[1]廖靖,胡月明,赵理,等.结合数据融合算法的光能利用率模型反演水稻地上部生物量[J].江苏农业学报,2019,(03):594-601.[doi:doi:10.3969/j.issn.1000-4440.2019.03.013]
　LIAO Jing,HU Yue-ming,ZHAO Li,et al.Inversion of aboveground biomass of rice by combining light use efficiency model with data fusion algorithm[J].,2019,(03):594-601.[doi:doi:10.3969/j.issn.1000-4440.2019.03.013]
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结合数据融合算法的光能利用率模型反演水稻地上部生物量()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年03期
页码:
594-601
栏目:
耕作栽培·资源环境
出版日期:
2019-06-30

文章信息/Info

Title:
Inversion of aboveground biomass of rice by combining light use efficiency model with data fusion algorithm
作者:
廖靖1; 2; 3; 4; 胡月明1; 2; 3; 4; 5; 赵理1; 2; 3; 4; 马昊翔1; 2; 3; 4; 王璐1; 2; 3; 4; 张洪亮6
（1.华南农业大学资源环境学院，广东广州510642；2.华南农业大学国土资源部建设用地再开发重点实验室，广东广州510642；3.华南农业大学广东省土地利用与整治重点实验室，广东广州510642；4.华南农业大学广东省土地信息工程技术研究中心，广东广州510642；5.青海大学农牧学院，青海西宁810016；6.贵州科学院，贵州贵阳550001）
Author(s):
LIAO Jing1; 2; 3; 4; HU Yue-ming1; 2; 3; 4; 5; ZHAO Li1; 2; 3; 4; MA Hao-xiang1; 2; 3; 4; WANG Lu1; 2; 3; 4; ZHANG Hong-liang6
(1.College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China;2.Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, South China Agricultural University, Guangzhou 510642, China;3.Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China;4.Guangdong Province Land Information Engineering Technology Research Center, South China Agricultural University, Guangzhou 510642, China;5.College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China;6.Guizhou Academy of Sciences, Guiyang 550001, China)
关键词:
影像融合; 光能利用率模型; 水稻地上部生物量; 遥感反演
Keywords:
image fusion; light use efficiency model; aboveground biomass of rice; remote sensing inversion
分类号:
TP75
DOI:
doi:10.3969/j.issn.1000-4440.2019.03.013
文献标志码:
A
摘要:
水稻作为世界范围内的重要粮食作物，其生长状况与产量信息的快速、精确获取，对保障耕地资源安全与粮食安全具有重要意义。本研究探索结合数据融合算法的光能利用率模型反演水稻地上部生物量，将增强型空间和时间自适应反射融合模型（ESTARFM）预测的水稻关键生长期数据，驱动ECLUE（Eddy covariancelight use efficiency）模型反演水稻地上部生物量，分别验证2个模型的精度。结果显示，ESTARFM算法预测值与真实值的Pearson相关系数为0.668(P＜0001)，对于中型耕地（11～50个Landsat像元），ESTARFM算法预测精度最为理想。ECLUE模型反演的水稻地上部生物量预测值与地面实测值Pearson相关系数为0630(P＜0001)。ECLUE模型驱动数据的空间分辨率与时间分辨率是制约反演结果精度的关键因素。
Abstract:
Rice as an important food crop in the world, so the rapid and accurate acquisition of its growth status and yield information is of great significance to ensure the safety of cultivated land resources and food security. In this study, the enhanced spatial and temporal adaptive reflection fusion model (ESTARFM) was used to predict the key growth data of rice, and the eddy covariancelight use efficiency (ECLUE) model was used to invert rice aboveground biomass. The accuracy of the two models was verified, respectively. The results showed that the Pearson correlation coefficient between the true value and the predicted value of the ESTARFM algorithm was 0668 (P＜0001). However, for mediumsized sultivated land (11-50 Landsat pixels), the ESTARFM algorithm had the best prediction accuracy. Pearson correlation coefficient between the predicted value of the ECLUE model and the measured value of aboveground biomass of rice was 0630 (P＜0001). The spatial resolution and time resolution of the ECLUE modeldriven data are the key factors that constrain the accuracy of inversion results.

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备注/Memo

备注/Memo:
收稿日期：2018-09-25 基金项目：广东省科技计划项目（2017A050501031、2017A040406022）；广州市科技计划项目（201804020034） 作者简介：廖靖（1989-），男，四川成都人 博士研究生，主要从事农用地生产力监测与评价研究。（E-mail）l_j_0817@163.com 通讯作者：胡月明，（E-mail）ymhu163@163.com
更新日期/Last Update: 2019-06-30