An enhanced spatiotemporal fusion method – Implications for DNN based time-series LAI estimation by using Sentinel-2 and MODIS

The consequent and accurate monitoring of the seasonal dynamics of crop leaf area index (LAI) is critical to yield estimation and agriculture policy development. It is difficult for a single sensor to balance spatial and temporal resolution. Spatiotemporal fusion is an effective way to meet the need for high spatial and temporal applications. Among the fusion methods, regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) may be recommended for vegetation dynamic monitoring because of its outperformance for the cases with considerable phenological changes. However, it is not good at capturing image structure and textures. To overcome the limitations, an enhanced version of Fit-FC, referred as Enhanced-Fit-FC (EFF), was developed. The EFF method can be applied with one (unidirectional prediction, Uni-EFF) or two (bidirectional prediction, Bi-EFF) coarse-fine image pairs as input for near real-time or post-growth applications. The visual and quantitative assessments indicated that EFF mitigated the blurring effect of Fit-FC and generated accurate reflectance, especially Bi-EFF contributed to capturing land cover changes. Compared with Fit-FC, the correlation coefficient (CC) and quality index (QI) of EFF increased by more than 0.12, and the root mean square error (RMSE) decreased by 0.16 at the maximum. Further, we identified the robustness and adaptability of deep neural network (DNN) model for time-series LAI estimation. The results substantiated the effectiveness of DNN in dealing with nonlinear problems and alleviating spectral saturation with higher CC and lower RMSE and relative RMSE (rRMSE) at whole growth-stages (CC= 0.91, RMSE= 0.28, and rRMSE= 7.18%) and vegetative stage (CC=0.94, RMSE=0.24, and rRMSE= 5.81%). In conclusion, the EFF method proposed in this study is competent in constructing time-series synthetic images for near real-time or post-growth applications. Moreover, the DNN model shows the potential for LAI estimation at whole growth-stages. This research not only contributes to dense time-series remote sensing-based applications (e.g., land cover change monitoring, yield forecast), but also is valuable for high-spatial-precision farmland management.