自然资源学报 ›› 2019, Vol. 34 ›› Issue (2): 400-411.doi: 10.31497/zrzyxb.20190215

• 资源评价 • 上一篇    下一篇

山区LAI遥感产品对比分析及影响因子评价

景金城1,2,3(), 靳华安1, 唐斌2, 李爱农1()   

  1. 1. 中国科学院水利部成都山地灾害与环境研究所数字山地与遥感应用中心,成都 610041
    2. 成都理工大学地球科学学院,成都 610059
    3. 四川省煤炭设计研究院,成都 610031
  • 收稿日期:2018-08-03 修回日期:2018-12-15 出版日期:2019-02-28 发布日期:2019-02-28
  • 作者简介:

    作者简介:景金城(1991- ),男,甘肃灵台人,硕士,主要从事3S技术集成和定量遥感产品验证工作研究。E-mail: jingjincheng27@163.com

  • 基金资助:
    国家重点研发计划项目(2016YFA0600103);国家自然科学基金项目(41671376,41631180);四川省应急测绘与防灾减灾工程技术研究中心开放基金(K2015B001)

Intercomparison and evaluation of influencing factors among different LAI products over mountainous areas

JING Jin-cheng1,2,3(), JIN Hua-an1, TANG Bin2, LI Ai-nong1()   

  1. 1. Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, CAS, Chengdu 610041, China
    2. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
    3. Sichuan Provincial Coal Design & Research Institute, Chengdu 610031, China;
  • Received:2018-08-03 Revised:2018-12-15 Online:2019-02-28 Published:2019-02-28
  • Contact: LI Ai-nong E-mail:jingjincheng27@163.com;ainongli@imde.ac.cn

摘要:

叶面积指数(LAI)遥感产品对比分析不仅能够提供产品质量的定量化描述信息,还对产品反演算法优化和认知陆面过程模型的不确定性具有重要意义。为研究不同LAI产品在地形复杂、景观破碎的中国西南地区的表现差异,选择2001-2016年间MODIS(C5、C6)和Geoland2(GEOV1、GEOV2)LAI产品,从时空完整性和连续性方面,对比分析不同LAI产品在山区的变化情况,并比较同源不同版本LAI产品在山区的改进效果。选择地形(如高程、地形起伏度)、植被类型、气候因子,使用地理探测器评估LAI遥感产品受不同下垫面的影响程度。结果表明:(1)高海拔和高地形起伏度区域LAI产品质量较差;(2) MODIS LAI产品连续性整体性差于Geoland2,MODIS LAI均值在局部地区高于Geoland2,同源产品LAI差值低于非同源产品;(3) MODIS C6主算法反演比例低于C5,时间连续性优于C5,GEOV2反演成功率和连续性优于GEOV1;(4)各因子对山区LAI变化的贡献量q:地形起伏度最小,MODIS产品受植被类型影响最大,Geoland2产品受高程和气象数据影响较大。通过LAI产品对比分析,能够准确认知山区各因素对LAI产品精度的影响程度,可为山区生产高质量的LAI产品提供借鉴。

关键词: 叶面积指数, 西南地区, 对比分析, 地理探测器, 空间异质性

Abstract:

The validation of LAI products not only provides quantitative quality description, but also plays an important part in the improvement of retrieval algorithm and understanding of uncertainties regarding to land surface process models over rugged surfaces. This study evaluated the performance of MODIS (C5, C6), Geoaldn2 (GEOV1, GEOV2) LAI products using intercomparison methods over Southwestern China. The spatiotemporal distribution of integrity and consistencies, such as the percentage of main algorithm, smoothness index, average value during growing season, yearly mean LAI bias and root mean square error (RMSE), respectively, were investigated during the period 2001-2016. Meanwhile, different versions of the same data source LAI products were compared so as to get a clear understanding of improvement about the new one over heterogeneously hilly regions. Lastly, four factors, such as topography (altitude and relief amplitude), vegetation types, and climate regionalization were selected to assess the influence of different underlying surfaces on LAI products using the Geodetector. The results show that spatial and temporal consistency of these LAI products is good over most areas. All LAI products exhibit a higher percentage of good quality data (i. e. successful retrieval) in mountainous areas, and GEOV2 LAI is higher than others. The percentage in altitude and higher relief amplitude area seems to be low. GEOV2 LAI shows smoother temporal profiles than others, and Geoland2 is smoother than MODIS LAI. It is clear that MODIS C5 is smoother than MODIS C6, and GEOV2 is superior to GEOV1. RMSE and yearly mean LAI bias between one and another LAI product are vulnerable to topographic indices, especially to altitude. Q-statistic in Geodetector is smallest related to relief amplitude, biggest to vegetation for MODIS LAI product, and biggest to climate for Geoland2 LAI. Altitude, vegetation, and climate play a dominant role in spatial distribution of LAI. The validation experience demonstrates the importance of topography, vegetation and climate for LAI estimation over mountainous areas. Considerable attention will be paid to the production of higher quality LAI products in topographically complex terrain.

Key words: leaf area index, Southwestern China, intercomparison, Geodetector, spatial heterogeneity