Remote sensing data applied for monitoring condition of cereals

This project has been done under CEC sponsorship (contract No CIPA-CT92-3019). The objective of the project has been to use NOAA/AVHRR data to monitor vegetation and detect changes in soil water condition. In this paper only cereal crops were considered. The “cereal” pixels for each of NOAA images were deduced from supervised classification of Landsat TM images. For each of the pixel, vegetation index NDVI and soil moisture indices - H/1E and WDI were calculated. Also the weekly maximum values of NDVI has been calculated and averaged for all of the “cereal” pixels. The Leaf Area Index was calculated using vegetation index NDVI and soil moisture index (H/LE or WDI). It has been found that there is high correlation between LAI values calculated for early development stage (jointing to heading) and crop yield. LAI values are important to be used as an input to improve crop yield models which are based on meteorological data.