A global long-term (2002–2022) C-band vegetation optical depth record retrieved after merging AMSR-E, AMSR2 and WindSat
• Proposed a calibration method for merging TB observations from three sensors. • Assessed the long-term C-VOD retrieved from the merged TB. • The long-term C-VOD was found to be highly consistent over three sensors. Vegetation optical depth (VOD) based on microwave remote sensing has become an essential indicator for monitoring large-scale vegetation conditions. The relatively long-term record of satellite-based passive C-band observations offers the potential for assessing dynamic vegetation changes. Due to the limited operational lifetime of individual microwave sensors, retrieving a long-term C-band VOD (C-VOD) dataset often requires combining multiple sensors, such as Advanced Microwave Scanning Radiometer for EOS Aqua (AMSR-E, 2002–2011), Advanced Microwave Scanning Radiometer 2 (AMSR2, 2012–present) and WindSat (2003–2020). Retrieving a long-term C-VOD from their C-band brightness temperature (TB) observations faces two major challenges: (i) non-negligible systematic biases exist between the TB observations among the three sensors; (ii) TB from different sensors may not be strongly associated, especially in tropical forests with small seasonal variations. To address these challenges, we employed a combined inter-calibration method, using linear regression in sparse vegetation and linear rescaling in dense vegetation, to merge TB from three sensors. Results showed that: (i) in undisturbed dense forests where minimal emissivity variation can be assumed, the merged TB (2002–2022) exhibited strong temporal correlations with skin temperature (H polarization: R = 0.90, V polarization: R = 0.86); (ii) the merged C-VOD retrieved from the merged TB exhibited substantially improved temporal consistency across three sensors, reducing global discrepancies between AMSR-E and AMSR2 from 6.20 % to 0.34 %. Furthermore, the merged C-VOD showed stable long-term consistency across sensors, with paired t-tests indicating no significant differences (P-value > 0.01) at the global and vegetation-type scales, confirming reliable cross-sensor continuity; (iii) the merged C-VOD showed stronger temporal correlations with normalized difference vegetation index, enhanced vegetation index, and site-level gross primary production across more vegetated areas globally, and exhibited higher and more stable spatial correlations with vegetation variables across sensor transitions compared to the existing merged C-VOD product.
Authors
- Shengli Tao (ORCID: https://orcid.org/0000-0002-2145-4736)
- Dongbo Chen (ORCID: https://orcid.org/0009-0007-5594-8914)
- Yao Xiao (ORCID: https://orcid.org/0000-0001-6393-3068)
- Xiangzhuo Liu (ORCID: https://orcid.org/0000-0002-1690-7083)
- Jian Peng (ORCID: https://orcid.org/0000-0002-4071-0512)
- Gabriëlle De Lannoy (ORCID: https://orcid.org/0000-0002-6743-7122)
- Jean‐Pierre Wigneron (ORCID: https://orcid.org/0000-0001-5345-3618)
- Qiangqiang Yuan (ORCID: https://orcid.org/0000-0001-7140-2224)
- Mengjia Wang
- Philippe Ciais (ORCID: https://orcid.org/0000-0001-8560-4943)
- Frédéric Frappart (ORCID: https://orcid.org/0000-0002-4661-8274)
- Huan Wang (ORCID: https://orcid.org/0000-0002-7472-0057)
- Xiaojun Li (ORCID: https://orcid.org/0000-0001-5030-6997)
- Lei Fan (ORCID: https://orcid.org/0000-0002-6157-7167)
- Xiuzhi Chen (ORCID: https://orcid.org/0000-0003-2375-9995)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Helmholtz Centre for Environmental Research (DE)
- Southwest University (CN)
- Université de Bordeaux (FR)
- Sun Yat-sen University (CN)
- Université de Versailles Saint-Quentin-en-Yvelines (FR)
- Peking University (CN)
- Beijing Normal University (CN)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- Wuhan University (CN)
- Zhengzhou University (CN)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Biologie du Fruit et Pathologie (FR)
- Interaction Sol Plante Atmosphère (FR)
- Laboratoire des Sciences du Climat et de l'Environnement (FR)
- CEA Paris-Saclay (FR)
- Unité mixte de recherche Œnologie (FR)
- State Key Laboratory of Remote Sensing Science (CN)
- Leipzig University (DE)
- KU Leuven (BE)
Publication Details
- Journal
- International Journal of Applied Earth Observation and Geoinformation
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.jag.2025.104961
- Primary Topic
- Soil Moisture and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 0.00