Abstract
Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the attenuation of the Earth's thermal emission at microwave wavelengths by the vegetation layer. At L-band, VOD is used to estimate the global biomass, a key component of the Earth's surface and of the carbon cycle. This study focuses on the behaviour of L-band VOD (L-VOD) retrieval algorithm over seasonally inundated areas, as some previous observations have shown an unexpected decline in VOD during flooding events. To analyse such variations, a passive microwave model was used to simulate the signal emitted by a mixed scene composed of soil and standing water. The retrieval over this inundated scene led to an overestimation of soil moisture (SM) and an underestimation of L-VOD. The phenomenon is more pronounced over grasslands than over forests, since low vegetation is mostly submerged under water and becomes invisible to the sensor; and since more standing water is visible to the sensor. The estimated L-VOD is typically reduced by ~10% over flooded forests and up to 100% over flooded grasslands. Such effects can distort the analysis of aboveground biomass (AGB) and aboveground carbon (AGC) dynamics based on L-VOD estimates. We evaluated that AGB can be underestimated by 15/20 Mg ha−1 in the largest seasonal wetlands, which can represent more than 50% of the actual AGB of these fields, and up to higher values during exceptional meteorological years. Consequently, to better estimate the global biomass, surface water seasonality has to be taken into account in passive microwave retrieval algorithms.
Highlights
Large-scale monitoring of vegetation cover is crucial for under standing its behaviour and its links with climate evolution, extreme events, and land cover changes (Piao et al, 2019; Qin et al, 2019)
We highlighted the anomalous decrease of Vegetation optical depth (VOD) during flooding; and we showed with a modelling experiment that this phe nomenon was linked to the influence of standing water temporal vari ations
SMOS Level2, Level3, and SMAP operational algorithms take the major water bodies into account with a static map, but this study showed the importance of considering the temporal dynamics of water extent
Summary
Large-scale monitoring of vegetation cover is crucial for under standing its behaviour and its links with climate evolution, extreme events, and land cover changes (Piao et al, 2019; Qin et al, 2019). Visible frequencies have predominantly been used for these applica tions, thanks to the high spatial resolution of optical instruments. They are impaired by their inability to penetrate clouds and dense vegetation. The low frequency L-band (1.4 GHz) VOD (L-VOD) measured with SMOS satellite was proven to be highly sensitive to aboveground biomass (AGB) in Africa, with less saturation over dense forests than optical indices and than C- or X-VOD (Rodríguez-Fernandez et al, 2018). A strong correlation was found at the global scale between L-VOD and two AGB datasets (R = 0.91–0.94), but was shown to be highly dependent on the vegetation type
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