Assessing radiometric corrections for UAS multi-spectral imagery in horticultural environments

UAS-based multi-spectral imagery is becoming ubiquitous for monitoring and managing various horticultural crops. To accurately measure and monitor their structure and condition and estimate yields, appropriately corrected data must be used to drive the necessary algorithms. There are several popular radiometric correction methods commonly used for UAS-based data correction. However, their relative and absolute accuracies are not known. This study used three flight datasets, including along- and across-tree-row flight patterns in an avocado orchard. Four correction methods were applied to produce at-surface reflectance image mosaics for each flight pattern as well as the grid pattern and the results were compared to assess the reflectance consistency. Results show that no method provided consistently correct at-surface reflectance for the same features. A BRDF correction workflow was being developed to address these limitations. Preliminary application of the BRDF correction shows that it significantly improves the brightness consistency of features across different images.