Dynamic Reconstruction of Vegetation Earth Observation Time Series: Beyond Gap-Filling in Level-3 Products

Level-3 (L3) Earth observation (EO) products are commonly generated through temporal compositing, smoothing, and gap-filling of Level-2 retrievals. While these procedures provide spatially and temporally continuous datasets, they rely on interpolation assumptions that cannot adequately capture high-frequency, non-linear physiological dynamics of vegetation. As a result, reconstructed time series often exhibit attenuated variability, temporal lag, and aliasing, with limited consistency with underlying ecosystem processes. This review proposes a shift from conventional gap-filling toward dynamic reconstruction, in which L3 products are interpreted as inferred trajectories of observable vegetation variables conditioned on sparse observations, complementary information, and explicit constraints. Dynamic reconstruction integrates satellite observations with complementary information, including multi-sensor EO data, meteorological drivers, spatial context, and model-based priors. The review critically synthesizes the principal methodological families for dynamic reconstruction and examines how complementary information and explicit constraints improve the reconstruction of vegetation dynamics while introducing trade-offs in uncertainty representation, physical consistency, scalability, and scale compatibility. Examples spanning physiological, environmental, and event-driven regimes illustrate how conventional L3 processing is particularly challenged by highly dynamic variables such as solar-induced chlorophyll fluorescence, evapotranspiration, land surface temperature, and stress indicators. These variables remain undersampled by satellite observations, leading to loss of short-term variability and distorted timing of rapid responses. Dynamic reconstruction provides a unifying framework for improving the representation of vegetation dynamics by explicitly accounting for observability, uncertainty, and cross-scale consistency. Overall, for highly dynamic vegetation variables, L3 products are increasingly understood as reconstructed trajectories rather than merely as gap-filled observations. This perspective may enhance the reliability and interpretability of EO-based monitoring of vegetation function and stress in an era of increasingly frequent, multi-sensor observations.