| 基于微波数据与光学数据集成的机器学习技术在作物产量估算中的应用 |
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| 引用本文： | 张菊,房世波,刘汉湖.基于微波数据与光学数据集成的机器学习技术在作物产量估算中的应用[J].地球信息科学,2021,23(6):1082-1091. |
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| 作者姓名： | 张菊 房世波 刘汉湖 |
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| 作者单位： | 1.成都理工大学 国土资源部地学空间信息技术重点实验室,成都 6100592.中国气象科学研究院生态与农业气象研究所,北京 1000813.南京信息工程大学气象灾害预警协同创新中心,南京 210044 |
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| 基金项目： | 国家重点研发计划项目(2018YFC1506500);国家重点研发计划项目(2019YFC1510200);基本科研业务费项目(2019Z010) |
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| 摘 要： | 各类光学植被指数已成功地应用于各种植被监测与作物产量估算中,但这些指数易受大气状况的影响。由星载微波辐射计得到的植被光学厚度数据（VOD）与植被密度、含水量密切相关,数据可全天候获得,在农业遥感监测中呈现着巨大的潜力。作为来自不同传感器的遥感数据,微波遥感数据与光学遥感数据可以提供不同波长范围内的植被信息。为了更准确地进行作物产量估算,本研究提出将微波遥感数据与光学遥感数据共同应用于冬小麦单产估算中。研究选择L波段微波辐射计SMAP卫星的VOD数据与MODIS的标准归一化植被指数NDVI、增强型植被指数EVI、叶面积指数LAI、光合有效辐射分量FPAR数据作为研究变量,分别使用BP神经网络、GA-BP神经网络和PSO-BP神经网络建立冬小麦产量估算模型。结果表明： 3种神经网络回归模型的P值均小于0.001,通过了显著性检验。GA-BP神经网络回归模型的估算值与真实值在3种神经网络回归模型中表现了最高的相关性（R=0.755）与最低的均方根误差（RMSE=529.145 kg/hm2）,平均绝对误差(MAE=425.168 kg/hm2)和平均相对误差（MRE=6.530%）。为了分析多源遥感数据的结合在作物产量估算中的优势,研究同时构建了仅使用NDVI和LAI,使用NDVI、EVI、LAI、FPAR等光学数据进行冬小麦产量估算的3种GA-BP神经网络回归模型作为对比。结果表明,使用微波遥感数据与光学遥感数建立的GA-BP神经网络回归模型较上述3种作为对比的GA-BP神经网络回归模型的相关系数R值分别提高了0.163,0.229与0.056,均方根误差RMSE分别降低了122.334、158.462和46.923 kg/hm2,使用多源遥感数据的组合可以很好地提高作物产量估算的准确性。
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| 关 键 词： | 遥感 植被光学厚度 光学植被指数 BP神经网络 遗传算法 粒子群算法 冬小麦 产量估算 |
| 收稿时间： | 2020-07-30 |
Machine Learning Approach for Estimation of Crop Yield Combining Use of Optical and Microwave Remote Sensing Data |
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| ZHANG Ju,FANG Shibo,LIU Hanhu.Machine Learning Approach for Estimation of Crop Yield Combining Use of Optical and Microwave Remote Sensing Data[J].Geo-information Science,2021,23(6):1082-1091. |
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| Authors: | ZHANG Ju FANG Shibo LIU Hanhu |
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| Institution: | 1. Key Laboratory of Ministry of Land and Resources for Geoscience Spatial Information Technology, Chengdu 610059, China2. Chinese Academy of Meteorological Sciences, Beijing 100081, China3. Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | Various optical vegetation indices have been widely used in vegetation monitoring and crop yield estimation. However, the temporal availability of optical vegetation indices derived from visible and infrared remote sensing bands is usually a problem in many studies. Currently, the Vegetation Optical Depth (VOD) derived from space-borne microwave radiometers that is unaffected by cloud cover has been found to be proportional to the vegetation density and water content, which shows a great potential in crop monitoring using remote sensing. The vegetation information captured by the two remote sensing approaches is different and complementary since they come from satellite sensors with different spectrum ranges. In this study, we focused on the synergistic use of optical remote sensing data and microwave remote sensing data to estimate wheat yield more accurately. We selected the VOD estimated by the L-band microwave radiometer on board of the SMAP mission, and the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), and the Fraction of Photosynthetically Active Radiation (FPAR) product retrieved from the MODIS satellite sensor as the input variables for winter wheat yield estimation using neural network regression models. We compared the performances of back propagation neural network, genetic algorithm back propagation neural network, and particle swarm optimization algorithm back propagation neural network regression models for estimating wheat yields. The results show that the significance values (P) of the three neural network regression models were all less than 0.001, which indicated that all models have passed the significance test. The genetic algorithm back propagation neural network regression model was the best compared to the other two neural networks regression models, with the highest correlation (R=0.755) and the lowest root mean square error (RMSE=529.145 kg/hm2), mean absolute error (MAE=425.168 kg/hm2), and mean relative error (MRE=6.530%). Moreover, in order to analyze the advantages of different optical vegetation indices in crop yield estimation, we also established another two different genetic algorithm back propagation neural network models that used NDVI and LAI, and that used NDVI, EVI, LAI, and FPAR optical data for winter wheat yield estimation as a comparison. By comparison, the correlation (R) of the model established by the microwave and optical remote sensing data increased by 0.163, 0.229, and 0.056, respectively; while its root mean square error (RMSE) decreased by 122.334, 158.462, and 46.923 kg/hm2, respectively. The combination of multi-source remote sensing data can improve the accuracy of model results to a large extent. |
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| Keywords: | remote sensing vegetation optical depth optical vegetation indices BP neural network genetic algorithm particle swarm optimization algorithm winter wheat yield estimation |
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