{'fraction_non_alphanumeric': 0.03137254901960784, 'fraction_numerical': 0.002240896358543417, 'mean_word_length': 5.739622641509434, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions?

Abstract.Abstract. Highlights Vegetation indices (NDVI, GNDVI, SAVI) extracted from high-resolution satellite imagery were significantly associated with vegetation indices extracted from UAV imagery. High-resolution satellite data can be used to predict maize yield at breeding plots scale. Breeding plot sizes and the variability between maize genotypes may be associated with prediction accuracies. The recent availability of high spatial and temporal resolution satellite imagery has widened its applications in agriculture. Plant breeding and genetics programs are currently adopting unmanned aerial vehicle (UAV) imaging as a complement to the ground data collection. With breeding trials across multiple geographical locations, UAV imaging is not always convenient. Hence, we anticipate that similar to UAV imaging, phenotyping of individual test plots from high-resolution satellite imagery may also provide value to plant genetics and breeding programs. In this study, high spatial resolution satellite imagery (~38-50 cm/pixel) was compared to imagery acquired using an UAV for its ability to phenotype maize grown in typical breeding plots. Statistics (mean, median, sum) of color (red, green, blue), near-infrared, and vegetation indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and soil adjusted vegetation index (SAVI) were extracted from imagery from both sources (UAV and satellite) for comparison at three time points. In general, a strong correlation between satellite and UAV imagery extracted NDVI, GNDVI, and SAVI features (especially with mean and median statistics, p