Estimation of rice yield based on canopy reflectance spectra and carbon flux in diverse growth phases

doi:10.17521/cjpe.2024.0186

Abstract

Abstract:

Aims Accurate estimation of crop yield is important for agricultural policy formulation. Vegetation canopy reflectance spectra and carbon fluxes are the primary data for monitoring crop growth status. However, there are fewer studies comparing their performance in predicting crop yield.

Methods Here, we explored the capability of various parameters to predict rice (Oryza sativa) grain yield and aboveground biomass using synchronized observations of vegetation canopy reflectance spectra and gas exchange parameters.

Important findings Results showed that vegetation reflectance index outperformed carbon flux parameters in estimating rice grain yield and aboveground biomass, in which the optimal estimation parameter was the near-infrared reflectance of vegetation. The vegetative phase was the optimal estimation phase of rice grain yield and aboveground biomass. Our results could provide critical guidance for cropland yield estimation based on remote sensing data and ground flux data.

Key words: vegetation canopy reflectance index, grain yield, aboveground biomass, near-infrared reflectance of vegetation, solar-induced chlorophyll fluorescence

XU En-Xiang, ZHOU Lei, ZHANG Xiao-Wei, ZHANG Guo-Ping, ZHONG Du-Wei, HUANG Zhi, LIU Pai, CHI Yong-Gang. Estimation of rice yield based on canopy reflectance spectra and carbon flux in diverse growth phases[J]. Chin J Plant Ecol, 2026, 50(1): 82-93.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2024.0186

https://www.plant-ecology.com/EN/Y2026/V50/I1/82

Figures/Tables 8

Table 1 Measurement dates of Oryza sativa in 2023

日期 Date	年序日 DOY	时期 Stage	阶段 Phase
07-10	191	移栽期 Transplanting	营养生长 Vegetative
07-17	198	分蘖期 Tillering
07-25	206	分蘖期 Tillering
08-06	218	拔节期 Jointing
08-12	224	拔节期 Jointing
08-20	232	孕穗期 Booting	生殖生长 Reproductive
08-26	238	孕穗期 Booting
09-08	251	抽穗期 Heading
09-18	261	扬花期 Flowering
09-26	269	乳熟期 Milk grain	成熟 Ripening
10-02	275	蜡熟期 Dough grain
10-15	288	蜡熟期 Dough grain
10-22	295	完熟期 Mature grain

Fig. 1 Controlled experiment region and field experiment sampling points (A), and measurements of Oryza sativa canopy reflectance spectra (B), net ecosystem exchange (C) and ecosystem respiration (D).

Fig. 2 Seasonal variations in daily average reflectance index (A), carbon flux parameters (B) and vegetation physiological parameters (C) (mean ± SE, n = 9). Grey dashed lines indicate different growth phases. ER, ecosystem respiration; EVI, enhanced vegetation index; GPP, gross primary productivity; LUE, light use efficiency; NDVI, normalized difference vegetation index; NEE, net ecosystem exchange; NIRv, near-infrared reflectance of vegetation; SIF, solar-induced chlorophyll fluorescence; SIFy, solar-induced chlorophyll fluorescence yield.

Table 2 Reflectance index, carbon flux parameters and vegetation physiological parameters at different nitrogen levels (mean ± SE)

Fig. 3 Correlations among various parameters within vegetative phase (A), reproductive phase (B) and ripening phase (C). The numbers in the figure are Pearson correlation coefficients, and the white background indicates the insignificant (p > 0.05) correlations. ER, ecosystem respiration; EVI, enhanced vegetation index; GPP, gross primary productivity; LUE, light use efficiency; NDVI, normalized difference vegetation index; NEE, net ecosystem exchange; NIRv, near-infrared reflectance of vegetation; SIF, solar-induced chlorophyll fluorescence; SIFy, solar-induced chlorophyll fluorescence yield.

Fig. 4 Coefficient of determination (bar plot) and root mean square error (line chart) of grain yield (A-C) and aboveground biomass (D-F) with reflectance index, carbon flux parameters and vegetation physiological parameters at different growing phases. *, linear regressions that are significant (p < 0.05). ER, ecosystem respiration; EVI, enhanced vegetation index; GPP, gross primary productivity; LUE, light use efficiency; NDVI, normalized difference vegetation index; NEE, net ecosystem exchange; NIRv, near-infrared reflectance of vegetation; SIF, solar-induced chlorophyll fluorescence; SIFy, solar-induced chlorophyll fluorescence yield.

Fig. 5 Correlations of reflectance index and vegetation physiological parameters with grain yield (A-E) and aboveground biomass (F-J) based on field experiment. EVI, enhanced vegetation index; NDVI, normalized difference vegetation index; NIRv, near-infrared reflectance of vegetation; SIF, solar-induced chlorophyll fluorescence; SIFy, solar-induced chlorophyll fluorescence yield. R2, coefficient of determination; RMSE, root mean square error.

Fig. 6 Correlation between grain yield and aboveground biomass based on controlled experiment and field experiment.

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