How accurately does L band vegetation optical depth predict aboveground biomass?

L-band Vegetation Optical Depth (L-VOD) has emerged as a critical remote sensing proxy for monitoring global aboveground biomass (AGB) dynamics. Persistent methodological ambiguities, including the absence of standardized protocols for deriving AGB from L-VOD and the use of space-for-time assumption underpinning temporal predictions, pose challenges to the reliability of AGB estimates. In this study, we conducted a comprehensive evaluation on the current methodology using the SMOS-ICV2 L-VOD dataset and five AGB reference datasets. We find that all the existing fitting methods generally capture the AGB spatial variation, achieving 69-77 % variance explanation. Yet integrating tree cover significantly improves the AGB predictions in regions with small and medium L-VOD values. However, all methods fail to capture the spatial variation of AGB references in dense rainforests with L-VOD > 1, where the AGB reference data also show large discrepancies. Testing on space-for-time assumption reveals that spatial AGB sensitivities to L-VOD tend to be larger than temporal sensitivities. We suggest incorporating long-term in situ observations and remotely sensed vegetation structural data to understand the discrepancy between the AGB-L-VOD sensitivities and to improve AGB predictions. By providing a comprehensive evaluation of fitting methods, our results offer a cautionary tale to the use of L-VOD data to infer AGB dynamics and the necessity of developing long-term field-based biomass change datasets for further constraining and evaluating AGB predictions from remote sensing observations.