Exploring Vegetation–Water Asynchrony in a Flood–Drought-Prone Landscape: Insights from Sentinel-2 and the Limitations of Predictive Modeling

Information from the abstract

Climate change has intensified hydrological extremes, placing complex stress on terrestrial ecosystems. Quantifying the decoupling between vegetation condition and water availability—termed vegetation–water asynchrony—is critical for sustainable ecosystem monitoring, yet no standardized remote sensing indicator exists. This study introduces the Asynchrony Index (AI) to quantify vegetation–water decoupling using Sentinel-2 data in northeastern Thailand. We analyzed 106 sampling points across four years (2019, 2020, 2023, 2024), calculating AI as the absolute difference between standardized NDVI and MNDWI. Four cross-validation strategies—GroupKFold, Leave-One-Year-Out, Spatially Blocked, and Random K-Fold—were employed to assess the predictive capability of five external environmental variables. Mixed-effects models accounted for repeated measures, and spatial autocorrelation was assessed using Moran’s I and LISA. AI exhibited significant interannual variation (ANOVA: F(3, 420) = 4.83, p = 0.0026), with the highest mean in 2020 (0.849) and the lowest in 2024 (0.547). No external predictor correlated strongly with AI (all |r| < 0.17), and PCA explained 70.9% of total variance in the first two components. Mixed-effects models revealed that 57.1% and 58.2% of variance was attributable to between-site differences (ICC = 0.571 and 0.582). Drought was not a significant predictor (p = 0.140). All models yielded negative R2 in GroupKFold (−0.61 to −0.09) and Spatial Block (−0.71 to −0.56), indicating insufficient predictive information. Residual Moran’s I remained significant (I = 0.252, p = 0.001), confirming that unmeasured factors drive asynchrony. Topographic clustering revealed three landscape units (F = 4.90, p = 0.0077), but effect sizes were small (η2 = 0.012). SHAP identified TWI, prox2river, and DEM as the most influential features. This study contributes an interpretable metric for ecosystem monitoring and demonstrates the importance of honest reporting of model limitations.

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Related topics: Remote Sensing in Agriculture · Hydrology and Drought Analysis · Plant Water Relations and Carbon Dynamics