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.

植被光学深度（VOD）是一种遥感指标，其特征是植被层在微波波长下衰减了地球的热辐射。在L波段，VOD用于估算全球生物量，这是地球表面和碳循环的关键组成部分。这项研究的重点是季节性淹没区域的L波段VOD（L-VOD）检索算法的行为，因为先前的一些观察表明，洪水期间VOD意外下降。为了分析这种变化，使用了无源微波模型来模拟由土壤和积水组成的混合场景发出的信号。在这个被淹没的场景上进行的检索导致对土壤水分（SM）的高估和对L-VOD的低估。这种现象在草原上比在森林上更为明显，因为低矮的植被大多淹没在水下，对传感器来说是不可见的；并且由于传感器可以看到更多的积水。L-VOD估计值通常在淹没的森林中减少约10％，在淹没的草地中最多可减少100％。这些影响会扭曲基于L-VOD估算的地上生物量（AGB）和地上碳（AGC）动力学分析。我们评估了AGB可以低估15/20 Mg ha在最大的季节性湿地中为^-1，可以代表这些田间实际AGB的50％以上，在特殊的气象年中可以达到更高的值。因此，为了更好地估算全球生物量，在被动微波检索算法中必须考虑地表水的季节性。