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Comparison of satellite-based models for estimating gross primary productivity in agroecosystems
Agricultural and Forest Meteorology ( IF 6.2 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.agrformet.2020.108253
Shouzheng Jiang , Lu Zhao , Chuan Liang , Ningbo Cui , Daozhi Gong , Yaosheng Wang , Yu Feng , Xiaotao Hu , Qingyao Zou

Abstract Satellite-based gross primary productivity (GPP) models have been widely used for simulating carbon exchanges of terrestrial ecosystems. However, the performances of various GPP models in agroecosystems have been rarely explored. In this study, we calibrated the model parameters and compared the performances of seven light use efficiency (LUE-GPP) models and five vegetation-index (VI-GPP) models for simulating daily GPP of agroecosystems over 106 crop growing seasons, and examined the effects of model structure on model performance. The simulations were carried out based on 19 eddy covariance (EC) sites from the global flux network and vegetation indices obtained from MODIS. The calibrated potential LUE (emax) for C4 crop (summer maize, 2.59±0.94 g C MJ−1) was higher than that for C3 crops (1.42±0.58 g C MJ−1) in any LUE-GPP models. The performances of models differed across the crops. Generally, all models performed better for C3 crops than C4 crops, and for winter crops (winter wheat-Triticum aestivum L, rape-Brassica napus L, and winter barley-Hordeum vulgare L) than summer crops (summer maize-Zea mays L, potato-Solanum tuberosum L, rice-Oryza sativa L. and soybean-Glycine max (L.) Merr.). Cloudiness index-LUE (CI-LUE) model outperformed the other LUE-GPP models, and vegetation index (VEI) model outperformed the other VI-GPP models. LUE-GPP models demonstrated better performance than VI-GPP models due to the inclusion of water stress (Ws) and temperature stress (Ts). A comparison of the model structures showed that models only considering the effects of Ws produced smaller errors than those only considering the effects of Ts in simulating GPP. Ws algorithms generated the larger variations in LUE-GPP models compared to those of Ts, especially during the drought period. All models obtained higher R2 and smaller errors using the minimum method (Min (Ts, Ws)) than using the multiplication method (Ts × Ws) to integrate the effects of Ts and Ws on GPP, which suggested that the minimum method was better than the multiplication method to integrate Ts and Ws on LUE. These results showed that satellite-based models with calibrated crop-specific parameters have the potential to serve as the basis for estimation of agroecosystem GPP, and can provide direction for future model structure optimization.

中文翻译:

用于估算农业生态系统初级生产力总值的卫星模型比较

摘要 基于卫星的总初级生产力(GPP)模型已被广泛用于模拟陆地生态系统的碳交换。然而,很少探索各种 GPP 模型在农业生态系统中的性能。在这项研究中,我们校准了模型参数并比较了七个光利用效率(LUE-GPP)模型和五个植被指数(VI-GPP)模型在模拟 106 个作物生长季节的农业生态系统每日 GPP 的性能,并检查了模型结构对模型性能的影响。模拟是基于来自全球通量网络的 19 个涡旋协方差 (EC) 站点和从 MODIS 获得的植被指数进行的。在任何 LUE-GPP 模型中,C4 作物(夏玉米,2.59±0.94 g C MJ-1)的校准潜在 LUE(emax)高于 C3 作物(1.42±0.58 g C MJ-1)。模型的性能因作物而异。一般来说,所有模型对 C3 作物的表现都优于 C4 作物,对冬季作物(冬小麦-Triticum aestivum L、油菜-欧洲油菜 L 和冬大麦-Hordeum vulgare L)优于夏季作物(夏玉米-Zea mays L、马铃薯-Solanum tuberosum L、水稻-Oryza sativa L. 和大豆-Glycine max (L.) Merr.)。云量指数-LUE(CI-LUE)模型优于其他LUE-GPP模型,植被指数(VEI)模型优于其他VI-GPP模型。由于包含水分胁迫 (Ws) 和温度胁迫 (Ts),LUE-GPP 模型表现出比 VI-GPP 模型更好的性能。模型结构的比较表明,在模拟 GPP 时,仅考虑 Ws 影响的模型比仅考虑 Ts 影响的模型产生的误差更小。与 Ts 相比,Ws 算法在 LUE-GPP 模型中产生了更大的变化,尤其是在干旱时期。所有模型使用最小法(Min (Ts, Ws))比使用乘法法(Ts × Ws)综合Ts和Ws对GPP的影响获得更高的R2和更小的误差,表明最小法优于在 LUE 上积分 Ts 和 Ws 的乘法方法。这些结果表明,具有校准作物特定参数的基于卫星的模型有可能作为农业生态系统 GPP 估计的基础,并可以为未来的模型结构优化提供方向。Ws)) 比使用乘法(Ts × Ws)积分 Ts 和 Ws 对 GPP 的影响,这表明最小法优于乘法积分 Ts 和 Ws 对 LUE 的影响。这些结果表明,具有校准作物特定参数的基于卫星的模型有可能作为农业生态系统 GPP 估计的基础,并可以为未来的模型结构优化提供方向。Ws)) 比使用乘法(Ts × Ws)积分 Ts 和 Ws 对 GPP 的影响,这表明最小法优于乘法积分 Ts 和 Ws 对 LUE 的影响。这些结果表明,具有校准作物特定参数的基于卫星的模型有可能作为农业生态系统 GPP 估计的基础,并可以为未来的模型结构优化提供方向。
更新日期:2021-02-01
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