VAWIlog: A Log-Transformed LSWI–EVI Index for Improved Surface Water Mapping in Agricultural Environments

Detecting surface water beneath vegetation canopies remains a major challenge for widely used water indices, which often underestimate water obscured by vegetation. This limitation is further compounded by the scarcity of reliable in-situ data needed for robust index development and validation. To address this, we introduce a Vegetation-Adjusted Water Index using a logarithmic transformation of the ratio between the Land Surface Water Index (LSWI) and the Enhanced Vegetation Index (EVI), referred to as VAWIlog. This transformation compresses high vegetation values while expanding the range typical of water surfaces, enhancing contrast in mixed land cover areas and improving class separability. The index was developed and validated using in-situ water level measurements, providing a strong empirical basis for detecting surface water under variable vegetation conditions. VAWIlog consistently outperformed established indices for detecting open water, wetlands, and flooded vegetation, demonstrating superior accuracy and overall detection performance. Evaluation against the Dynamic World V1 dataset confirmed its enhanced ability to identify water under vegetation, though some limitations remain in dense forest and open water contexts. Overall, VAWIlog offers a simple yet effective solution for improved surface water mapping in vegetated landscapes. Its compatibility with open-source optical satellite data supports broader applications in irrigation monitoring, and greenhouse gas assessments.