面向干旱条件下的冬小麦估产HLM模型改进研究

doi:10.12133/j.smartag.SA202408009

Smart Agriculture

• •

面向干旱条件下的冬小麦估产HLM模型改进研究

赵培钦¹^,², 刘长斌²(), 郑婕¹^,², 孟炀², 梅新¹, 陶婷¹^,², 赵倩¹^,², 梅广源¹^,², 杨小冬²()

^1. 湖北大学资源环境学院，湖北武汉 430062，中国
^2. 农业农村部农业遥感机理与定量遥感重点实验室，北京市农林科学院信息技术研究中心，北京 100097，中国

收稿日期:2024-08-14 出版日期:2025-01-24
基金项目:
国家重点研发计划(2023YFD2000105)
作者简介:
赵培钦，研究方向为农业遥感。E-mail： w1564826543@gmail.com
通信作者:
刘长斌，工程师，研究方向为农业信息化技术研究。E-mail： liucb@nercita.org.cn；
杨小冬，博士，研究员，研究方向为农业遥感、农业空间信息服务系统研发。E-mail： yangxd@nercita.org.cn

Improvement of HLM modeling for Winter Wheat Yield Estimation Under Drought Conditions

ZHAO Peiqin¹^,², LIU Changbin²(), ZHENG Jie¹^,², MENG Yang², MEI Xin¹, TAO Ting¹^,², ZHAO Qian¹^,², MEI Guangyuan¹^,², YANG Xiaodong²()

^1. School of Resources and Environment, Hubei University, Wuhan 430062, China
^2. Key Laboratory of Agricultural Remote Sensing and Quantitative Remote Sensing Mechanism, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences. Beijing 100097, China

Received:2024-08-14 Online:2025-01-24
Foundation items:National Key Research and Development Program of China(2023YFD2000105)
About author:
ZHAO Peiqin, E-mail: w1564826543@gmail.com
Corresponding author:
LIU Changbin, E-mail: liucb@nercita.org.cn;
YANG Xiaodong, E-mail: yangxd@nercita.org.cn

摘要/Abstract

摘要：

【目的】 现有的作物估产模型通常在灾害气候条件下的估产精度不高，本研究提出一种基于干旱天气指数减产率模型的改进分层线性模型（Improved Hierarchical Linear Model, IHLM），旨在提高在干旱条件下作物产量估算的精度。 【方法】 采用最大值的增强植被指数-2（Maximum Enhanced Vegetation Index 2, EVI2max）和每年3月~5月降水量，辐射量和气温等气象数据和2018—2021年山东省160个农情调查基点的冬小麦实测产量数据构建冬小麦产量预测基础分层线性模型（Hierarchical Linear Model, HLM）。考虑到气象因素的变异程度是影响作物生长的关键障碍因子，首先将气象因子相对性计算进行模型改进，并对改进的HLM模型与随机森林（Random Forest, RF）模型、支持向量回归（Support Vector Regression, SVR）模型和极端梯度提升（Extreme Gradient Boosting, XGBOOST）模型进行精度对比。然后引入农业保险行业的干旱天气指数减产率模型，对改进的HLM模型进一步优化，从而更加适应干旱条件下的作物估产。为了验证IHLM模型的迁移性，本研究将其应用于河南省对比分析，以评估该模型在不同地理和气候条件下的表现。 【结果和讨论】 基于相对气象因子（Relative Meteorological Factors, rMF）改进的HLM模型精度相比于RF、SVR和XGBOOST更高，验证精度皮尔逊相关系数（Pearson Correlation Coefficien, r）为0.76，均方根误差（Root Mean Square Error, RMSE）为0.60 t/hm²，归一化均方根误差（Normalized Root Mean Square Error, nRMSE）为11.21%。在干旱条件数据集中，利用冬小麦干旱天气指数和冬小麦减产率的关系对模型进行了改进，改进之后RMSE减少了0.48 t/hm², nRMSE减少了28.64个百分点，提高了IHLM模型在干旱条件下的精度。 【结论】 该研究对冬小麦产量HLM模型进行改进，提高了模型精度，在干旱情况下模型精度和稳定性有一定提升，相比于RF、SVR、XGBOOST模型，IHLM模型更适合对冬小麦产量预测。

关键词: 冬小麦, 产量预测, HLM模型, 干旱

Abstract:

