多源遥感数据耦合CBA-Wheat模型的冬小麦生物量估算研究
Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing
- 2024年 页码：1-12
网络出版日期： 2024-03-07
DOI： 10.11834/jrs.20243080
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摘要
生物量是反映作物生长状况的重要指标，及时准确估计冬小麦地上生物量对于产量预测和田间管理决策具有重要意义。综合考虑遥感植被指数（Vegetation index，VI）与数字化生育期（Zadoks stage，ZS）创建的作物生物量模型（Crop biomass algorithm for wheat，CBA-Wheat）适用于全生育时期的冬小麦生物量估算，但是模型参数基于地面高光谱数据构建，而卫星遥感数据在应用过程中，需要使用更多的地面实测数据进行模型参数的调试，限制了模型的推广使用。因此，本研究采用遗传优化算法（Genetic algorithm，GA）对CBA-Wheat模型进行全局优化确定模型最优参数，利用高分辨率遥感影像提取VI与试验记录的ZS数据，分别构建以不同VI为输入变量的冬小麦生物量反演模型。结果表明：增强型植被指数（Enhanced Vegetation Index2，EVI2）为输入变量建立的模型精度最高，冬小麦生物量估算验证的决定系数（R
2
）和均方根误差（RMSE）分别为0.92和1.37t/ha。基于CBA-Wheat模型的生物量估算精度效果优于基于偏最小二乘回归方法的生物量估算精度（R
2
=0.85，RMSE=1.87t/ha）。本研究基于遗传算法优化的CBA-Wheat模型不仅具有较高的反演精度，而且适用于全生育期反演，在区域大面积生物量预测方面具有较好的应用潜力。
Abstract
Objective
Biomass is an important indicator to reflect the growth status of crops. Timely and accurate estimation of aboveground biomass of winter wheat is significant for yield prediction and field management decision-making. The crop biomass model (CBA-Wheat) developed by remote sensing spectral index (VI) and digital growth stage (ZS) is suitable for the estimation of winter wheat biomass in the whole growth period. The first layer of this model is a linear model of AGB and VI
which constructs linear regression models between AGB and vegetation index at different growth periods. On this basis
it was found that the AGB model coefficients of each period have a good evolutionary law with ZS. However
the model parameters are based on the ground hyperspectral data in the previous study
and the satellite data need much ground measured data to calibrate the model parameters
which limits the popularization at regional scale.
Method
In this study
genetic algorithm (GA) is used to globally optimize the model parameters of the CBA-Wheat model in this study.
GA combines the survival rules of the fittest in the process of biological evolution with the random information exchange system of the chromosome within the population and has a more efficient global optimization effect for some non-linear
multi model
multi objective function optimization problems. Two input variables of CBA-Wheat includes ZS data from field recorded and VI from high-resolution remote sensing images. Four VI-based CBA-Wheat models of enhanced vegetation index 2 (CBA-Wheat
EVI2
)
difference vegetation index (CBA-Wheat
DVI
)
ratio vegetation index (CBA-Wheat
RVI
) and modified simple ratio vegetation index (CBA-Wheat
MSR
)
were constructed and the best model was used for AGB mapping.
Meanwhile
performance of partial least squares regression (PLSR) was used to compare the CBA-Wheat model accuracy.
Result
The results showed that all four CBA-Wheat models have good accuracy
and the simulated winter wheat biomass is consistent with the measured biomass
Among them
the precision of the CBA-Wheat
EVI2
model has the highest determination coefficient (R
2
) and root mean square error (RMSE) of 0.92 and 1.37t/ha for validation results
respectively. The precision of biomass estimation based on CBA-Wheat model was better than that based on PLSR method (R
2
=0.85
RMSE=1.87t/ha). The CBA-Wheat model has good biomass estimation performance in various growth stages of winter wheat
and performs well in high biomass situations without significant underestimation.
Conclusion
The CBA-Wheat model optimized by genetic algorithm in this study not only has high inversion accuracy
but also is applicable to the inversion of the whole growth period
and has good application potential in the prediction of regional large area biomass.
It also provides a good method reference for biomass prediction of other crops.
关键词
冬小麦地上生物量遗传算法CBA-Wheat多源数据EVI2Sentinel-2遥感
Keywords
winter wheatbiomassCBA-Wheat modelgenetic algorithmmulti-source dataEVI2Sentinel-2remote sensing
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福建师范大学 湿润亚热带山地生态国家重点实验室培育基地
福建师范大学地理研究所
国家林业和草原局华东调查规划设计院
福建省减灾中心
中国地质大学（武汉） 地质调查研究院
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