From theory to hydrological practice: Leveraging CYGNSS data over seven years for advanced soil moisture monitoring

Authors: KIM, H - Gwangju Institute Of Science And Technology; HAI, N - Gwangju Institute Of Science And Technology; Crow, Wade; WIGNERON, J - Inrae; YUEH, S - California Institute Of Technology; COLLIANDER, A - California Institute Of Technology; LEI, F - Mississippi State University; WAGNER, W - Vienna University Of Technology

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/5/2024
Publication Date: 11/16/2024
Citation: Kim, H., Hai, N., Crow, W.T., Wigneron, J., Yueh, S., Colliander, A., Lei, F., Wagner, W. 2024. From theory to hydrological practice: Leveraging CYGNSS data over seven years for advanced soil moisture monitoring. Remote Sensing of Environment. 316. Article e114509. https://doi.org/10.1016/j.rse.2024.114509.
DOI: https://doi.org/10.1016/j.rse.2024.114509

Interpretive Summary: Remotely sensed soil moisture products are valuable for a range of important agricultural applications including drought monitoring, irrigation scheduling, and numerical weather prediction. However, the satellite instrumentation required to generate these products is generally expensive and difficult to maintain. As a result, there has been recent interest in the development of soil moisture remote sensing capabilities that leverage off existing investments in global positioning system (GPS) satellites. Such systems send signals from a constellation of satellites that bounce off the land surface. These reflected signals can then be measured - by a second set of satellites - and processed to make estimates of soil moisture availability in near-surface soil. This measurement principle is the basis of the NASA Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation mission. This review paper discusses the physical principles behind the measurement of soil moisture using the CYGNSS mission and describes how CYGNSS-based soil moisture products can be enhanced in the future. In this way, it advances the ancillary use of GPS satellite systems for important water resources and agricultural applications.

Technical Abstract: This review comprehensively evaluates the application of the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation for soil moisture (SM) retrieval. CYGNSS, primarily designed for atmospheric studies, has been adeptly repurposed to enhance SM monitoring, exploiting its high revisiting frequency to capture dynamic moisture changes across various landscapes. Here we describe CYGNSS’s unique strengths in providing frequent observations, particularly at mid-latitudes, which is critical for accurate hydrological modeling, natural hazard mitigation, and agricultural management. Despite its potential, CYGNSS faces challenges stemming from its initial design objectives and operational constraints. These include variations in spatial resolution and the complexity of integrating its data with traditional SM retrieval methodologies, which often rely on supplementary data for algorithm calibration. The review delineates the complexities of adapting CYGNSS for terrestrial SM applications, highlighting the influence of land cover diversity and topographical variations on measurement accuracy. Past work with CYGNSS data has facilitated a deeper understanding of these issues, leading to the development of refined algorithms and processing techniques aimed at overcoming its limitations. We review this work and emphasize the need for advanced calibration strategies and the integration of CYGNSS measurements with data from other satellite systems to achieve a more comprehensive and accurate global SM monitoring network. This synthesis not only reflects on CYGNSS’s current capabilities and limitations in SM detection but also proposes future research directions to enhance the spatial resolution and temporal accuracy of its data. Future advancements are envisioned to include the development of integrated approaches that leverage machine learning algorithms and deep learning frameworks to interpret complex data patterns more effectively, thereby improving SM retrievals at finer spatial scales. Additionally, the potential for merging CYGNSS data with inputs from other high-resolution remote sensing technologies is discussed, which would enable a more detailed assessment of SM dynamics and enhance the system's utility in environmental monitoring. Moreover, we highlight the necessity for innovative data assimilation techniques that can seamlessly integrate CYGNSS measurements into existing hydrological models, thus enriching the models’ predictive capabilities. Such integration is crucial for advancing our understanding of the hydrologic cycle and for improving responses to climatic variations. Finally, ongoing efforts to refine the algorithms for SM retrieval are emphasized, aiming to reduce dependency on external ancillary data and increase the autonomy of the CYGNSS measurements. By capitalizing on the extensive dataset provided by CYGNSS, coupled with advanced analytical methodologies, researchers can significantly enhance the accuracy and applicability of SM monitoring globally. This concerted approach will not only bridge the gaps identified in current methodologies but also expand the operational boundaries of CYGNSS, facilitating its integration into broader agricultural management and meteorological forecasting frameworks.