收稿：2016-06-13，
录用：2016-12-3，
纸质出版：2017-05
移动端阅览
植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题，主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大，另一方面在地面验证中地面观测数据与遥感反演数据的物理含义不一致导致的验证方法的系统性误差。本文以黑河流域为研究区，对比验证基于EVI(Enhanced Vegetation Index)时间序列数据提取的MLCD(MODIS global land cover dynamics product)植被遥感物候产品和基于LAI(Leaf Area Index)时间序列数据提取的UMPM(product by universal multi-life-cycle phenology monitoring method)植被遥感物候产品的有效性及精度等。同时，通过验证分析进一步评估基于EVI和LAI时间序列提取的物候特征的差异及特点，探讨由于地面观测植被物候与遥感提取植被物候的物理意义的不一致问题导致的直接验证结果偏差。结果表明: UMPM产品有效性整体高于MLCD产品，但在以草地和灌木为主的稀疏植被区，由于LAI取值精度的原因，UMPM产品存在较多缺失数据，且时空稳定性较低；基于玉米地面观测数据表明，EVI对植被开始生长的信号比LAI更加敏感，更适合提取生长起点，但植被指数易饱和，峰值起点普遍提前，基于LAI提取的峰值起点更加合理。由于地面观测的物候期在后期更加关注果实生长，遥感观测仅关注叶片的生长，遥感定义的峰值终点和生长终点与玉米的乳熟期和成熟期差异较大。
Land surface phenology is of great significance in the fields of global change response
agricultural management
and ecological applications. However
compared with the strong demand for vegetation phenology mapping for global and regional research and applications
the development of remote sensing vegetation phenology products is slower than that of other remote sensing parameters
such as Leaf Area Index (LAI) and Vegetation Index (VI). Although Enhanced Vegetation Index (EVI) and LAI are the most widely used vegetation parameters for remote sensing phenology extraction
this study aims to compare the remote sensing phenology products derived from MODerate-resolution Imaging Spectroradiometer (MODIS) EVI and global land surface satellite product LAI in Heihe River Basin. Moreover
this study aims to assess the difference inphenology information that was extracted from EVI and LAI time series. The validity and accuracy between the MODIS global Land Cover Dynamics product (MLCD) and Universal Multi-life-cycle Phenology Monitoring Method (UMPM) product in the Heihe River Basin were compared. Validity contains the missing product rate and stability. Accuracy was assessed using representative observation sites. The sites were the basis of the two proposed indications (the mean bias and mean absolute bias) for the evaluation of remote sensing phonological metrics. Furthermore
phenology information that was extracted from EVI and LAI products was compared. Then
the variances between ground observations and phenology products were discussed in detail. The validity of UMPM is better than that of MLCD as a whole. However
in sparse vegetation that is mainly composed of shrubs and grasslands
UMPM has more missing data and lower spatial-temporal stability because its precision value is lower than that of EVI. For maize
EVI is more suitable for extracting the start of growing season
whereas LAI performs better for extracting the peak of growing season. However
field observations focus on fruit development during the later period of maize growth
whereas remote sensing phenology detection is specific to leaf development. Both MLCD and UMPM are inconsistent with ground observations after the peak of the growing season. Either MLCD or UMPM has advantages in different vegetation types and various phenological developmental stages. Uniting multisource data can improve the accuracy and validity of remote sensing phenology products. In addition
owing to the coarse spatial resolution of current remote sensing phenology products
it inevitably includes other plants within one square kilometers
which may lead to variability in phenological developmental stages and a weak relationship between remote sensing data and ground observations. Improving the spatial resolution of remote sensing phenology products is significant in the future promotion of their application and development.
