H002-07
Hyper-resolution land surface modeling enables hydrologically consistent 30-m SMAP-based soil moisture retrievals over continental scales

Monday, 7 December 2020: 04:24
Virtual
Noemi Vergopolan1, Nathaniel W. Chaney2, Hylke Beck1, Ming Pan1, Sara Sadri3, Justin Sheffield4 and Eric F Wood1, (1)Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States, (2)Duke University, Civil and Environmental Engineering, Durham, NC, United States, (3)Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada, (4)University of Southampton, Geography and Environment, Southampton, United Kingdom
Recorded Presentation
Abstract:
Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Microwave-based satellite remote sensing offers unique opportunities for the large-scale monitoring of soil moisture at frequent temporal intervals. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. Several downscaling techniques based on high-resolution remotely sensed data proxies have been proposed (1 km-100 m). However, by neglecting the fine-scale interactions of the landscape with current meteorological conditions, these approaches often fail to provide soil moisture estimates that are hydrologically consistent.

This work introduces a state-of-the-art framework that combines a process-based hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution brightness temperature to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). We demonstrate this framework by coupling HydroBlocks with the Soil Moisture Active Passive (SMAP) Tau-Omega RTM and subsequently merging the HydroBlocks-RTM and the SMAP L3-Enhanced brightness temperature at the HRU-scale. This allows for hydrologically consistent SMAP-based soil moisture retrievals at an unprecedented 30-m spatial resolution over continental domains.

We applied this framework to obtain 30-m SMAP-based soil moisture retrievals over the contiguous United States (2015-2019). When evaluated against sparse and dense in-situ soil moisture networks, the 30-m soil moisture retrievals showed substantial improvements in performance at field and watershed scales. This work leads the way towards hydrologically consistent field-scale soil moisture retrievals, and it highlights the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications.

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