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Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground-based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study, we aim at estimating the SCW by fusing information extracted from the radar imagery acquired with the Sentinel-1 constellation, aerial laser scanning, and meteorological data. To describe the change of radar backscatter with moisture, we focused on six forest stands in the H.J. Andrews experimental forest in central Oregon, as well as four clear cut areas and one golf course, over the summers of 2015–2017. We found significant relationships when we executed the analysis on radar images in which individual tree crowns were delineated from lidar, as opposed to SCW estimated from individual pixel backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. dry periods (no rain for &gt;1 h), but these effects only last for roughly 30 min after the end of a rain event. We developed a predictive model for SCW using the radar images acquired at dawn, and proved the capability of space-based radar systems to provide information for estimation of the canopy moisture under conditions of fresh rainfall during the dry season.

Estimation of Surface Canopy Water in Pacific Northwest Forests by Fusing Radar, Lidar, and Meteorological Data