地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (11): 1304-1312.doi: 10.3724/SP.J.1047.2015.01304

• 全球卫星气候遥感数据 • 上一篇    下一篇

基于LTDR AVHRR和MODIS观测的全球长时间序列叶面积指数遥感反演

刘洋(), 刘荣高*()   

  1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2015-03-06 修回日期:2015-04-21 出版日期:2015-11-10 发布日期:2015-11-10
  • 通讯作者: 刘荣高 E-mail:liuyang@igsnrr.ac.cn;liurg@igsnrr.ac.cn
  • 作者简介:

    作者简介:刘 洋(1986-),女,甘肃庆阳人,博士,研究方向为定量遥感反演与分析。E-mail:

  • 基金资助:
    气象行业科研专项(GYHY201106014);国家自然科学基金项目(41301354);中国科学院战略性先导科技专项(XDA05090303);资源与环境信息系统国家重点实验室青年人才培养基金项目(08R8B6G0YA)

Retrieval of Global Long-term Leaf Area Index from LTDR AVHRR and MODIS Observations

LIU Yang(), LIU Ronggao*()   

  1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2015-03-06 Revised:2015-04-21 Online:2015-11-10 Published:2015-11-10
  • Contact: LIU Ronggao E-mail:liuyang@igsnrr.ac.cn;liurg@igsnrr.ac.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

叶面积指数是描述土壤-植被-大气之间物质和能量交换的关键参数,获取大区域长时间序列叶面积指数有助于研究气候变化条件下植被的响应及反馈。本文利用MODIS观测和经过重新处理的地表长时间数据集(Land Long Term Data Record)LTDR AVHRR数据,生成了全球1981-2012年叶面积指数数据。算法通过建立二者之间像元级关系,利用高质量MODIS观测约束历史AVHRR数据的反演,这有助于减小2种存在显著差别传感器反演结果的不一致性,也有助于提高AVHRR反演质量。首先算法利用高质量MODIS地表反射率反演2000-2012年叶面积指数,然后利用多年每8 d的LTDR AVHRR地表反射率数据计算简单比植被指数(Simple Ratio,SR),利用SR平均值和MODIS LAI平均值建立像元级AVHRR SR-MODIS LAI关系。在此基础上,实现1981-1999年AVHRR LAI反演,最终得到全球1981-2012年叶面积指数数据。本算法反演的AVHRR和MODIS LAI与全球植被的空间分布吻合,能表征主要生物群系类型的季节变化特征,2个数据集一致性较好,并且与NASA MODIS LAI标准产品(MOD15A2)的空间分布和季节变化曲线吻合较好。

关键词: 植被, 叶面积指数, 全球, 长时间序列, 遥感反演

Abstract:

Leaf area index (LAI) is a primary parameter for charactering the water, carbon and energy exchanges among soil, vegetation and the atmosphere. Global long-term LAI datasets help to understand the response and feedback of vegetation to climate change. In this paper, the global LAI was retrieved during a 32-year period from 1981 to 2012 by utilizing a combination of MODIS measurements and reprocessed long-term data record (LTDR) AVHRR observations. The high-quality MODIS observations were used to constrain the LAI retrieval from historical AVHRR data, by establishing the pixel-by-pixel relationship between them directly. Thus, the inconsistency of LAI derived from these two notably different sensors could be reduced, and the quality of LAI derived from AVHRR data could be improved. Firstly, MODIS LAI series (2000-2012) were generated from high-quality MODIS land surface reflectance based on the GLOBCARBON LAI algorithm. Then, the relationships between AVHRR Simple Ratio (SR) and MODIS LAI were regressed pixel-by-pixel using the multi-year average values of these two data for each 8-day period. After that, the AVHRR LAI was estimated from historical AVHRR observations based on these pixel-level relationships from 1981 to 1999. The retrieved LAI could represent the spatial distribution of global vegetation and the seasonal characteristics of the major biomes. The LAI derived from AVHRR was inter-compared with that from MODIS. The results demonstrate a good consistency between the LAIs from these two different sensors. The comparison with NASA MODIS standard products of MOD15A2 shows that our results are consistent with MOD15A2 in both spatial pattern and seasonal cycle.

Key words: vegetation, leaf area index, global, long-term, remote sensing retrieval