自然资源学报 ›› 2019, Vol. 34 ›› Issue (2): 400-411.doi: 10.31497/zrzyxb.20190215
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:
JING Jin-cheng1,2,3(), JIN Hua-an1, TANG Bin2, LI Ai-nong1(
)
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。
景金城, 靳华安, 唐斌, 李爱农. 山区LAI遥感产品对比分析及影响因子评价[J]. 自然资源学报, 2019, 34(2): 400-411.
JING Jin-cheng, JIN Hua-an, TANG Bin, LI Ai-nong. Intercomparison and evaluation of influencing factors among different LAI products over mountainous areas[J]. JOURNAL OF NATURAL RESOURCES, 2019, 34(2): 400-411.
表2
各影响因子探测结果"
LAI产品 | 交互探测 | ||||
---|---|---|---|---|---|
高程 | 地形起伏度 | 气候区 | 植被类型 | ||
MOIDS C5 | 高程 | 0.4597 | |||
地形起伏度 | 0.5139 | 0.0692 | |||
气候区 | 0.5740 | 0.5297 | 0.4865 | ||
植被类型 | 0.6070 | 0.5290 | 0.6389 | 0.5201 | |
MOIDS C6 | 高程 | 0.5321 | |||
地形起伏度 | 0.6035 | 0.0891 | |||
气候区 | 0.6373 | 0.5837 | 0.5233 | ||
植被类型 | 0.7531 | 0.6748 | 0.7579 | 0.6635 | |
GEOV 1 | 高程 | 0.6414 | |||
地形起伏度 | 0.7079 | 0.1139 | |||
气候区 | 0.7209 | 0.6579 | 0.6040 | ||
植被类型 | 0.7438 | 0.5598 | 0.7409 | 0.5439 | |
GEOV 2 | 高程 | 0.7064 | |||
地形起伏度 | 0.7617 | 0.1063 | |||
气候区 | 0.7743 | 0.6997 | 0.6488 | ||
植被类型 | 0.8052 | 0.6275 | 0.7956 | 0.6165 |
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