Key Elements of Remote Sensing Based Systems for Assessing Drought Impact on EU Agriculture

Abstract

The paper presents principle effects of drought on growth and yield performance of agricultural crops. Based on drought related phenomena a retrospective learning process is suggested using satellite images either with low or high spatial resolution. In case of low spatial resolution images, the learning process is based on (1) the appearance of the crop vegetation cover in the image, (2) yield data of major crops from agrostatistics, (3) rotation patterns of cultivated crops, (4) phenological stages of cultivated crops, (5) local distribution of soil types, and (6) regional weather conditions. Parameters 3 to 6 are classified into categories of different susceptibility to drought. Combining all parameters over time will allow to reveal both periods and areas of high drought impact on crop growth. Moreover, at the end of a learning and calibration process a quantitative measure of drought impact can be related to yield. High resolution satellite images enable a different approach based on individual farms and field sites that are representative for a major cropping area with respect to soils, climate, cropping pattern and yield. In retrospective, signatures from satellite images of identical farms or field sites have to be related to crop yield in both normal and dry years.

The operational use of remote sensing for assessing drought impact on crop growth requires the availability of satellite data with different spatial resolutions and at a reasonable price.