EGU23-481, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multiple information sources to characterize surface soil moisture dynamics in flood-prone rainfed agricultural areas

Lucía María Cappelletti1,2,3, Anna Sörensson1,2,3, Mercedes Salvia4, Romina Ruscica1,2,3, Pablo Spennemann5,6, Maria Elena Fernández-Long7, and Esteban Jobbágy8
Lucía María Cappelletti et al.
  • 1Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales.
  • 2CONICET – Universidad de Buenos Aires. Centro de Investigaciones del Mar y la Atmósfera (CIMA). Buenos Aires, Argentina.
  • 3CNRS – IRD – CONICET – UBA. Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI). Buenos Aires, Argentina.
  • 4Grupo de Teledetección Cuantitativa, Instituto de Astronomía y Física del Espacio (IAFE, UBA/CONICET). Buenos Aires, Argentina.
  • 5CONICET-Servicio Meteorológico Nacional. Buenos Aires, Argentina.
  • 6Universidad Nacional de Tres de Febrero (UNTREF), Buenos Aires, Argentina.
  • 7Facultad de Agronomía de la Universidad de Buenos Aires (FAUBA). Buenos Aires, Argentina.
  • 8Grupo de Estudios Ambientales, IMASL – CONICET/Universidad Nacional de San Luis. San Luis, Argentina.

Important progress has been made in recent years in characterizing surface soil moisture (SSM) dynamics at regional scales, both through remote sensing estimates and new in situ networks. Each of these databases has intrinsic features, such as the dynamic range of SSM, the temporal frequency of acquisition and the occurrence of data gaps periods. Improving the understanding of the limitations and the biases that these features can introduce in the characterization of the SSM dynamics is crucial to increase the potential and the consistency of the data sources validations. As a case study, we consider an area of the Argentinean Pampas dedicated to rainfed agro-industrial production. The region is extremely flat and has a sub-humid climate with a high seasonality of both rainfall and cropping. It is also subject to flooding and waterlogging that can last from days to months. The combination of their characteristics makes the region a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we study two types of bias. First, considering that data gaps in SSM registries are not usually taken into account in the calculation of representative statistics, we explore if these data gaps are given by spurious behaviors and their impact on SSM statistical metrics. Secondly, and taking into account the characteristics of the region, we assess the bias introduced by the placing of in situ devices on a land cover that is not representative, but which are contained in the remote sensing estimation area. As SSM satellite data we employed estimates from the SMOS and SMAP missions, in conjunction with SSM in situ data preceding from a network belonging to the Argentina National Commission for Space Activities. During the study period (2015-2019), we found a month-long gap resulting from the filtering of high SSM values in the SMAP data. These values are not spurious but typical for this flood-prone region, according to reports from national institutions and comparison with other data sources that identify high soil water content at the same period. In the case of the SMOS data, it presents a period of more than a year with very low data frequency due to radio-frequency interference. We found that ignoring the lack of SMOS data for periods on the seasonal scale, biases in simple statistics are introduced, which might cause erroneous conclusions to be drawn. We also identified that using the in situ data is not possible to represent the transition between growing and fallow seasons. Furthermore, the in situ data fail to capture waterlogging situations, which only became evident with the extensive integration of the satellite data. In this context, our study shows the importance of using multiple sources of information, avoiding taking any one source as absolute truth, with caution about the temporal and spatial biases introduced by both in situ and remote data.


How to cite: Cappelletti, L. M., Sörensson, A., Salvia, M., Ruscica, R., Spennemann, P., Fernández-Long, M. E., and Jobbágy, E.: Multiple information sources to characterize surface soil moisture dynamics in flood-prone rainfed agricultural areas, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-481,, 2023.