Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations

Authors: Crow, Wade; CHEN, F. - Science Systems And Applications, Inc; COLLIANDER, A. - Jet Propulsion Laboratory

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/29/2022
Publication Date: 10/20/2022
Citation: Crow, W.T., Chen, F., Colliander, A. 2022. Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations. Remote Sensing of Environment. 283. Article 113300. https://doi.org/10.1016/j.rse.2022.113300.
DOI: https://doi.org/10.1016/j.rse.2022.113300

Interpretive Summary: Remotely sensed estimates of surface soil moisture are valuable for a range of agricultural applications including drought monitoring, irrigation scheduling, and fertilizer-application management. However, the current spatial resolution of such estimates (>10 km) is too coarse to resolve individual agricultural management units – thus greatly reducing the value of downscaled soil moisture products for farm- and field-scale applications. In response, researchers have recently developed multiple approaches for downscaling soil moisture products to provide finer-resolution estimates. There exists a clear need to evaluate these approaches and identify the most promising for agricultural applications. Unfortunately, existing ground-based soil moisture networks are not suited to this task due to their low spatial density. In response, this paper describes a new downscaling validation strategy that can be applied to even very sparse ground-based soil moisture observations. As such, it greatly expands our capacity to validate, and thus improve, existing downscaled soil moisture products.

Technical Abstract: While strides have been made in their accuracy and availability, the overall utility of satellite-derived surface soil moisture (SM) datasets derived from passive microwave radiometry is still reduced by their relatively coarse spatial resolution (typically >30 km). In response to this shortcoming, many independent satellite-based SM downscaling approaches have been introduced recently. However, owing to limitations in the spatial sampling characteristics of existing SM ground-monitoring networks, it has proven difficult to obtain reliable reference SM observations at the target downscaling resolution for these approaches (typically 1 to 10 km). As a result, the objective evaluation of SM downscaling approaches is often challenging. Here, we introduce and evaluate a point-scale downscaling (PSD) benchmarking strategy whereby spatially sparse, long-term, point-scale SM observations available from existing ground-based SM networks are utilized for the objective benchmarking of downscaled satellite-based SM products. First, we define criteria that must be met for a given SM downscaling strategy to add either temporal accuracy or spatial skill relative to its coarse-resolution SM baseline. Next, we illustrate, both analytically and numerically, that such criteria can be accurately evaluated using sparse, point-scale SM observations available from existing ground-based SM networks. Finally, we apply our new PSD benchmarking approach to evaluate fine-scale SM products. Results demonstrate that the PSD approach, in concert with existing ground-based network data, can be leveraged to robustly evaluate SM downscaling approaches.