地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (2): 268-279.doi: 10.12082/dqxxkx.2018.170068

• 遥感科学与应用技术 • 上一篇    

农田生产力监测中3种多源遥感数据融合方法的对比分析

罗亮1,2(), 闫慧敏1,,2,*(), 牛忠恩1,2   

  1. 1. 中国科学院地理科学与资源研究所,北京 10010
    2. 中国科学院大学,北京 100049
  • 收稿日期:2017-02-24 修回日期:2017-09-27 出版日期:2018-03-02 发布日期:2018-03-05
  • 通讯作者: 闫慧敏 E-mail:luol.15s@igsnrr.ac.cn;yanhm@igsnrr.ac.cn
  • 作者简介:

    作者简介:罗 亮(1991-),男,湖北赤壁人,硕士生,主要研究陆地生态系统生产力。E-mail: luol.15s@igsnrr.ac.cn

  • 基金资助:
    科技部国家重点研发计划项目(2016YFC0503700);国家自然科学基金重点项目(41430861)

Comparative Analysis on Three Multi-Source Remote Sensing Data Fusion Models in Monitoring Farmland Productivity

LUO Liang1,2(), YAN Huimin1,,2,*(), NIU Zhong'en1,2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-02-24 Revised:2017-09-27 Online:2018-03-02 Published:2018-03-05
  • Contact: YAN Huimin E-mail:luol.15s@igsnrr.ac.cn;yanhm@igsnrr.ac.cn
  • Supported by:
    National Key Research and Development Program of China, No.2016YFC0503700;National Key Program of Natural Science Foundation, No.41430861

摘要:

借助多源遥感数据融合技术能够得到高时空分辨率的遥感数据,可以为高精度农业遥感动态监测提供强有力的支持。在诸多融合算法不断发展的情况下,评估每种方法的特点及其适用性,有助于找到最适宜的融合方法,进而应用于农田生产力监测的实践之中。本研究根据高标准农田建设成效评估对高时空分辨率生产力信息的需求,以宁夏灵武市农业综合开发项目区为实验区,采用线性拟合法、时序拟合法、时空融合法3种多源遥感数据融合方法,融合空间分辨率30 m的Landsat遥感数据的空间精度信息与空间分辨率500 m、时间步长8 d的MODIS遥感数据的高时相信息并对比不同方法对于农田生产力的空间格局精细化描述能力、对于农田生产力变化监测的能力以及运算速度的差异。研究结果表明:① 3种融合方法融合的30 mNPP数据均能显示出道路、田埂等线状裸地与田间NPP的差异,但是时序拟合法、时空融合法比线性拟合法更加清晰;在NPP相对均匀的田块内部,时空融合法比时序拟合法更能体现出农田内部均匀度的差异。② 线性拟合法仅适用于农田生产力年季变化的评估,不能用于作物生产力的实时动态监测;时序拟合法和时空融合法适用于农田生产力变化动态监测且时序拟合法适宜于大范围监测。③ 3种方法的计算速度差异显著,线性拟合法计算速率最快,时空融合法计算速率最慢;线性拟合法计算速率分别是时序拟合法和时空融合法的1.5倍和20倍。

关键词: NPP, 线性拟合, 时序拟合, 时空融合, 遥感数据融合

Abstract:

Multi-source remote sensing data fusion models can produce remotely sensed data products with both fine spatial resolution and frequent coverage from multi-source satellite data. It is able to provide strong support for the dynamic monitoring of vegetation with high precision. With the development of different fusion models, evaluating the characteristics and applicability of each model is of great importance for choosing the fittest fusion model in the practice of cropland productivity monitoring. In this paper, we chose Agriculture Comprehensive Exploitation Zone in Lingwu, Ningxia as a focal area. By using linear fitting, time series fusion, and spatial-temporal fusion model to blend the remote sensing data of the Landsat with spatial resolution of 30 m and MODIS with spatial resolution of 500 m, and the time step of 8 days, respectively. Finally, we made a comparison of ability to make a fine description of farmland productivity in spatial pattern, ability of conducting the dynamic monitoring of farmland productivity, and computing speed based on different multi-source remote sensing data fusion models. Results show that: (1) all of the three fusion models can clearly show the differences of NPP between threadiness bare objects such as roads, ridges and cropland. However, time series fusion and spatial-temporal fusion models are clearer than linear fitting model. The spatial-temporal fusion model shows more differences in evenness than time series fusion model for a relatively homogeneous cropland field. (2) Linear fitting model is suitable for estimating the annual variation of farmland productivity only, time series fusion model and spatial-temporal fusion model is suitable for the dynamic monitoring of farmland productivity. What's more, time series fusion model is suitable for monitoring farmland productivity at large or small-scale. (3) There are obvious vatiation in computing speed among the three fusion models. Computing speed of linear fitting model is the fastest, while spatial-temporal fusion model is the slowest. Among them, the computing speed of linear fitting model is 1.5 times faster than time series fusion model and 20 times faster than the spatial-temporal fusion model, respectively.

Key words: NPP, linear fitting, time series fusion, spatial-temporal fusion, remote sensing data fusion