A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis
A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis
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DOI：
10.1016/j.isprsjprs.2023.09.025
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发表时间：
2023-11
影响因子：
12.7
通讯作者：
Chishan Zhang;C. Diao
中科院分区：
文献类型：
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作者：
Chishan Zhang;C. Diao
大规模作物估产对于了解农业生产对环境因素和管理实践的反应具有重要意义，在保险设计、贸易决策和经济规划中起着至关重要的作用。经验模型(例如，深度学习模型)越来越多地被用于估计作物产量，能够考虑一系列产量预测因素和复杂的建模关系。然而，作物产量的经验估计仍然面临着重大挑战，特别是在适应作物物候发展的时空模式以及解决多种产量预测因素的异质性方面。与经验收益率估计相关的不同类型的不确定性很少被探索。本研究以美国玉米种植带的大豆为例，建立了一个物候学指导的贝叶斯卷积神经网络(PB-CNN)框架，用于县级作物产量估计和不确定性量化。PB-CNN框架包括三个关键部分：物候图像构建，多流贝叶斯-CNN建模，以及特征重要性(即产量预测因子和物候阶段)和预测不确定性分析(即任意和认知不确定性)。在对作物产量对一组不同的产量预报器(即基于卫星的、与热有关的、与水有关的和与土壤有关的预报器)以及相关的不确定性进行建模时，创新性地集成了关键作物物候阶段以及相关的不确定性，开发的PB-CNN框架的表现优于三个先进的基准模型，在估计2014年至2018年美国玉米带的县级大豆产量时，平均均方根误差为4.622 BU/Ac，平均R2为0.709，平均偏差为−2.057 BU/Ac。在产量预报组中，卫星预报组在大豆估产中最关键，其次是与水和热有关的预报组。在整个生长季节，大豆开花至落叶的物候期在模拟大豆产量方面起着更为关键的作用。土壤预测因子组和早期生长阶段可以提高模型的估计精度，但可能会给产量估计带来更多的不确定性。进一步的不确定性解缠表明，产量估计中的主要不确定性是任意不确定性，主要源于模型输入观测中固有的波动和变化。PB-CNN框架在很大程度上提高了我们对大豆产量对不同作物物候期环境条件的复杂响应以及与之相关的不确定性的理解，从而实现更可持续的农业发展。
Large-scale crop yield estimation is important for understanding the response of agriculture production to environmental forces and management practices, and plays a critical role in insurance designing, trade decision making, and economic planning. The empirical models (e.g., deep learning models) have been increasingly utilized for estimating crop yields with the ability to take into account a range of yield predictors and complex modeling relationships. Yet empirical estimation of crop yields still faces important challenges, particularly in accommodating spatio-temporal crop phenological development patterns as well as tackling the heterogeneity of a diversity of yield predictors. The different types of uncertainties associated with empirical yield estimations have seldom been explored. The objective of this study is to develop a Phenology-guided Bayesian-Convolutional Neural Network (PB-CNN) framework for county-level crop yield estimation and uncertainty quantification, with soybean in the US Corn Belt as a case study. The PB-CNN framework comprises three key components: Phenology Imagery construction, multi-stream Bayesian-CNN modeling, as well as feature importance (i.e., yield predictor and phenological stage) and predictive uncertainty analysis (i.e., aleatoric and epistemic uncertainty). With the innovative integration of critical crop phenological stages in modeling the crop yield response to a heterogeneous set of yield predictors (i.e., satellite-based, heat-related, water-related, and soil predictors) as well as the associated uncertainties, the developed PB-CNN framework outperforms three advanced benchmark models, achieving an average RMSE of 4.622 bu/ac, an average R2of 0.709, and an average bias of −2.057 bu/ac in estimating the county-level soybean yield of the US Corn Belt in testing years 2014–2018. Among the yield predictor groups, the satellite-based predictor group is the most critical in soybean yield estimation, followed by the water- and heat-related predictor groups. Throughout the growing season, the soybean blooming to dropping leaves phenological stages play a more crucial role in modeling the soybean yield. The soil predictor group as well as the early growing stages can improve the model estimation accuracy yet potentially brings more uncertainties into the yield estimation. The further uncertainty disentanglement indicates that the dominant uncertainty in yield estimation is the aleatoric uncertainty, mainly stemming from the fluctuations and variations inherent in the modeling input observations. The PB-CNN framework largely enhances our understanding of the complex soybean yield response to varying environmental conditions across crop phenological stages as well as associated uncertainties for more sustainable agricultural development.