UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras

Abstract

Abstract Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown great potential for precision agriculture. However, there are many problems in data acquisition, processing and application, which have stunted its development. In this study, a narrowband Mini-MCA6 multispectral camera and a sunshine-sensor-equipped broadband Sequoia multispectral camera were mounted on a multirotor micro-UAV. They were used to simultaneously collect multispectral imagery and soil–plant analysis development (SPAD) values of maize at multiple sampling points in the field, in addition to the spectral reflectances of six standard diffuse reflectance panels with different reflectance values (4.5%, 20%, 30%, 40%, 60% and 65%). The accuracies of the reflectance and vegetation indices (VIs) derived from the imagery were compared, and the effectiveness and accuracy of the SPAD prediction from the normalized difference vegetation index (NDVI) and red-edge NDVI (reNDVI) under different nitrogen treatments were examined at the plot level. The results show that the narrowband Mini-MCA6 camera could produce more accurate reflectance values than the broadband Sequoia camera, but only if the appropriate calibration method (the nonlinear subband empirical line method) was adopted, especially in visible (blue, green and red) bands. However, the accuracy of the VIs was not completely dependent on the accuracy of the reflectance, i.e., the NDVI from Mini-MCA6 was slightly better than that from Sequoia, whereas Sequoia produced more accurate reNDVI than did Mini-MCA6. At the plot level, reNDVI performed better than NDVI in SPAD prediction regardless of which camera was employed. Moreover, the reNDVI had relatively low sensitivity to the vegetation coverage and was insignificantly affected by environmental factors (e.g., exposed sandy soil). This study indicates that UAV multispectral remote sensing technology is instructive for precision agriculture, but more effort is needed regarding calibration methods for vegetation, postprocessing techniques and robust quantitative studies.