The objective of this study was the spatial identification of the NDVI index and cotton yield distributions through different crop phenological stages using geostatistical methods in Goiás state, Brazil. The experiment was carried out in a commercial field with 47.4 ha, in 80x80m georeferenced grid with 74 plots. Yield monitor data and multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates to monitor the plant vigor. Satellite images of AWiFS sensor were acquired on 08/02/2011 and 01/04/2011, during the first flowering and fruiting cotton stages, respectively, corresponding to 70 and 120DAE (days after emergence). Measures of canopy reflectance, plant height and leaf nitrogen content were determined and cotton yield was obtained by mechanical harvest in August, 2011. Data were analyzed using descriptive statistics, correlation and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between NDVI and cotton yield at 120DAE. At first flowering, the NDVI and cotton yield showed strong spatial dependence, while for 120DAE there was no dependence, probably due to the enlargement of vegetated coverage. There were similarities in the bottom left of the study area with high values of NDVI, as well as the highest values of cotton yield due to excellent plant vigor in the cotton flowering stage. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct stages of plant growth and also to predict the cotton yield.
The Interest in Unmanned Aerial Vehicles (UAVs) has grown around the world and several efforts are underway to integrate UAV operations routinely and safely into remote sensing applications, specially applied in precision agriculture. Reviewing the use of UAV in agriculture it shows limitations and opportunities. So the challenges of UAV platforms for remote sensing and precision agriculture were identified during a real case studied at a citrus area to monitor the HLB (Huanglongbing) infestation. Recommended actions for moving forward were identified and showed that is possible to use UAVs for detection of crop diseases with high precision.