Published works

Fire intensity, spread rate and ignition are very sensitive to the fuel dryness which in turn is strongly linked to soil moisture deficit. Though the value of soil moisture deficit in predicting fire danger has been long established, very few fire danger rating systems employ a comprehensive methodology to estimate it. Most fire danger rating systems use very simple empirical water balance models which are found to have errors. Hence they are poor drivers of the sophisticated fire models used operationally to manage and warn for dangerous fire conditions and spread. With advances in the science of measurement, in the form of satellite remote sensing, and in prediction, in the form of physically based land surface models, soil moisture can now be better analysed and predicted. Neither observations nor models give a complete picture of the soil moisture state in isolation, however. Data assimilation combines observational and model information optimally, yielding increasingly consistent and complete estimates of soil moisture. In this paper, we touch on the various operational satellite observations available. We also discuss land surface
data assimilation methods used widely in soil moisture research and operations. This report is prepared for those with very limited technical and scientific background in satellite remote sensing or data assimilation. Hence complex mathematical and physical formulations are carefully omitted. However, the problems discussed here are highly non-trivial and inter-desciplinary, with much progress made in recent decades. Hence some technicalities are unavoidable. Also, the discussion is not intended to be complete. Our intention is to highlight, especially to the emergency management community, soil moisture estimation methods that may not be well known outside the scientific community.