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Reference data for Improved Sar-based fOresT waTer Observations (RISOTTO)



During drought, water deficit depletes tree water content, which can lead to water stress mortality, vulnerability to wildfires and increased susceptibility to insect and pathogen attacks. Widespread tree mortality could be triggered by increased drought frequency, such as expected in Europe, which can lead to disrupting the provision of important ecosystem services and increased carbon emissions. To better observe and respond to the ongoing changes in forest susceptibility to drought, a pan-European, homogeneous monitoring system of forest water status is needed. Current and upcoming Synthetic Aperture Radar (SAR) satellites offer a great opportunity to monitor forest water status due to their sensitivity to Vegetation Water Content (VWC), even at different levels in the canopy, including woody material. Furthermore, these satellites guarantee consistent and spatially explicit observations with high spatial resolution. However, the development of SAR-based forest water observables is restricted due to the limited amount, frequency and quality of ground-observed VWC. Ground observations serve as inputs for (inverse) models and AI algorithms, and, in case of satellite-based soil moisture products, are widely used for calibration/validation activities to improve these products. However, unlike networks of in situ soil moisture observations, no dedicated networks of automated VWC observations exist. This is partly due to the complexity of the measurements. This project aims to accelerate the development of SAR-based products for forest water status assessment by exploiting and developing ground-based reference data, and making these openly accessible. To do so the research will be subdivided into two main themes. The first theme is the development of a dedicated, scalable reference site with high-quality observations of forest water using different state-of-the-art ground sensors to continuously observe the water content of (1) local stem, (2) entire trunk, (3) entire canopy, and (4) the rain/dew water droplets held by the needles. These experiments will be conducted at an enclosed coniferous forest site with a flux tower, in the centre of the Netherlands. Special focus will be on long-term, automated data collection, with open accessibility.

The second theme is exploring the opportunistic use of existing data networks for cal/val activities, such as dendrometer, TreeTalker and GNSS networks. From dendrometer and TreeTalker networks, we may be able to observe spatially distributed stem water content. GNSS networks may give us spatially distributed water content of the canopy. The experiments from the first theme will give insights in the value of each observation for SAR-based VWC observables. Being able to use existing data networks for cal/val activities, and for model and AI algorithm inputs, would provide large amounts of observations to accelerate the development of SAR-based forest water products from Sentinel-1, ROSE-L and BIOMASS. Dedicated, scalable reference sites will be required for detailed understanding of SAR signals and products. Both will promote the use of SAR for forest health monitoring.


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