https://orcid.org/0000-0001-7297-1170
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ABSTRACT
Soil moisture plays a key role in hydrological processes in ecosystems and regulates water and energy exchanges between the surface and the atmosphere. Global coverage of surface soil moisture (SSM) satellite estimates makes them a fundamental source of information, while the validation of these estimates is usually based on in situ measurements, ideally from networks that cover areas similar to the satellite data resolution. However, as we expose in this study, both SSM data sources face challenges over extremely flat regions with large SSM variability. A homogenous farming region in the subhumid Pampas of Argentina, characterized by a large interannual rainfall variability as well as a marked annual cycle of rainfall and cropping, was taken as a case study. The region is almost devoid of irrigation and drainage infrastructure, is subject to large episodic flood and waterlogging events and holds an in situ network belonging to the Argentinean National Commission for Space Activities. This in situ network was set to evaluate the soil moisture estimated by satellite missions, such as SMAP and SAOCOM. However, several of these sites have been placed close to homesteads in a more uniform perennial vegetation than the prevailing seasonal crop. In this work, we examine how this placement bias influences SSM dynamics and its interpretation. We find that in situ data fails to capture the large seasonal and daily SSM variability caused by the cropping dynamics as well as the situation of waterlogging. As for the satellite SSM estimates, provided by the SMOS and SMAP missions in this study, while they capture the impact of cropping on SSM, data gaps can hinder robust statistical analysis. During periods of waterlogging, SSM values can lie outside the dynamic range considered valid by satellite missions, and thus are usually removed by users, creating ‘blind spots’ for high soil water content stages in flood-prone lands. Our study underlines the importance of using multiple sources of information to interpret the hydrological status, including data from in situ measurements and remote sensing estimations of SSM as well as, when available, locally collected information such as reports from national, sub-national and private agro-industrial agencies.
Acknowledgements
The authors would like to express their special thanks to Argentina’s “Comisión Nacional de Actividades Espaciales” (CONAE) for providing the in situ soil moisture data. They also wish to point out that the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) used in this work was processed and produced by the NASA/Goddard Space Flight Center’s Global Inventory Modeling and Mapping Studies (GIMMS) Group through funding support of the Global Agricultural Monitoring project by USDA’s Foreign Agricultural Service (FAS). SMOS SM data are available at https://cp34-bec.cmima.csic.es, and SMAP SM data are available at NASA National Snow and Ice Data Center Distributed Active Archive Center, DOIs:
https://doi.org/10.5067/EVYDQ32FNWTH, https://doi.org/10.5067/T90W6VRLCBHI.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author contribution
L.M.C., A.A.S. and E.J. developed the original idea of the study, developed the analysis and wrote the text. L.M.C. produced all scripts for analysis and figures. M.S. provided preprocessed satellite data. M.S. and R.C.R. contributed to the discussions on the drydown metrics. P.S. and M.E.F.-L. provided preprocessed in situ data. All authors participated in discussing the studies and commenting on the manuscript.
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
Notes
1. To provide an estimation of soil moisture losses attributable to evapotranspiration (ET), we used as a reference of maximum possible ET in the study region, values of 1 mm1day−1 in winter and 3.5 mm1day−1 in summer (Sörensson and Ruscica Citation2018). Soil depths at which soil moisture is measured differ slightly among data sources, but as boundary approximation, we considered that all these ET losses could have come from the top 5 cm of surface soil being fully detectable by remote sensing. So, the ET rate in the top 5 cm would be: 1 mm1water×5 cm−1soil per day = 0.02 m3m−3day−1 in winter and 3.5 mm1water×5 cm−1soil per day = 0.07 m3m−3day−1 in summer.