Spatial variability of chlorophyll and NDVI obtained by different sensors in an experimental coffee field
¹Federal University of Lavras, School of Engineering, Department of Agricultural Engineering, Rotary Clover Professor Edmir Sá Santos, BR37200-900 Lavras, Brazil
²Agricultural Research Company of Minas Gerais, Av. José Cândido da Silveira 1647, Bairro União Belo Horizonte, BR31170-495 Belo Horizonte, Brazil
³University of Florence – UniFI, Department of Agriculture, Food, Environment and Forestry (DAGRI), Via San Bonaventura, 13, IT50145 Florence, Italy
* Correspondence: gabriel.ferraz@ufla.br
Abstract:
The objective of this research was to study the spatial variability of NDVI and chlorophyll sampled by different sensors, as well as to evaluate the correlation between them in a coffee field. The study was carried out on a coffee farm located in the municipality of Três Pontas, Minas Gerais. A sampling grid containing 30 points was created for the study area. Each sampling point was represented by one plant, which was georeferenced by a GNSS RTK. For each sample plant, NDVI and chlorophyll were obtained by the optical and active sensors GreenSeeker and ClorofiLOG, respectively. In addition, it was carried out a flight with an RPA equipped with a passive and multispectral sensor. Using the data obtained by active sensors, a geostatistical analysis was carried out to evaluate the spatial variability of NDVI and chlorophyll. The geostatistical analysis verified the existence of spatial dependence for the two attributes, and thus it was possible to generate spatialization maps through kriging. The images obtained by the passive sensor resulted in five multispectral orthomosaics, making it possible to calculate the NDVI, thus generating a spatialization map of this index. It was possible to observe in the generated maps, points that presented a certain similarity and for this purpose a correlation analysis was carried out for the values of each attribute, sampled directly in the maps, and in different sampling grids (30, 60, 90 and 120 points). By analyzing the Pearson coefficient (R) it was possible to quantify the level of correlation between the data obtained by the different sensors and through the t test it was possible to find significant correlations between them.
Key words:
active sensor, geostatistics, passive sensor, spatial distribution, spectral indices