POTENTIAL OF SENTINEL-2 SATELLITE AND NOVEL PROXIMAL SENSOR DATA FUSION FOR AGRICULTURAL APPLICATIONS

Considering the importance of crop production for the growing population of the world, timely and accurate information about crop development is essential for successful agricultural monitoring. With an increasing interest of the agricultural community in precision agriculture, there is also a growing interest for using different spectral vegetation indices derived by different sensor devices. They can offer a valuable perspective both at the field-scale and at the plant level. In order to better utilize the spectral reflectance measurements from different sensors for agricultural applications, as well as to promote synergistic use of proximal and remote sensing sensors in this area, this paper aims to compare two novel sensing approaches for crop monitoring; a) the recently developed active multispectral proximal sensor named Plant-O-Meter and b) Sentinel-2 satellite, which carries a multispectral optical instrument. Both sensors and sensing methods are suitable for agricultural applications, following the same basic measurement principles. In general, their operation is based on the estimation of the proportion of radiation that is reflected from the target, which in agricultural systems refers to plants or the soil, at different wavelengths of the spectrum of light. However, each of the two sensing systems shows pros and cons regarding the spatial, spectral and temporal resolutions, the need for corrections and calibrations and the dependency from external parameters such as the weather or illumination conditions. Therefore, their complementary use is expected to bring added value comparing to information retrieved by each sensor separately. In order to correctly address the problem of data fusion, compatibility studies between the two sensors (passive remote and active proximal) are necessary. In this study, a maize field was sensed on several dates in 2018 growing season using both the Plant-O-Meter active proximal sensor and images acquired by Sentinel-2. Numerous vegetation indices based on different spectral channel combinations were calculated and the results were compared using linear regression analysis. First results showed good positive correlations between the indices obtained by the two sensors which signify their joint potential, hence further development and research on this topic are appreciated and expected.