Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications
are designed to provide farmers with timely crop monitoring and production information. Such information can be used
to identify crop vigor problems.
Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state
and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among
VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical
but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the
atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and
sun elevation influence direct comparability of vegetation indicators among different sensors.
In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of
Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively.
Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters
pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a
statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other
innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant