Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level.
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This project was financially supported under Cooperative Agreement no. 58-8042-7-006 from the U.S. Department of Agriculture, from NASA Applied Sciences-Water Resources Program under Award no. 200906 NNX17AF51G, and by the Utah Water Research Laboratory at Utah State University. The authors wish to thank E&J Gallo Winery for their continued collaborative support for this research, and the AggieAir UAV Remote Sensing Group at the Utah Water Research Laboratory for their UAV technology and skill and hard work in acquiring the scientific-quality, high-resolution aerial imagery used in this project. USDA is an equal opportunity provider and employer.
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Communicated by N. Agam.
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Aboutalebi, M., Torres-Rua, A.F., Kustas, W.P. et al. Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration. Irrig Sci 37, 407–429 (2019). https://doi.org/10.1007/s00271-018-0613-9