Remote sensing of Land Surface Temperature for precision irrigation modelling: experience for different case studies.

Agriculture will progressively require more and more attention due to changing climatic conditions and increasing population, consequently threatening food security worldwide. Improving irrigation efficiency and its control on large agricultural areas has become a must for the present and next future.

Satellite data coupled with pixel-wise energy and water balance plays a relevant role in the soil moisture assessment and relative irrigation water needs for different crop and soil types.

In this framework, data from remote sensing is a potential source of information and in particular land surface temperature is nowadays extensively used in agricultural monitoring as input of energy balance models (residuals) that provide evapotranspiration estimates and so irrigation water needs.

Two main issues hinder the quality of the results from these models: (a) sub-pixel heterogeneity, in particular related to mixed crops (e.g. row and tree crops), which can be captured only partially by the available LST spatial resolution and (b) temporal frequency of the information, which for most freely-available products is usually in contrast with spatial resolution (e.g., 1 km data from MODIS is available daily, whereas 90 m data from Landsat only once every 7-8 days).

This work discusses the use of land surface temperature for calibration and validation of a pixel-wise soil-energy-water balance model and its impact on irrigation volumes for different case studies.

The discussion is carried out on several case studies characterized by different land use heterogeneity due to arboreal or crop cover and comparing satellite and ground data. LST data at different grid resolutions (10^0 to 10^2 m) are available and have been used with a corresponding spatial scheme to model the pixel soil, energy and water balances.