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

Hydrology and Earth System Sciences | Cammalleri et al. [2017]


Agricultural drought events can affect large regions across the World, implying the urge 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 modelled and/or satellite-derived proxy of soil moisture anomalies was investigated. In this study, three datasets have been evaluated as possible proxies of root zone soil moisture anomalies: (1) soil moisture from the Lisflood distributed hydrological model (LIS), (2) remotely sensed land surface temperature data from the MODIS satellite (LST), and (3) the combined passive/active microwave skin soil moisture dataset developed by ESA (CCI). Due to the independency of these three datasets, the Triple Collocation (TC) technique has been applied, aiming at quantifying the likely error associated to 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 assessment of the accuracy of each method. A clear outcome of the TC analysis is the good performance of 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, these results can be used to design an ensemble system that exploits the advantages of each dataset.

Full text can be found here.



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