Global unsupervised assessment of multifrequency vegetation optical depth sensitivity to vegetation cover

Vegetation optical depth (VOD) has contributed to monitor vegetation dynamics and carbon stocks at different microwave frequencies. Nevertheless, there is a need to determine which are the appropriate frequencies to monitor different vegetation types. Also, as only a few VOD-related studies use multi-frequency approaches, it is needed to evaluate their applicability. Here, we analyze the sensitivity of VOD at three frequencies (L-, C- and X-bands) to different vegetation covers by applying a global-scale unsupervised classification of VOD. A combination of these frequencies (LCX-VOD) is also studied. Two land cover datasets are used as benchmarks and, conceptually, serve as proxies of vegetation density. Results confirm that L-VOD is appropriate for monitoring the densest canopies but, in contrast, there is a higher sensitivity of X-, C- and LCX-VOD to the vegetation cover in savannahs, shrublands and grasslands. In particular, the multi-frequency combination is the most suited to sense vegetation in savannahs. Also, our study shows a vegetation-frequency relationship which is consistent with theory: the same canopies (e.g., savannahs and some boreal forests) are classified as lighter ones at L-band due to its higher penetration (e.g., as shrublands), but labeled as denser ones at C- and X-bands due their saturation (e.g., boreal forests are labeled as tropical forests). This study complements quantitative approaches investigating the link between VOD and vegetation, extends them to different frequencies, and provides hints on which frequencies are suitable for vegetation monitoring depending on the land cover. Conclusions are informative for upcoming multi-frequency missions, such as the Copernicus Multi-frequency Image Radiometer (CIMR).
This work has been funded by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF, EU) through projects ESP2017-89463-C3-2-R, RTI2018-096765-A-100 and MDM-2016-0600, and by the project PID2020-114623RB-C32, funded by MCIN/ AEI /10.13039/501100011033. D. Chaparro has also received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/MIT19/51840001 (MIT-Spain “La Caixa” Foundation Seed Fund), and from the XXXIII Ramón Areces Postdoctoral Fellowship.
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