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  • Title: [Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China].
    Author: Chen Y, Zhao GX, Chang CY, Wang ZR, Li YS, Zhao HS, Zhang SW, Pan JR.
    Journal: Ying Yong Sheng Tai Xue Bao; 2023 Dec; 34(12):3347-3356. PubMed ID: 38511374.
    Abstract:
    Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation. 建立小麦-玉米轮作耕地遥感估产模型,可及时准确估测耕地综合粮食产量。本研究以山东曹县冬小麦-夏玉米轮作耕地为对象,利用2018—2019年Sentinel-2影像,通过对比基于QGIS平台的时序特征分类与支持向量机算法,优选提取冬小麦-夏玉米轮作耕地播种面积;通过小麦、玉米植被指数与其统计产量的相关性,筛选敏感植被指数及其生育期,采取牛顿-梯形积分法,获取敏感光谱时段植被指数积分值,构建基于积分值组合的多元线性回归和3种机器学习(随机森林RF、BP神经网络BPNN和支持向量机SVM)模型,并优选最佳估产模型。结果表明: 利用QGIS平台基于时序特征提取冬小麦-夏玉米播种面积精确率达94.6%,其总体精度与Kappa系数比支持向量机算法分别高5.9%和0.12;敏感光谱时段的遥感估产优于单生育期,小麦基于归一化植被指数与比值植被指数及玉米基于增强型植被指数与结构加强色素植被指数的积分组更能有效凝聚光谱信息;植被指数积分最优组合方式为差值,机器学习算法拟合精度高于经验统计模型;最佳估产模型为机器学习算法的差值组-随机森林(DVG-RF)模型(R2为0.843,均方根误差为2.822 kg·hm-2),估产精度达93.4%。本研究探索了利用QGIS平台提取播种面积,对冬小麦-夏玉米轮作耕地粮食估产方法进行了系统研究,建立的多植被指数积分组合模型有效可行,对提升估产精度和效率具有参考价值。.
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