Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery

Bo Chen; Guanmin Huang; Xianju Lu; Shenghao Gu; Weiliang Wen; Guangtao Wang; Wushuai Chang; Xinyu Guo; Chunjiang Zhao

doi:10.3389/fpls.2023.1253536

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery

Front Plant Sci. 2023 Dec 18:14:1253536. doi: 10.3389/fpls.2023.1253536. eCollection 2023.

Authors

Bo Chen^{1

2

3}, Guanmin Huang^{1

4}, Xianju Lu^{1

4}, Shenghao Gu¹, Weiliang Wen^{1

4}, Guangtao Wang², Wushuai Chang², Xinyu Guo^{1

2}, Chunjiang Zhao^{1

3}

Affiliations

¹ Information Technology Research Center, Beijing Academy of Agriculture Forestry Sciences, Beijing, China.
² Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China.
³ College of Resources and Environment, Jilin Agricultural University, Changchun, China.
⁴ Nongxin Science & Technology (Beijing) Co., Ltd, Beijing, China.

Abstract

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R² and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods.

Keywords: SPAD values; UAV multispectral; canopy chlorophyll; maize; vertical distribution.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Key R&D Program of China (2022ZD0115705), the construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences (KJCX201917), the supported by the earmarked fund for CARS-02 and CARS-54 and the Young Elite Scientist Sponsorship Program by BAST (No.BYESS2023204).