基于不同生育阶段冠层光谱和碳通量的水稻产量估算

doi:10.17521/cjpe.2024.0186

摘要/Abstract

摘要：

准确估算作物产量对于农业政策制定具有重要意义。植被冠层光谱和碳通量是监测作物生长状态的主要数据, 然而比较两者在预测产量方面精确度的研究较少。该研究以同步观测的植被冠层反射光谱和气体交换参数为基础数据, 探索各类参数预测水稻(Oryza sativa)籽粒产量和地上生物量的精确度。结果表明, 植被反射指数在估算水稻籽粒产量和地上生物量时的表现优于碳通量参数, 其中最优估算参数为植被近红外反射率; 营养生长阶段是水稻籽粒产量和地上生物量的最优估算阶段。研究结果可为基于遥感数据和地面通量数据的农田产量估算提供指导。

关键词: 植被冠层反射指数, 籽粒产量, 地上生物量, 植被近红外反射率, 日光诱导叶绿素荧光

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

徐恩相, 周蕾, 章晓炜, 张国萍, 仲杜伟, 黄智, 刘派, 迟永刚. 基于不同生育阶段冠层光谱和碳通量的水稻产量估算. 植物生态学报, 2026, 50(1): 82-93. DOI: 10.17521/cjpe.2024.0186

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. Chinese Journal of Plant Ecology, 2026, 50(1): 82-93. DOI: 10.17521/cjpe.2024.0186

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2024.0186

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

图/表 8

表1 2023年水稻测量日期

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

图1 控制实验区域和田块实验采样点(A)以及水稻冠层光谱(B)、净生态系统交换(C)和生态系统呼吸(D)的测量。

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).

图2 日均反射指数(A)、碳通量参数(B)和植被生理参数(C)的季节变化(平均值±标准误, n = 9)。灰色虚线表示不同生长阶段。ER, 生态系统呼吸; EVI, 增强型植被指数; GPP, 总初级生产力; LUE, 光能利用率; NDVI, 归一化植被指数; NEE, 净生态系统碳交换; NIRv, 植被近红外反射率; SIF, 日光诱导叶绿素荧光; SIFy, 日光诱导叶绿素荧光产率。

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.

表2 不同氮水平下反射指数、碳通量参数和植被生理参数(平均值±标准误)

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

图3 各类参数在营养生长阶段(A), 生殖生长阶段(B)和成熟阶段(C)内的相关性。图中数字为Pearson相关系数, 白色表示未通过显著性检验(p > 0.05)。ER, 生态系统呼吸; EVI, 增强型植被指数; GPP, 总初级生产力; LUE, 光能利用率; NDVI, 归一化植被指数; NEE, 净生态系统碳交换; NIRv, 植被近红外反射率; SIF, 日光诱导叶绿素荧光; SIFy, 日光诱导叶绿素荧光产率。

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.

图4 籽粒产量(A-C)和地上生物量(D-F)与反射指数和碳通量参数以及植被生理参数在不同生育阶段内的决定系数(柱状图)和均方根误差(折线图)。*, 线性回归结果显著(p < 0.05)。ER, 生态系统呼吸; EVI, 增强型植被指数; GPP, 总初级生产力; LUE, 光能利用率; NDVI, 归一化植被指数; NEE, 净生态系统碳交换; NIRv, 植被近红外反射率; SIF, 日光诱导叶绿素荧光; SIFy, 日光诱导叶绿素荧光产率。

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.

图5 基于田块实验反射指数和植被生理参数与籽粒产量(A-E)和地上生物量(F-J)的相关性。EVI, 增强型植被指数; NDVI, 归一化植被指数; NIRv, 植被近红外反射率; SIF, 日光诱导叶绿素荧光; SIFy, 日光诱导叶绿素荧光产率。R2, 决定系数; RMSE, 均方根误差。

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.

图6 基于控制实验和田块实验籽粒产量和地上生物量的相关性。

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

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