Re-Evaluating Inherited SIF Anomaly Detection Methods for the 2024 Amazon Drought: A 3D Convolutional Autoencoder Approach

In the past decade, the Amazon has experienced multiple severe droughts, raising critical questions about how vegetation stress can be detected using remote sensing indicators such as Solar-Induced Chlorophyll Fluorescence (SIF). In this study, we compare two anomaly detection approaches applied to TROPOSIF data. First a classical pixel wise z-score method, and second a 3D Convolutional Autoencoder (CAE) that learns spatiotemporal SIF patterns. We evaluate both methods against two drought products, the global Standardised Precipitation-Evapotranspiration Index (SPEI) metric and Brazil's Monitor de Secas (MSB). Our results show that the CAE identifies broader, more persistent anomalies than the z-score method and aligns more closely with drought patterns detected by SPEI and MSB, particularly during the peak dry period in September 2024 and the months of delayed recovery that followed. These findings highlight how methodological choices shape the interpretation of vegetation stress and why anomaly detection methodologies should be carefully selected.