[Objective] Winter wheat yield is crucial for national food security and the standard of living of the population. Existing crop yield prediction models often show low accuracy under disaster-prone climatic conditions. This study proposed an improved hierarchical linear model (IHLM) based on a drought weather index reduction rate, aiming to enhance the accuracy of crop yield estimation under drought conditions. [Methods] The study constructed a winter wheat yield prediction hierarchical linear model (HLM) using the maximum Enhanced Vegetation Index-2 (EVI2max), meteorological data (precipitation, radiation, and temperature from March to May), and observed winter wheat yield data from 160 agricultural survey stations in Shandong province (2018–2021). To validate the model's accuracy, 70% of the data from Shandong province was randomly selected for model construction, and the remaining data was used to validate the accuracy of the yield model. Based on the basic HLM model, the study proposed a method to build an improved hierarchical linear model (IHLM). It considered the variation in meteorological factors as a key obstacle affecting crop growth and improved the model by calculating the relative meteorological factors. The calculation of relative meteorological factors helped reduce the impact of inter-annual differences in meteorological data. The accuracy of the improved HLM model was compared with that of the Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models. The HLM model provided more intuitive interpretation, especially suitable for processing hierarchical data, which helped capture the variability of winter wheat yield data under drought conditions. Therefore, a drought weather index reduction rate model from the agricultural insurance industry was introduced to further optimize the HLM model, resulting in the construction of the IHLM model. The IHLM model was designed to improve crop yield prediction accuracy under drought conditions. Since the precipitation differences between Henan and Shandong Provinces were small, to test the transferability of the IHLM model, Henan province sample data was processed in the same way as in Shandong, and the IHLM model was applied to Henan province to evaluate its performance under different geographical conditions. [Results and Discussions] The accuracy of the HLM model, improved based on relative meteorological factors (rMF), was higher than that of RF, SVR, and XGBoost. The validation accuracy showed a Pearson correlation coefficient (r) of 0.76, a Root Mean Squared Error (RMSE) of 0.60 t/hm², and a Normalized RMSE (nRMSE) of 11.21%. In the drought conditions dataset, the model was further improved by incorporating the relationship between the winter wheat drought weather index and the reduction rate of winter wheat yield. After the improvement, the RMSE decreased by 0.48 t/hm², and the nRMSE decreased by 28.64 percentage points, significantly enhancing the accuracy of the IHLM model under drought conditions. The IHLM model also demonstrated good applicability when transferred to Henan Province. This study used meteorological data from March to May and remote sensing data from EVI2max, which are easy to obtain, highly transferable, and offer good accuracy and resolution. Additionally, this research provided an approach to improve model stability under extreme weather conditions, showing a significant improvement in the model's performance. [Conclusions] The IHLM model developed in this study improved the accuracy and stability of crop yield predictions, especially under drought conditions. Compared to RF, SVR, and XGBoost models, the IHLM model was more suitable for predicting winter wheat yield. This research can be widely applied in the agricultural insurance field, playing a significant role in the design of agricultural insurance products, rate setting, and risk management. It enables more accurate predictions of winter wheat yield under drought conditions, with results that are closer to actual outcomes.

Key words: winter wheat, yield prediction, HLM model, drought conditions

中图分类号:

赵培钦, 刘长斌, 郑婕, 孟炀, 梅新, 陶婷, 赵倩, 梅广源, 杨小冬. 面向干旱条件下的冬小麦估产HLM模型改进研究[J]. 智慧农业(中英文), doi: 10.12133/j.smartag.SA202408009.

ZHAO Peiqin, LIU Changbin, ZHENG Jie, MENG Yang, MEI Xin, TAO Ting, ZHAO Qian, MEI Guangyuan, YANG Xiaodong. Improvement of HLM modeling for Winter Wheat Yield Estimation Under Drought Conditions[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202408009.

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数据集	样本数量/个	产量/（t/hm²）
数据集	样本数量/个	最小值	最大值	均值	标准差	变异系数
2018	28	5.63	9.50	7.68	1.12	0.15
2019	37	6.41	9.57	8.18	0.78	0.10
2020	37	4.27	9.32	8.00	0.87	0.11
2021	58	4.70	10.07	8.04	0.95	0.12
建模集	112	4.70	10.07	8.04	0.93	0.12
验证集	48	4.27	9.39	7.90	0.96	0.12
总计	160	4.27	10.07	8.00	0.94	0.12

数据集	3月			4月			5月
数据集	Pre/mm	Rad/（MJ/ m²）	Tem/℃	Pre/mm	Rad/（MJ/ m2）	Tem/℃	Pre/mm	Rad/（MJ/ m²）	Tem/℃
2018	0.92	13.97	10.28	1.76	16.0	16.41	2.53	16.56	21.25
2019	0.32	15.47	10.52	1.55	15.21	14.99	0.27	19.26	22.16
2020	0.31	14.53	10.60	1.08	17.82	14.56	1.72	18.34	21.82
2021	0.68	13.15	9.72	1.16	14.58	14.21	1.44	18.30	20.65

模型类别	超参数	超参数范围	最优超参数
RF	子树的数量	100，200，500，1 000，2 000，4 000	2000
RF	最大生长深度	1，3，5，10，20	3
SVR	惩罚系数	100，500，1 000，2 000，4 000	1000
SVR	核函数类型	linear，rbf，poly	linear
XGBOOST	子树的数量	50，60，70，80	70
	最大深度	11，13，15，17，19，21	15
	学习率	0.01，0.1，0.2	0.1

模型类别	r	RMSE/（t/hm2）	nRMSE/%
RF	0.71	0.69	13.50
SVR	0.74	0.71	13.90
XGBOOST	0.72	0.67	13.04
HLM	0.72	0.74	14.41
HLM基于rMF改进后	0.75	0.65	12.80

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面向干旱条件下的冬小麦估产HLM模型改进研究

Improvement of HLM modeling for Winter Wheat Yield Estimation Under Drought Conditions

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