The Combination of Uav Survey and Landsat Imagery for Monitoring of Crop Vigor in Precision Agriculture

Abstract

ABSTRACT:Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as aboveground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of aboveground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 -0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.

Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

“…The overall difference in median NDVI between the average atmospherically corrected and UAV product was 0.184 in the case of Landsat 8, and 0.229 in the case of Sentinel-2 NDVI. This is a meaningful difference for many studies, and authors tend to use UAVs as ground truth data, which is especially the case of precision agriculture [52][53][54]. Considering the different range of NIR band of tested platforms, we found the shift to the shorter wavelengths of the UAV band significant, and thus, we conclude, contrary to Ryu et al [55], the differences in measured values observed at our locality limit the possibility for using UAV as a collector of ground truth NDVI values for satellites.…”

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

“…Zarco-Tejada, González-Dugo, and Berni et al 2012focused on the calculation of fluorescence, temperature, and narrow band indices and applied these observations to the water stress detection; they also used data from a hyperspectral sensor to calculate relationships between photosynthesis and chlorophyll fluorescence (Zarco-Tejada, Catalina, González, & Martín, 2013). Lukas et al (2016) compared the basic growth parameters obtained from a fixed-wing UAV equipped with a NIR camera versus data from Landsat 8. Both platforms showed a high correlation with ground measurements of biomass and nitrogen content but the satellite data had a coarser resolution.…”

An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region

“…The utilization of unmanned aerial vehicle (UAV) images using near-infrared (NIR), greenish, red, and blue multispectral images is effectively implemented in personalized agriculture for evaluating plant growth and condition [ 9 ]. Spectral indicators like the normalization differential vegetative indices (NDVI) or greenish normalization differential vegetative indices (GNDVI) could be used to evaluate crop kind, productivity, and maturing phase [ 10 ].…”