Ahl D E, Gower S T, Burrows S N, Shabanov N V, Myneni R B and Knyazikhin Y. 2006. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sensing of Environment, 104(1): 88–95
Barichivich J, Briffa K R, Myneni R B, Osborn T J, Melvin T M, Ciais P, Piao S L and Tucker C. 2013. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO 2 at high northern latitudes from 1950 to 2011. Global Change Biology, 19(10): 3167–3183
陈正华, 麻清源, 王建, 祁元, 李净, 黄春林, 马明国, 杨国靖. 2008. 利用CASA模型估算黑河流域净第一性生产力. 自然资源学报, 23(2): 263–273
Chen Z H, Ma Q Y, Wang J, Qi Y, Li J, Huang C L, Ma M G and Yang G J. 2008. Estimation of Heihe basin net primary productivity using the CASA model. Journal of Natural Resources, 23(2): 263–273
de Beurs K M and Ioffe G. 2014. Use of landsat and MODIS data to remotely estimate Russia’s sown area. Journal of Land Use Science, 9(4): 377–401
Fisher J I and Mustard J F. 2007. Cross-scalar satellite phenology from ground, landsat, and MODIS data. Remote Sensing of Environment, 109(3): 261–273
Ganguly S, Friedl M A, Tan B, Zhang X Y and Verma M. 2010. Land surface phenology from MODIS: characterization of the Collection 5 global land cover dynamics product. Remote Sensing of Environment, 114(8): 1805–1816
Garrity S R, Bohrer G, Maurer K D, Mueller K L, Vogel C S and Curtis P S. 2011. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agricultural and Forest Meteorology, 151(12): 1741–1752
Gonsamo A, Chen J M, Price D T, Kurz W A and Wu C Y. 2012. Land surface phenology from optical satellite measurement and CO 2 eddy covariance technique. Journal of Geophysical Research: Biogeosciences, 117(G3): G03032
Hmimina G, Dufrêne E, Pontailler J Y, Delpierre N, Aubinet M, Caquet B, de Grandcourt A, Burban B, Flechard C, Granier A, Gross P, Heinesch B, Longdoz B, Moureaux C, Ourcival J M, Rambal S, Saint André L and Soudani K. 2013. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: an investigation using ground-based NDVI measurements. Remote Sensing of Environment, 132: 145–158
侯学煜. 1984. 我国的植被类型是怎样分布的? 植物杂志, (6): 21–22
Hou X Y. 1984. What is the distribution of the vegetation types in China? Plants, (6): 21–22
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195–213
Klosterman S T, Hufkens K, Gray J M, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl M A and Richardson A D. 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences, 11(16): 4305–4320
Liang L, Schwartz M D and Fei S L. 2011. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Environment, 115(1): 143–157
Liang S L, Zhao X, Liu S H, Yuan W P, Cheng X, Xiao Z Q, Zhang X T, Liu Q, Cheng J, Tang H R, Qu Y H, Bo Y C, Qu Y, Ren H Z, Yu K and Townshend J. 2013. A long-term global land surface satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth, 6(S1): 5–33
Linderholm H W. 2006. Growing season changes in the last century. Agricultural and Forest Meteorology, 137(1/2): 1–14
Lloyd D. 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing, 11(12): 2269–2279
Lunetta R S, Shao Y, Ediriwickrema J and Lyon J G. 2010. Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data. International Journal of Applied Earth Observation and Geoinformation, 12(2): 81–88
Moulin S, Kergoat L, Viovy N and Dedieu G. 1997. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. Journal of Climate, 10(6): 1154–1170
Myneni R B, Keeling C D, Tucker C J, Asrar G and Nemani R R. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626): 698–702
Piao S L, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J Y, Barr A, Chen A P, Grelle A, Hollinger D Y, Laurila T, Lindroth A, Richardson A D and Vesala T. 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(7174): 49–52
Piao S L, Friedlingstein P, Ciais P, Peylin P, Zhu B and Reichstein M. 2009. Footprint of temperature changes in the temperate and boreal forest carbon balance. Geophysical Research Letters, 36(7): L07404
Piao S L, Wang X H, Ciais P, Zhu B, Wang T and Liu J. 2011. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Global Change Biology, 17(10): 3228–3239
Reed B C, Brown J F, VanderZee D, Loveland T R, Merchant J W and Ohlen D O. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5): 703–714
Root T L, Price J T, Hall K R, Schneider S H, Rosenzweig C and Pounds J A. 2003. Fingerprints of global warming on wild animals and plants. Nature, 421(6918): 57–60
Schwartz M D, Ahas R and Aasa A. 2006. Onset of spring starting earlier across the Northern Hemisphere. Global Change Biology, 12(2): 343–351
