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

Abstract

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.

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Cite

BibTeX RIS

BibTeX

@article{zhang2026accurately,
  title = {{How accurately does L band vegetation optical depth predict aboveground biomass?}},
  author = {Yuan Zhang and Philippe Ciais and Jean-Pierre Wigneron and Jérôme Chave and Nan Cong and Xiaojun Li and Yan Yang and Sassan Saatchi},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2026},
  month = oct,
  doi = {10.1016/j.jag.2025.104931},
  url = {https://hal.science/hal-05838936v1},
  note = {CC BY-NC 4.0},
}

RIS

TY  - JOUR
TI  - How accurately does L band vegetation optical depth predict aboveground biomass?
AU  - Yuan Zhang
AU  - Philippe Ciais
AU  - Jean-Pierre Wigneron
AU  - Jérôme Chave
AU  - Nan Cong
AU  - Xiaojun Li
AU  - Yan Yang
AU  - Sassan Saatchi
PY  - 2026
DA  - 2026/10/26
JO  - International Journal of Applied Earth Observation and Geoinformation
DO  - 10.1016/j.jag.2025.104931
UR  - https://hal.science/hal-05838936v1
AB  - 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.
SN  - 1569-8432
SN  - 1872-826X
ER  -

Abstract

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