地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (11): 1304-1312.doi: 10.3724/SP.J.1047.2015.01304
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:
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:
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。
刘洋, 刘荣高. 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[J]. 地球信息科学学报, 2015, 17(11): 1304-1312.DOI:10.3724/SP.J.1047.2015.01304
LIU Yang,LIU Ronggao. Retrieval of Global Long-term Leaf Area Index from LTDR AVHRR and MODIS Observations[J]. Journal of Geo-information Science, 2015, 17(11): 1304-1312.DOI:10.3724/SP.J.1047.2015.01304
[1] |
Chen J M, Black T A.Defining Leaf-Area Index for non-flat leaves[J]. Plant Cell and Environment, 1992,15(4):421-429.
doi: 10.1111/j.1365-3040.1992.tb00992.x |
[2] |
Myneni R B, Hoffman S, Knyazikhin Y, et al.Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data[J]. Remote Sensing of Environment, 2002,83(1-2):14-231.
doi: 10.1016/S0034-4257(02)00074-3 |
[3] | GCOS.GCOS, Systematic observation requirements for satellite-based products for climate. WMO/TD No.1338. September 2006, 103. (available at ). |
[4] |
Masson V, Champeaux J L, Chauvin F, et al.A global database of land surface parameters at 1-km resolution in meteorological and climate models[J]. Journal of Climatology, 2003,16(9):1261-1282.
doi: 10.1175/1520-0442-16.9.1261 |
[5] |
Los S O, Collatz G J, Sellers P J, et al.A global 9-yr biophysical land surface dataset from NOAA AVHRR data[J]. Journal of Hydrometeorology, 2000,1(2):183-199.
doi: 10.1175/1525-7541(2000)001<0183:AGYBLS>2.0.CO;2 |
[6] |
Baret F, Hagolle O, Geiger B, et al.LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION - Part 1: Principles of the algorithm[J]. Remote Sensing of Environment, 2007,110(3):275-286.
doi: 10.1016/j.rse.2007.02.018 |
[7] |
Deng F, Chen J M, Plummer S, et al.Algorithm for global leaf area index retrieval using satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,44(8):2219-2229.
doi: 10.1109/TGRS.2006.872100 |
[8] |
Chen J M, Pavlic G, Brown L, et al.Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J]. Remote Sensing of Environment, 2002,80(1):165-184.
doi: 10.1016/S0034-4257(01)00300-5 |
[9] |
Bacour C, Baret F, Beal D, et al.Neural network estimation of LAI, fAPAR, fCover and LAIxC(ab), from top of canopy MERIS reflectance data: Principles and validation[J]. Remote Sensing of Environment, 2006,105(4):313-325.
doi: 10.1016/j.rse.2006.07.014 |
[10] | Diner D J, Martonchik J V, Borel C, et al.MISR Level 2 Surface Retrieval Algorithm Theoretical Basis[R]. Pasadena, California: Jet Propulsion Laboratory, California Institute of Technology, 2008. |
[11] |
Zhao M, Running S W.Drought-induced reduction in global terrestrial vet primary production from 2000 through 2009[J]. Science, 2010,329(5994):940-943.
doi: 10.1126/science.1192666 pmid: 20724633 |
[12] |
Leuning R, Zhang Y Q, Rajaud A, et al.A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation[J]. Water Resources Research, 2008,44(10):1-17.
doi: 10.1029/2007WR006562 |
[13] |
Ganguly S, Schull M A, Samanta A, et al.Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory[J]. Remote Sensing of Environment, 2008,112(12):333-4343.
doi: 10.1016/j.rse.2008.07.014 |
[14] | Liu Y, Liu R G, Chen J M.Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data[J]. Journal of Geophysical Research-Biogeosciences, 2012,117(G04003):1-14. |
[15] | Pedelty J, Devadiga S, Masuoka E, et al.Generating a long-term land data record from the AVHRR and MODIS instrument[C]. IGARRS '07, Barcelona, Spain, 2007:23-27. |
[16] |
Vermote E F, Kaufman Y J.Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views[J]. International Journal of Remote Sensing, 1995,16(13):2317-2340.
doi: 10.1080/01431169508954561 |
[17] |
He L, Chen J M, Pisek J, et al.Global clumping index map derived from the MODIS BRDF product[J]. Remote Sensing of Environment, 2012,119:118-130.
doi: 10.1016/j.rse.2011.12.008 |
[18] |
Liu R G, Chen J M, Liu J, et al.Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research[J]. Journal of Environmental Management, 2007,85(3):649-658.
doi: 10.1016/j.jenvman.2006.04.023 pmid: 17123698 |
[19] |
Knyazikhin Y, Martonchik J V, Myneni R B, et al.Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data[J]. Journal of Geophysical Research-Atmosphere, 1998,103(D24):32257-32275.
