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Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

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Abstract

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives (\(\rho^{\prime}\), \(\rho^{\prime\prime}\)) from twelve airborne hyperspectral images of a cotton field for one season \(\rho^{\prime}\) were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation.

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Acknowledgements

The research was supported by the Program for new century excellent talents in Heilongjiang Provincial University, PRC, Postdoctoral start-up fund of Heilongjiang province (LBH-Q13026) and “Young Talents” Project of Northeast Agricultural University, PRC (14QC28). The study was conducted while the primary author was located at the University of California Davis.

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Authors and Affiliations

  1. College of Natural Resources and the Environment, Northeast Agricultural University, Harbin, 150030, China

    Huanjun Liu & Xinle Zhang

  2. Department of Land, Air, and Water Resources, Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, Davis, 139 Veihmeyer Hall/One Shields Avenue, Davis, CA, 95616-8617, USA

    Michael L. Whiting & Susan L. Ustin

  3. Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas CSIC), Alameda del Obispo s/n, 14004, Cordoba, Spain

    P. J. Zarco-Tejada

  4. Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, 960 Carling Avenue, Ottawa, ON, K1A 0C6, Canada

    Ted Huffman

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  1. Huanjun Liu
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Correspondence to Susan L. Ustin.

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Liu, H., Whiting, M.L., Ustin, S.L. et al. Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images. Precision Agric 19, 348–364 (2018). https://doi.org/10.1007/s11119-017-9521-x

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