with Raúl López, Andrea Toreti and Mateo Zampieri (Joint Research Centre). Work in progress.
The purpose of this research project is to advance in describing the role of remote sensing drought indicators as explanatory factors of rainfed crop yields using empirical crop models. From this research phase, an early warning drought classification system could be extracted.
Weather and Remote Sensing data
Meteorological indicators (temperature, precipitation) from weather stations: Local network of stations in the Po Valley, as the network is dense enough and available.
fAPAR from Copernicus. Indicator on crop photosynthetic activity.
Actual evapotranspiration from MODIS. This product provides global ET at 1km spatial resolution in 8-days composites. The estimation of ET is based on the energy balance approach.
Superficial soil moisture from radar imagery. It is available since 1979 up to present and the SSM is estimated through a combination of passive and active radar sensors. The spatial resolution is 0.25 degrees (approx. 25 km).
Land Surface Temperature (LST), from thermal imagery. This product is available since 1981.
Yield data. RICA dataset on yield and other agricultural variables over the Po Valley.
We will adapt the methodology of García-León et al. (2017) to this case study. An empirical crop model is estimated using meteorological and biophysical remote sensing observations
Yield anomalies will be defined as
Sensitivity of yields could be explored by looking at the Drought Environment Index (DEI)
Following Zampieri et al (2017), construct a tailor-made Combined Stress Indicator (CSI) as yield explanatory factor, using non-parametric techniques: LOESS detrending and ridge linear equations.
Given the discontinuity of farms surveillance, it is proposed to work on a longitudinal framework. Working at the strata level (economic dimension of farms) seems also interesting.