Multitask Deep Learning Framework for Spatiotemporal Fusion of NDVI

High spatial and temporal resolution normalized difference vegetation index (NDVI) time series are indispensable for monitoring land surfaces dynamics in spatiotemporally heterogeneous areas. Spatiotemporal fusion (STF) is one of the most common methods used for producing such data. These methods require the use of one or two pairs of fine images (with fine spatial but rough temporal resolution, such as Landsat images) and coarse images [with fine temporal but rough spatial resolution, such as Moderate Resolution Imaging Spectroradiometer (MODIS)]. A coarse image at the prediction date is also required to predict the corresponding missing fine image in the time series. Recently, the proposed deep learning (DL)-based STF methods have achieved promising fusion performance but are challenged in areas with frequent cloud contamination and landcover change prediction, while they also suffer from unstable fusion performance. Moreover, current STF methods lack a quality assessment process for the fusion results. To address these limitations, in this article, we propose a multitask DL framework for STF of NDVI time series, which integrates two types of DL-based STF methods. Four experiments in two study sites were conducted to test the effectiveness of the proposed method, and the results indicate that it achieves accurate and stable fusion capable of predicting landcover changes even when image pairs obtained at long intervals are used. In addition, a fusion uncertainty estimation method is proposed, which has the potential to be used as a quality assessment metric.