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Journal Article

SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates

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Vittucci, C., Ferrazzoli, P., Kerr, Y., Richaume, P., Guerriero, L., Rahmoune, R., et al. (2016). SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates. Remote Sensing of Environment, 180, 115-127. doi:10.1016/j.rse.2016.03.004.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-5362-F
This research aims to test data obtained by level 2 retrieval algorithm of SMOS over land, in order to provide
information regarding vegetation and soil moisture over forested areas. Results presented in this paperwere obtained
using the last 620 version of the algorithm.
The correlation between the new vegetation optical depth (VOD) product and the height of the forest estimated
by ICES at GLAS lidar on a global scale is investigated. Over South American and African forests a good correspondence
between the two variables is observed, with saturation occurring above about 30 m height. Moreover, the
comparison between the VOD and the height of the forest shows good spatial and temporal stability, and the r2 correlation
coefficient is within a 0.59–0.69 range. Conversely, discrepancies are observed in some Indonesian islands,
particularly New Guinea. Over specific areas, the trends vs. forest height obtained with SMOS VOD are compared
with the corresponding trends of AMSR-E VOD. Results are also validated at country-level scale. To this aim, accurate
estimates of forest biomass derived from airborne lidar over selected forests of Peru, Columbia and Panama are used.
Finally, the soil moisture retrieved over forests is investigated, reporting continental maps for Tropical areas and
comparisons with ground measurements in selected forests of the US. Continental maps obtained with the new
level 2 V620 algorithm cover almost all forest areas, and show seasonal variations which are dependent on
climatic zones. Comparisons between soil moisture retrievals in forests and ground measurements of the US
SCAN network produce worseRMSE valueswith respect to lowvegetation areas. Significant improvements however
are achieved after averaging among close nodes of the ground network.