LLM4HRS: LLM-Based Spatiotemporal Imputation Model for Highly Sparse Remote Sensing Data

Remote sensing data are of considerable significance for monitoring global climate, detecting harmful algae bloom, and so on. However, due to sensor failures, cloud cover, and thick aerosols, the collected remote sensing data for various ocean factors, such as chlorophyll-a (Chl-a) concentration and sea surface temperature (SST), often have a high missing rate, which seriously hinders their applications. Existing data imputation models mostly ignore highly sparse spatial locations or perform badly when the data missing rate is high due to the lack of available information. Large language models (LLMs) possess powerful representation learning capabilities and can effectively capture sequential correlations even with extremely limited information, thereby presenting the promising potential for highly sparse remote sensing data imputation. Therefore, we proposed a novel LLM-based spatiotemporal model for highly sparse remote sensing data imputation, i.e., LLM4HRS. First, LLM4HRS develops an LLM-based bidirectional temporal representation learning module to learn forward and backward temporal dependencies in data sequences and fuses them together to obtain comprehensive temporal representations. Next, LLM4HRS constructs a LLM-based spatial representation learning module to learn spatial correlations with the learned temporal representation. Finally, a spatiotemporal representation fusion and data imputation module is developed to achieve data imputation. Experiments on the SST and Chl-a remote sensing datasets demonstrate that LLM4HRS significantly outperforms existing data imputation models, with its advantages becoming more pronounced as the masking rate increases. Furthermore, when extended to the remote sensing PAR data in a large region, LLM4HRS still achieves the best performance, further validating its broad applicability for remote sensing data imputation. The code of LLM4HRS is publicly available at https://github.com/ssyuwang/LLM4HRS-master.