Schwartz M D and Hanes J M. 2010. Intercomparing multiple measures of the onset of spring in eastern North America. International Journal of Climatology, 30(11): 1614–1626
Soudani K, le Maire G, Dufrêne E, François C, Delpierre N, Ulrich E and Cecchini S. 2008. Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sensing of Environment, 112(5): 2643–2655
Tan B, Morisette J T, Wolfe R E, Gao F, Ederer G A, Nightingale J and Pedelty J A. 2011. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 361–371
Verger A, Filella I, Baret F and Peñuelas J. 2016. Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sensing of Environment, 178: 1–14
Wagenseil H and Samimi C. 2006. Assessing spatio-temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia. International Journal of Remote Sensing, 27(16): 3455–3471
Wang H, Li X B, Ying G, Wang D D and Long H L. 2006. The methods of simulating vegetation growing season based on NOAA NDVI. Progress in Geography, 25(6): 21–32
Wang X H, Piao S L, Ciais P, Li J S, Friedlingstein P, Koven C and Chen A P. 2011. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proceedings of the National Academy of Sciences of the United States of America, 108(4): 1240–1245
White K, Pontius J and Schaberg P. 2014. Remote sensing of spring phenology in northeastern forests: a comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment, 148: 97–107
White M A, De Beurs K M, Didan K, Inouye D W, Richardson A D, Jensen O P, O’Keefe J, Zhang G, Nemani R R, Van Leeuwen W J D, Brown J F, De Wit A, Schaepman M, Lin X M, Dettinger M, Bailey A S, Kimball J, Schwartz M D, Baldocchi D D, Lee J T and Lauenroth W K. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 15(10): 2335–2359
White M A, Thornton P E and Running S W. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles, 11(2): 217–234
吴炳方, 张峰, 刘成林, 张磊, 罗治敏. 2005. 农作物长势综合遥感监测方法. 遥感学报, 8(6): 498–514
Wu B F, Zhang F, Liu C L, Zhang L and Luo Z M. 2005. An integrated method for crop condition monitoring. Journal of Remote Sensing, 8(6): 498–514
Wu C Y, Gonsamo A, Gough C M, Chen J M and Xu S G. 2014. Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sensing of Environment, 147: 79–88
夏传福, 李静, 柳钦火. 2012. 基于MODIS 叶面积指数的遥感物候产品反演方法. 农业工程学报, 28(19): 103–109
Xia C F, Li J and Liu Q H. 2012. Vegetation phenology monitoring method using time-series MODIS LAI data. Transactions of the Chinese Society of Agricultural Engineering, 28(19): 103–109
Xiao W W, Sun Z G, Wang Q X and Yang Y H. 2013. Evaluating MODIS phenology product for rotating croplands through ground observations. Journal of Applied Remote Sensing, 7(1): 073562
Xiao Z Q, Liang S L, Wang J D, Chen P, Yin X J, Zhang L Q and Song J L. 2014. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 209–223
Xin J F, Yu Z R, van Leeuwen L and Driessen P M. 2002. Mapping crop key phenological stages in the North China Plain using NOAA time series images. International Journal of Applied Earth Observation and Geoinformation, 4(2): 109–117
杨永民, 田静, 荣媛, 龙爱华. 2012. 基于遥感的黑河流域植被物候空间格局提取分析. 遥感技术与应用, 27(2): 282–288
Yang Y M, Tian J, Rong Y and Long A H. 2012. Monitoring spatial patterns of vegetation phenology in Heihe River Basin based on remote sensing. Remote Sensing Technology and Application, 27(2): 282–288
Yu F F, Price K P, Ellis J and Shi P J. 2003. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sensing of Environment, 87(1): 42–54
Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, Reed B C and Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471–475
仲波, 马鹏, 聂爱华, 杨爱霞, 姚延娟, 吕文博, 张航, 柳钦火. 2014. 基于时间序列HJ-1/CCD数据的土地覆盖分类方法. 中国科学: 地球科学, 44(5): 967–977
Zhong B, Ma P, Nie A H, Yang A X, Yao Y J, Lü W B, Zhang H and Liu Q H. 2014. Land cover mapping using time series HJ-1/CCD data. Science China Earth Sciences, 57(8): 1790–1799
Zhong B, Yang A X, Nie A H, Yao Y J, Zhang H, Wu S L and Liu Q H. 2015. Finer resolution land-cover mapping using multiple classifiers and multisource remotely sensed data in the heihe river basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10): 4973–4992
Zhou L M, Tucker C J, Kaufmann R K, Slayback D, Shabanov N V and Myneni R B. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 106(D17): 20069–20083
竺可桢, 宛敏渭. 1999. 物候学. 长沙: 湖南教育出版社
Zhu K Z and Wan M W. 1999. Phenology. Changsha: Hunan Education Press
相关作者
相关机构
京公网安备11010802024621
