Synergistic retrievals of leaf area index and soil moisture from Sentinel-1 and Sentinel-2

Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613, Pinnington, E. M., Marzhan, P., Kaminski, T., Vossbeck, M., Timmermans, J., Isola, C., Rommen, B. and Loew, A. (2023) Synergistic retrievals of leaf area index and soil moisture from Sentinel-1 and Sentinel-2. International Journal of Image and Data Fusion, 14 (3). pp. 225-242. ISSN 1947-9824 doi: 10.1080/19479832.2022.2149629

Abstract/Summary

Joint retrieval of vegetation status from synthetic aperture radar (SAR) and optical data holds much promise due to the complimentary of the information in the two wavelength domains. SAR penetrates the canopy and includes information about the water status of the soil and vegetation, whereas optical data contains information about the amount and health of leaves. However, due to inherent complexities of combining these data sources there has been relatively little progress in joint retrieval of information over vegetation canopies. In this study, data from Sentinel–1 and Sentinel–2 were used to invert coupled radiative transfer models to provide synergistic retrievals of leaf area index and soil moisture. Results for leaf area are excellent and enhanced by the use of both data sources (RSME is always less than 0.5 and has a correlation of better than 0.95 when using both together), but results for soil moisture are mixed with joint retrievals generally showing the lowest RMSE but underestimating the variability of the field data. Examples of such synergistic retrieval of plant properties from optical and SAR data using physically based radiative transfer models are uncommon in the literature, but these results highlight the potential for this approach.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/109053
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Taylor & Francis
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