Remote sensing applications using Normalized Difference Vegetation Index (NDVI) to identify nitrogen management in a cotton (Gossypium hirsutum L.) field

Abstract

This study has as main objective the evaluation of different fertilization rates with the see of crop spectral reflectance. The experiment was set up in the Agioi Anargyroi village which is close to Larissa, in cotton field where we tested different fertilization mates. This paper is separated into two parts. The first part is describing the importance that cotton (Gossypiumhirsutum L.) cultivation has for Greece primary agricultural sector. Also another important part is the position and the role that the cotton plays in worldwide scale. It is mentioned also the cultural cotton characteristics for higher yield. Another important matter that is described analytically is the importance and the feature of crop reflectance sensors using vegetation indices (Vis), of which the most popular is the NDVI (Gossypiumhirsutum L) in cotton cultivation. The analysis and discussion of the research results is presented in the second part. The project was set up in the Agioi Anargyroi village which located close to Larissa, in central Greece. The study analyzed the effects of five different Nitrogen (N) fertilization rates on the cotton yield, with the use of NDVI values. The evaluation of the potential yield with the use of spectral reflectance and the productivity of the cotton field under the five fertilization treatments is the main propose of this project. The suggestion for the farmers and specially the cotton growers is to use the NDVI in order to identify the variability of the field. The results showed that with the use of the NDVI we can "predict the potential yield of the cotton crop, under several fertilization treatments. Another important effect identified with the NDVI use is the decrease of the N waste and the same time the improved Nitrogen Use Efficiency (NUE). With this experiment we trying to prove and safely state that with the use of NDVI senor farmers can identify the lack of Nitrogen which crop has.