Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models

Kaili Yang; Jiacai Mo; Shanjun Luo; Yi Peng; Shenghui Fang; Xianting Wu; Renshan Zhu; Yuanjin Li; Ningge Yuan; Cong Zhou; Yan Gong

doi:10.34133/plantphenomics.0056

Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models

Plant Phenomics. 2023 May 31:5:0056. doi: 10.34133/plantphenomics.0056. eCollection 2023.

Authors

Kaili Yang¹, Jiacai Mo¹, Shanjun Luo¹, Yi Peng^{1

2}, Shenghui Fang^{1

2}, Xianting Wu^{2

3}, Renshan Zhu^{2

3}, Yuanjin Li¹, Ningge Yuan¹, Cong Zhou¹, Yan Gong^{1

2}

Affiliations

¹ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
² Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, China.
³ College of Life Sciences, Wuhan University, Wuhan, China.

Abstract

The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV). The results indicated that the correlation of VI and AGB was weak for the whole growing season of rice and the accuracy of the height model was also limited for the whole growing season. In comparison with the NDVI-based rice AGB estimation model in 2019 data (R² = 0.03, RMSE = 603.33 g/m²) and canopy height (R² = 0.79, RMSE = 283.33 g/m²), the PAM calculated by NDVI and canopy height could provide a better estimate of AGB of rice (R² = 0.95, RMSE = 136.81 g/m²). Then, based on the time-series analysis of the accumulative model, a simplified photosynthetic accumulation model (SPAM) was proposed that only needs limited observations to achieve R² above 0.8. The PAM and SPAM models built by using 2 years of samples successfully predicted the third year of samples and also demonstrated the robustness and generalization ability of the models. In conclusion, these methods can be easily and efficiently applied to the UAV estimation of rice AGB over the entire growing season, which has great potential to serve for large-scale field management and also for breeding.