Abstract
An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.
Highlights
In order to reference remote sensing data with reliable and accurate yield data, we introduced an alternative method of generating yield maps by plot yields and unmanned aerial vehicle (UAV)-based crop cover maps
UAV-based approach enables an assessment of the Sentinel-2 spectral data with regard to these requirements
A simple ratio approach based on the NIR- and Red Edge (RE)-bands performed best, enabling the calculation of stable green area index (GAI)-courses through the season
Summary
In the context of sustainable intensification of food production systems (e.g., fertilization, irrigation, pesticide application), the estimation of spatially and temporally varying crop productivity as early and precise as possible is of major concern [1]. The availability of reliable and affordable data is a key issue, whereby the required time intervals and the spatial resolution differ with regard to the main objective and the considered crop. Ground-based sensors were the most widely used option to obtain such data for precision farming [2]. Their main disadvantage is a limited area representativity, a limitation that could be overcome by aerial and satellite systems [2]
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