Abstract:
Monitoring wheat growth under different weather and ecological conditions is vital for a reliable supply of wheat yield estimations. Remote sensing techniques have been applied in the agricultural sector for monitoring crop biophysical properties and predicting crop yields. This study explored the application of Land Surface Temperature (LST)-vegetation index relationships for winter wheat in order to determine indices that are sensitive to changes in the wheat health status. The indices were derived from Landsat 8 scenes over the wheat growing area across Bloemfontein, South Africa. The vegetation abundance indices evaluated were the Normalised Difference Vegetation Index (NDVI) and the Green Normalised Difference Vegetation Index (GNDVI). The moisture indices evaluated were the Normalised Difference Water Index (NDWI) and the Normalised Difference Moisture Index (NDMI). The results demonstrated that LST exhibited an opposing trend with the vegetation abundance indices and an analogous trend with the moisture indices. Furthermore, NDVI proved to be a better index for winter wheat abundance as compared to the GNDVI. The NDWI proved to be a better index for determining water stress in winter wheat as compared to the NDMI. These results indicate that NDVI and NDWI are very sensitive to LST. These indices can be comprehensive indicators for winter wheat health status. These pilot results prove that LST-vegetation index relationships can be used for agricultural applications with a high level of accuracy.
