Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Wenxiang Wu,Yahui Guo,Hanxi Wang,Kirsten De Beurs,Yi Xiao,Shouzhi Chen,Yongshuo H Fu

doi:10.3390/rs14020244

Abstract

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.

Highlights

IntroductionGlobal warming has advanced the spring vegetation phenology, leading to more uniformity across different tree species and variations in geographic elevations [6,7,8,9,10,11]
Klosterman et al (2014) extracted the phenophase transition dates at 13 temperate deciduous forest sites throughout eastern North America using phenocams and the results showed that the phenological dates derived from high-frequency phenocams had smaller uncertainties than those derived from moderate resolution imaging spectroradiometer (MODIS) and Advanced Very-High-Resolution Radiometer (AVHRR)
The average values of the spectral and textural indices for each day were selected for phenological extraction

Summary

Introduction

Global warming has advanced the spring vegetation phenology, leading to more uniformity across different tree species and variations in geographic elevations [6,7,8,9,10,11]. Both ground observations and model simulations have indicated that major crops such as maize and wheat have been negatively affected by climate change in the past decades [12,13]. Summer maize is one of the dominate crops worldwide and its stable production is crucial for guaranteeing regional and global food security [14,15,16,17]. There is a need to increase maize production by timely monitoring its growth condition and employing adaptive management to reduce the negative impacts of climate change [18,19,20].

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Methods

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Discussion

Conclusion