VAWI_log: 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 VAWI_log. 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. VAWI_log consistently outperformed established indices in detecting surface water beneath vegetation, demonstrating superior detection accuracy and overall performance, with a balanced accuracy (BA) of 0.69 and a producer’s accuracy (PA) of 0.80. This reflects an average improvement of 25% over conventional methods. Benchmarking against the Dynamic World V1 dataset further confirmed the improved capability of the proposed index to detect surface water in vegetated areas, which are often missed by the dataset. This enhanced performance can be attributed to the fact that the proposed index was developed using on-the-ground observations, rather than relying solely on expert-labeled imagery as in the case of Dynamic World. Overall, VAWI_log provides a simple yet effective solution for improved surface water mapping in vegetated landscapes. Its compatibility with open-source optical satellite data enables broader applications, including irrigation monitoring and greenhouse gas assessments.