SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

Yapeng Wu; Wenhui Wang; Yangyang Gu; Hengbiao Zheng; Xia Yao; Yan Zhu; Weixing Cao; Tao Cheng

doi:10.34133/plantphenomics.0087

SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

Plant Phenomics. 2023 Sep 6:5:0087. doi: 10.34133/plantphenomics.0087. eCollection 2023.

Authors

Yapeng Wu¹, Wenhui Wang^{1

2}, Yangyang Gu¹, Hengbiao Zheng¹, Xia Yao¹, Yan Zhu¹, Weixing Cao¹, Tao Cheng¹

Affiliations

¹ National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, PR China.
² Langfang Normal University, 100 Aimin West Road, Langfang, Hebei 065000, PR China.

Abstract

Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading. The effect of background materials on PNPA estimated by textural indices (TIs) was examined, and the composite index SPSI was constructed by integrating the optimal spectral index (SI) and TI. Subsequently, the performance of SPSI was evaluated in comparison with other indices (SI and TIs). The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_[HOM], TI_[ENT], and TI_[SEM] among all indices from 8 types of textural features. SPSI, which was calculated by the formula DATT_{[850,730,675]} + NDTI_COR[850,730], exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_{[850,730,675]}, TI_NDRE[MEA], and NDTI_COR[850,730]. For the unified models assembling 2 experimental datasets, the R_V² values of SPSI increased by 0.11 to 0.23, and both RMSE and RRMSE decreased by 16.43% to 38.79% as compared to the suboptimal index on each date. These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.