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.

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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
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article
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article

A global long-term (2002–2022) C-band vegetation optical depth record retrieved after merging AMSR-E, AMSR2 and WindSat

Shengli Tao, Dongbo Chen, Yao Xiao, Xiangzhuo Liu et al.
International Journal of Applied Earth Observation and Geoinformation
Soil Moisture and Remote Sensing
article

A global long-term (2002–2022) C-band vegetation optical depth record retrieved after merging AMSR-E, AMSR2 and WindSat

Shengli Tao, Dongbo Chen, Yao Xiao, Xiangzhuo Liu, Jian Peng, Gabriëlle De Lannoy, Jean‐Pierre Wigneron, Qiangqiang Yuan, Mengjia Wang, Philippe Ciais, Frédéric Frappart, Huan Wang, Xiaojun Li, Lei Fan, Xiuzhi Chen
article en

Abstract

• 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.

International Journal of Applied Earth Observation and GeoinformationVol. 145
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)
Openalex Percentile: Top 67%
Soil Moisture and Remote Sensing
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