Integrating UAV and Landsat data: A two-scale approach to topsoil moisture mapping in coastal wetlands

Abstract

Surface soil moisture (SSM) is a key variable influencing ecosystem dynamics, particularly in wetland systems, highlighting its importance for management. This study integrates UAV-derived high-resolution SSM maps with Landsat-based predictions to enable multiscale SSM monitoring in wetland ecosystems. UAV multispectral and thermal imagery were used to estimate the Temperature Vegetation Dryness Index (TVDI), which was calibrated with in-situ measurements of volumetric water content percentage (VWC%) to produce fine-scale SSM maps. These maps were aggregated to train and test XGBoost models using Landsat-derived predictors.

While UAV data captured fine-scale SSM variability, Landsat-based predictions provided consistency at lower spatial scales (30 m of spatial resolution from Collection-2 Level-2), with RMSE values below 10 %. Among all surveyed periods, August yielded the most reliable results. During this month—the warmest and most hydrologically dynamic—TVDI and Land Surface Temperature (LST) emerged as the strongest predictors. This also demonstrates that XGBoost model to better represent the full range of moisture conditions.

This framework addresses challenges like cloud cover in high-latitude regions and offers scalable solutions for SSM monitoring. Results contribute to the understanding of essential climate variables and support the restoration and management of coastal meadows. By bridging UAV and satellite observations, this approach provides a reliable and scalable tool for SSM assessment across diverse ecosystems. Future efforts should prioritize surveys during ecologically responsive periods, such as August, and explore the methodology's applicability in other wetland systems and long-term monitoring schemes.