Forest Flows: the integration of remote sensing and terrestrial big data to quantify forest hydrological fluxes at multiple scales

Land-use intensification and climate change are increasing pressure on water availability and use around the world. It is becoming urgent to understand hydrological cycles to manage water availability for natural and human systems. Forests cover 31% of global land area and are crucial for storing and releasing precipitation, however, it is difficult to quantify forest hydrology processes and apply the learnings from one watershed to another.   

 In New Zealand, the 5-year Forest Flows MBIE Endeavour Research Programme (https://www.scionresearch.com/science/sustainable-forest-and-land-management/Forest-flows-research-programme) investigated these challenges with the novel integration of various terrestrial and remote sensing data in Pinus radiata (D. Don) plantation forests. At total of 1,717 terrestrial sensors were deployed above and below ground in wireless IoT sensor networks across five watersheds with a range of climatic and physiographic regions. The Kafka Big Data Pipeline streamed, cleaned and stored the 360,000 observations collected every 24-hours. The fusion of temporally rich terrestrial data and spatially rich remote sensing data provided new insights into the mechanistic drivers of forest hydrological processes at the point (tree), watershed, forest scales. Forest Flows used both traditional and machine learning methodologies, as well as process-based modelling, to quantify tree water use, watershed water storage and release.

 This presentation will introduce a novel deep learning (DL) framework applied to Big Data in environmental science, with a particular focus on the DL-based Neural Ordinary Differential Equations (NODE) Hydrological Framework. This innovative approach enabled high-precision super-resolution predictions of forest soil moisture derived from NASA's Soil Moisture Active Passive (SMAP) Mission, downscaling from a 9 km to a 1 km spatial resolution. Additionally, the framework provided reliable predictions for regions lacking direct observational products. We will demonstrate how this DL methodology can be leveraged to predict evapotranspiration, as well as surface and subsurface water fluxes, at fine spatial and temporal resolutions within forest ecosystems. The potential applications of this approach extend beyond forest environments, offering insights for other complex environmental Big Data challenges.