sentinel-2 images. In view of this problem, we focused on mining more information provided by the high spatial resolution bands of Sentinel-2 images using the Super-Resolution for Multispectral Multiresolution Estimation (SupReME) algorithm. Furthermore, The SNAP (Sentinel Application Platform) biophysical processor and the PROSAIL radiation transfer model coupled with Random Forest (RF) model were applied to estimate time-series LAI of maize canopy at 10 m spatial resolution, and the Leaf Area Index Wireless Sensor Network (LAINet) measurements were used for accuracy verification. Finally, the effectiveness of images reconstructed by SupReME and the two inversion methods for time-series LAI estimation were evaluated. The results showed that: (1) the Sentinel-2 images reconstructed by SupReME can improve spatial characteristics while maintaining spectral invariance, and they were more advantageous for LAI estimation than the original images; (2) The SNAP biophysical processor suits a quick large-scale estimation with robustness, while the PROSAIL coupled RF model achieved a higher coefficient of determination (R2) and a lower root mean square error (RMSE) (R2 increased by more than 0.1, RMSE decreased by more than 0.33) for time-series LAI estimation in this specific study area; (3) both inversion methods showed apparent underestimation at the late growth stage. This study verifies the feasibility of obtaining high spatial resolution images using a super-resolution algorithm for LAI inversion and provides the effect of two commonly used inversion methods for time-series LAI estimation at 10 m resolution. Keywords: Leaf area index, PROSAIL model, Random forest, SNAP biophysical processor, SupReME algorithm.