Articles | Volume 21, issue 12
Hydrol. Earth Syst. Sci., 21, 6329–6343, 2017
Hydrol. Earth Syst. Sci., 21, 6329–6343, 2017

Research article 14 Dec 2017

Research article | 14 Dec 2017

Comparing soil moisture anomalies from multiple independent sources over different regions across the globe

Carmelo Cammalleri, Jürgen V. Vogt, Bernard Bisselink, and Ad de Roo Carmelo Cammalleri et al.
  • European Commission, Joint Research Centre (JRC), Ispra, Italy

Abstract. Agricultural drought events can affect large regions across the world, implying the need for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO,, the suitability of three datasets as possible representations of root zone soil moisture anomalies has been evaluated: (1) the soil moisture from the Lisflood distributed hydrological model (namely LIS), (2) the remotely sensed Land Surface Temperature data from the MODIS satellite (namely LST), and (3) the ESA Climate Change Initiative combined passive/active microwave skin soil moisture dataset (namely CCI). Due to the independency of these three datasets, the triple collocation (TC) technique has been applied, aiming at quantifying the likely error associated with each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as an assessment of the accuracy of each method. Even if no definitive statement on the spatial distribution of errors can be provided, a clear outcome of the TC analysis is the good performance of the remote sensing datasets, especially CCI, over dry regions such as Australia and southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, the results of the error analysis are used to design a weighted-average ensemble system that exploits the advantages of each dataset.

Short summary
Drought can affect large regions of the world, implying the need for a global monitoring tool. For the JRC Global Drought Observatory (GDO,, 3 soil moisture anomaly datasets have been compared, in order to evaluate their consistency. The analysis performed on five macro-regions (North America, Europe, India, southern Africa and Australia) suggests the need to combine these different data sources in order to obtain robust assessments over a variety of conditions.