Surface soil water content estimation based on thermal inertia and bayesian smoothing

Soil water content plays a critical role in agro-hydrology since it regulates the rainfall partition between surface runoff and infiltration and, the energy partition between sensible and latent heat fluxes. Current thermal inertia models characterize the spatial and temporal variability of water content by assuming a sinusoidal behavior of the land surface temperature between subsequent acquisitions. Such behavior implicitly supposes clear sky during the whole interval between the thermal acquisitions; but, since this assumption is not necessarily verified even if sky is clear at the exact epoch of acquisition, the accuracy of the model may be questioned due to spatial and temporal variability of cloud coverage. During the irrigation season, cloud coverage exhibits a quite regular daily behavior, which, when rendered in probabilistic terms, allows for an a-priory evaluation of the most likely suitable pair of images to estimate thermal inertia, given the results of the satellite passes. In turn, the water content of soil is estimated through thermal inertia by coupling diurnal optical and nighttime thermal images, e.g. as acquired by MODIS sensor on board polar orbiting satellites AQUA and TERRA, which have spatial resolution high enough to cope with typical agricultural applications. The method relies on the availability of the shortwave albedo and, at least, two daily thermographs preferably acquired in specific epochs of the day: the first at sunset when latent and heat fluxes are negligible; the second just before sunrise, when surface soil temperature reaches its minimum. Unfortunately, high resolution thermal images are often not available in those specific epochs, so that the accuracy of estimate accuracy decays even severely. In this perspective the paper, following previous contributions by some of the authors of the present paper, proposes exploiting SEVIRI data, characterized by higher acquisition rate but coarser spatial resolution as available from geostationary platform, to supplement MODIS data in a twofold way: i) by allowing to verify, by means of cloud detection algorithms, the hypothesis of clear sky throughout the time; ii) by synthesizing a high spatial/high temporal resolution sequence of images, through fusion of MODIS and SEVIRI data via Bayesian smoothing. A first validation of the latter method is achieved by comparing the results with in situ micro-meteorological measurements.