предыдущий|следующий

Estimating winter wheat biomass by coupling the CBA-Wheat model and multispectral remote sensing

  • role： First author第一作者
  • Affiliation：

    College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：wangshijun@sdust.edu.cn
  • Introduction：王士俊，研究方向为农业定量遥感。E-mail： wangshijun@sdust.edu.cn
WANG Shijun1,  
  • Affiliation：

    College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

LIU Miao1,  
  • Affiliation：

    Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

ZHAO Yu2,  
  • Affiliation：

    China Institute of Intelligent Information Processing and Systems, Central South University, Changsha 410083, China

LIU Zhaoyu3,  
  • Affiliation：

    College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

LIU Xiuyu1,  
  • Affiliation：

    Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

FENG Haikuan2,  
  • Affiliation：

    Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan 250100, China

SUI Xueyan4,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

  • Email：lizh323@126.com
  • Introduction：李振海，研究方向为农业遥感。E-mail： lizh323@126.com
LI Zhenhai12*

реферат

Biomass is an important indicator that reflects the growth status of crops. Timely and accurate estimation of aboveground biomass of winter wheat is crucial for yield prediction and field management decision-making. The crop biomass model (CBA-Wheat) developed using the 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, and it constructs linear regression models between AGB and VI in different growth periods. The AGB model coefficients of each period have a good evolutionary law with ZS. However, the model parameters are based on ground hyperspectral data in a previous study, and the satellite data need extensive ground-measured data to calibrate the model parameters, thus limiting popularization at the regional scale. In this study, the Genetic Algorithm (GA) is used to optimize the parameters of CBA-Wheat model globally. GA combines the survival rules of the fittest in biological evolution with the random information exchange system of the chromosome within the population and has an efficient global optimization effect on some nonlinear, multimodel, multiobjective function optimization problems. The two input variables of CBA-Wheat are field-recorded ZS data and VI from high-resolution remote sensing images. Four VI-based CBA-Wheat models, namely, enhanced vegetation index 2 (CBA-WheatEVI2), difference vegetation index (CBA-WheatDVI), ratio vegetation index (CBA-WheatRVI), and modified simple ratio vegetation index (CBA-WheatMSR), are constructed. The best model is used for AGB mapping. Meanwhile, partial least squares regression (PLSR) is adopted to compare the accuarcy of CBA-Wheat models. Results showed that all four CBA-Wheat models have good accuracy, and the simulated winter wheat biomass is consistent with the measured biomass. Among the models, CBA-WheatEVI2 has the highest determination coefficient (R2) and root mean square error (RMSE) of 0.92 and 1.37 t/ha, respectively. Compared with the machine learning method, the accuracy of biomass estimation based on CBA-Wheat model is better than that of biomass estimation based on the PLSR method (R2=0.85, RMSE=1.87 t/hm2). The CBA-Wheat model has good biomass estimation performance at the various growth stages of winter wheat and performs well in high-biomass situations without considerable underestimation. The CBA-Wheat model optimized by GA in this study has high inversion accuracy and is suitable for the inversion of winter wheat at multiple growth stages, and has good application potential in using satellite remote sensing data to predict large-area biomass.

ключеви́че слова́

winter wheat;biomass;genetic algorithm;CBA-Wheat model;multi-source data;EVI2;Sentinel-2;remote sensing

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