Using Partial Cloud-Free Images to Improve Spatiotemporal Fusion for Terrestrial Latent Heat Flux: The Multiphase Self-Adaptive (MSA) Model

The acquisition of a time series of high spatial resolution terrestrial latent heat flux (LE) is crucial for agricultural water resource management. However, the currently and frequently used spatiotemporal data fusion model fails to capture LE spatial and temporal patterns during periods of vigorous vegetation growth due to the limited availability of cloud-free fine-resolution images. In this study, we proposed a multiphase self-adaptive (MSA) spatiotemporal data fusion model to address this issue. Unlike popular spatiotemporal data fusion models that rely heavily on cloud-free images, MSA utilizes all available fine- and coarse-resolution images, including those with partial cloud contamination, as inputs to the model. The proposed MSA method was tested at six sites representing five major land cover types across China. The results demonstrate that the MSA model, utilizing all available Landsat images, was more accurate than that relies solely on cloud-free Landsat images [coefficient of determination (R2): 0.53 versus 0.38; root mean square error (RMSE): 8.12 versus 9.93 W/m2]. We also compared the proposed method with three widely used models, the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. The results show that MSA performed better than other models at recognizing LE spatial details. Additionally, MSA produced a high spatial resolution daily LE that was the most similar to ground-observed LE (R2 = 0.34 (p < 0.01), RMSE = 27.23 W/m2, bias = -2.75 W/m2). The proposed strategy provides an alternative approach for monitoring the high spatial resolution dynamic flux of heat and water over various land cover types.