LIU, Xiangzhuo; WIGNERON, J.-P.; FRAPPART, Frederic; BAGHDADI, Nicolas; ZRIBI, Mehrez; JAGDHUBER, Thomas; CIAIS, Philippe; LI, Xiaojun; WANG, Mengjia; FAN, Lei; YGORRA, Bertrand; MA, Hongliang; XING, Zanpin; AL-YAARI, A.; FERNANDEZ-MORAN, Roberto; MOISY, Christophe

doi:10.1109/IGARSS47720.2021.9553244

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LIU, Xiangzhuo
Interactions Sol Plante Atmosphère [UMR ISPA]

WIGNERON, J.-P.
Interactions Sol Plante Atmosphère [UMR ISPA]

FRAPPART, Frederic
Laboratoire d'études en Géophysique et océanographie spatiales [LEGOS]

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IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, 2021-07-11, Brussels. p. 6403-6406

IEEE

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Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass (R=0.74, RMSE=44.82 Mg ha−1) , which is better than V16 VOD (R=0.64, RMSE=51.27 Mgha−1) and VODCA VOD (R=0.72, RMSE=47.14 Mg ha−1) . Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.< Réduire

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Biomedical optical imaging

Radar Measurement

vegetation mapping

C-band

prediction algorithm

biomass

optical sensor

remote sensing

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First Retrievals of ASCAT IB VOD (Vegetation Optical Depth) at Global Scale

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