doi: 10.1029/98JD02462 |
[20] |
Tucker C J, Pinzon J E, Brown M E, et al.An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data[J]. International Journal of Remote Sensing, 2005,26(20):4485-4498.
doi: 10.1080/01431160500168686 |
[21] |
Chen J M.Evaluation of vegetation indices and a modified simple ratio for boreal applications[J]. Canadian Journal of Remote Sensing, 1996,22:229-242.
doi: 10.1080/07038992.1996.10855178 |
[22] |
Los S O, North P R J, Grey W M F, et al. A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry[J]. Remote Sensing of Environment, 2005,99:400-411.
doi: 10.1016/j.rse.2005.08.017 |
[23] |
Liu R G, Liu Y.Generation of new cloud masks from MODIS land surface reflectance products[J]. Remote Sensing of Environment, 2013,133:21-37.
doi: 10.1016/j.rse.2013.01.019 |
[24] |
Lu X L, Liu R G, Liu J Y, et al.Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products[J]. Photogrammetric Engineering & Remote Sensing, 2007,73(10):1129-1140.
doi: 10.1016/j.media.2007.07.004 |
[1] | 谢聪慧, 吴世新, 张晨, 孙文涛, 何海芳, 裴韬, 罗格平. 基于谱系聚类的全球各国新冠疫情时间序列特征分析[J]. 地球信息科学学报, 2021, 23(2): 236-245. |
[2] | 薛浩男, 张雪英, 吴明光, 曹天阳. 基于新闻数据的新冠疫情事件下“全球-中国”国际关系变化分析方法[J]. 地球信息科学学报, 2021, 23(2): 351-363. |
[3] | 王艳杰, 王卷乐, 魏海硕, Altansukh Ochir, Davaadorj Davaasuren, Sonomdagva Chonokhuu. 基于稀疏样点的蒙古国产草量估算方法研究[J]. 地球信息科学学报, 2020, 22(9): 1814-1822. |
[4] | 陈辉, 厉青, 王中挺, 马鹏飞, 李营, 赵爱梅. 一种基于FY3D/MERSI2的AOD遥感反演方法[J]. 地球信息科学学报, 2020, 22(9): 1887-1896. |
[5] | 陈如如, 胡中民, 李胜功, 郭群. 不同数据源归一化植被指数在中国北方草原区的应用比较[J]. 地球信息科学学报, 2020, 22(9): 1910-1919. |
[6] | 王学文, 赵庆展, 韩峰, 马永建, 龙翔, 江萍. 机载多光谱影像语义分割模型在农田防护林提取中的应用[J]. 地球信息科学学报, 2020, 22(8): 1702-1713. |
[7] | 蒋世豪, 江洪, 陈慧. 基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估[J]. 地球信息科学学报, 2020, 22(8): 1725-1734. |
[8] | 龚围, 李丽, 柳钦火, 辛晓洲, 彭志晴, 邬明权, 牛铮, 田海峰. “一带一路”区域水电站工程生态环境影响遥感监测[J]. 地球信息科学学报, 2020, 22(7): 1424-1436. |
[9] | 程维明. 现代地貌制图学的发展与展望——纪念陈述彭先生诞辰100周年[J]. 地球信息科学学报, 2020, 22(4): 688-696. |
[10] | 赵尚民, 程维明, 蒋经天, 沙文娟. 资源三号卫星DEM数据与全球开放DEM数据的误差对比[J]. 地球信息科学学报, 2020, 22(3): 370-378. |
[11] | 张兴航, 张百平, 王晶, 姚永慧, 余付勤. 神农架林区植被分布与地形的关系研究[J]. 地球信息科学学报, 2020, 22(3): 482-493. |
[12] | 郭云开, 张晓炯, 许敏, 刘雨玲, 钱佳, 章琼. 路域植被等效水厚度估算模型研究[J]. 地球信息科学学报, 2020, 22(2): 308-315. |
[13] | 李玉, 张黎明, 张兴国, 王昊, 张鑫港. 基于气象监测网络的森林火险快速预警模型[J]. 地球信息科学学报, 2020, 22(12): 2317-2325. |
[14] | 杨丹, 周亚男, 杨先增, 郜丽静, 冯莉. LSTM支持下时序Sentinel-1A数据的太白山区植被制图[J]. 地球信息科学学报, 2020, 22(12): 2445-2455. |
[15] | 丁忠昊, 宋立生, 徐同仁, 白岩, 刘绍民, 马明国, 徐自为. 不同下垫面DTD模型与TSEB模型比较[J]. 地球信息科学学报, 2020, 22(11): 2152-2165. |
|