浏览全部资源
扫码关注微信
Published： 2019-11 ，
Accepted： 30 September 2018
扫 描 看 全 文
马培培, 李静, 柳钦火, 何彬彬, 赵静. 2019. 中国区域MuSyQ叶面积指数产品验证与分析. 遥感学报, 23(6): 1232–1252
Ma P P, Li J, Liu Q H, He B B and Zhao J. 2019. Multisensor synergistic quantitative leaf area index product of China. Journal of Remote Sensing, 23(6): 1232–1252
对多源遥感数据协同生产的2010年—2015年中国区域1 km空间分辨率5天合成的MuSyQ(Multi-source data Synergized Quantitative remote sensing production system)叶面积指数LAI产品进行验证。参考现有的LAI产品(MODIS c5，GLASS LAI)和中国生态系统研究网络部分农田和森林站点可用的LAI地面测量数据，从时空连续性、时空一致性、精度和准确性等方面对中国区域的MuSyQ LAI产品进行定性和定量分析与评价。结果表明：(1) MuSyQ LAI产品在保证精度优于MODIS产品的情况下，时间分辨率和时空连续性均有提高。MuSyQ LAI与其他LAI产品(MODIS c5，GLASS LAI)在整体上有很好的一致性(RMSE=1.0，RMSE=0.81)，但对常绿阔叶林高值处的描述不稳定；(2) 与LAI地面测量数据相比，MuSyQ LAI产品与地面参考图对比结果较好(最高相关性(
R
2
=0.54)和较低总体误差(RMSE=0.96))，其在阔叶作物生长季高值处有些许低估且在某些阔叶林站点有些高估。整体上，MuSyQ LAI产品呈现出较高的精度，可靠的空间分布和连续稳定的时间分布，且对森林LAI的描述具有更可靠的动态范围。
Leaf Area Index (LAI)
which is defined as the single-sided leaf area per unit horizontal ground area
is a key biophysical variable in land surface processes related to vegetation dynamics
such as photosynthesis
transpiration
and energy balance. This study aims to validate the Multisource data Synergized Quantitative (MuSyQ) remote sensing production system LAI product
which is produced by multisource remote sensing data of China from 2010 to 2015
with 1 km spatial resolution and 5 day time step. Several performance criteria
such as continuity
spatial and temporal consistency
dynamic range of retrievals
statistical analysis per biome type
precision
and accuracy
were evaluated. In this study
the performance of the MuSyQ LAI product was assessed in two ways
namely
(1) by comparing the spatial and temporal characteristics of the MuSyQ LAI product with those of other moderate-resolution LAI products and (2) by comparing MuSyQ LAI values with ground measurement data. The spatial and temporal consistencies of the MuSyQ LAI product were evaluated through intercomparison with reference LAI products (MODIS C5
GLASS) over a network of homogeneous study sites (BELMANIP-2.1 and CERN) in China in 2010—2015. The accuracy of the MuSyQ LAI product was evaluated using numerous available ground reference maps. The MuSyQ LAI product presents a reliable and consistent spatial distribution of dynamic range in spatial distribution
thereby overcoming MODIS’ underestimation of the LAI values and the unreasonable spatiotemporal variations in MODIS LAI products. MuSyQ LAI and MODIS and GLASS LAI have the highest differences (systematic and total) and lowest correlations in evergreen broadleaf forest given the decrease in cloud or snow and other factors by comparing their spatial and temporal characteristics with other products. Qualitative analysis of temporal profiles shows that the MuSyQ LAI product exhibits a consistent seasonal variation with two other LAI products. The temporal profiles of MuSyQ LAI have a reasonable dynamic range and are smoother and more stable than MODIS. The main limitation of the MuSyQ LAI product is the existence of some inconsistencies in the time series at the forest site with evident fluctuations near the peak of the temporal profiles in comparison with the GLASS product. The high values of broadleaf crops during their growth season were slightly underestimated. Comparisons with LAI surface measurements show that the MuSyQ product agrees well with the LAI data (highest correlation
lower systematic error
and overall error) and is slightly underestimated at high values in the broadleaf growing season
where several broadleaf forest sites were slightly overestimated. In summary
the MuSyQ LAI product based on multisource remote sensing data immensely improves the product time resolution to 5 days and the spatiotemporal continuity of LAI products. The MuSyQ LAI product exhibits favorable performance
where the spatial distribution in China is realistic
and the spatial and temporal continuity is greater than other similar products. The temporal profiles reflect the seasonal characteristics of vegetation well
with a slight difference from the ground reference measurement values. The improvement in the spatial and temporal continuity is helpful for applying LAI in dynamic models (such as crop growth and surface process models). However
these validation results are limited by the ground dataset used to quantify the uncertainties attached to the satellite products. Currently
limited data on broadleaf evergreen
deciduous forests
and large regions around the world (e.g.
Asia) are available. Their representativeness can be improved by obtaining multitemporal measurements on various sites
thereby sampling the various biome types found around the world. Furthermore
the uncertainties associated with ground reference data must be quantified to provide a reliable analysis of the validation results.
遥感叶面积指数(LAI)MuSyQ多源协同验证MODISGLASS中国
remote sensingLeaf Area Index(LAI)MuSyQmulti-sensorvalidationMODISGLASSChina
Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L and Leroy M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: part 1: principles of the algorithm. Remote Sensing of Environment, 110(3): 275–286
Baret F, Morissette J T, Fernandes R A, Champeaux J L, Myneni R B, Chen J, Plummer S, Weiss M, Bacour C, Garrigues S and Nickeson J E. 2006. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1794–1803
Baret F, Weiss M, Lacaze R, Camacho F, Makhmara H, Pacholcyzk P and Smets B. 2013. GEOV1: LAI and FAPAR essential climate variables andFCOVER global time series capitalizing over existing products. Part1: principles of development and production. Remote Sensing of Environment, 137: 299–309
Camacho F, Cernicharo J, Lacaze R, Baret F and Weiss M. 2013. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: validation and intercomparison with reference products. Remote Sensing of Environment, 137: 310–329
陈军, 廖安平, 陈晋, 彭舒, 陈利军, 张宏伟. 2017. 全球30 m地表覆盖遥感数据产品-GlobeLand30. 地理信息世界, 24(1): 1–8
Chen J, Liao A P, Chen J, Peng S, Chen L J and Zhang H W. 2017. 30-meter global land cover data product-GlobeLand30. Geomatics World, 24(1): 1–8
Chen J M and Black T A. 1992. Defining leaf area index for non‐flat leaves. Plant Cell and Environment, 15(4): 421–429
Claverie M, Matthews J L, Vermote E F and Justice C O. 2016. A 30+ year AVHRR LAI and FAPAR climate data record: algorithm description and validation. Remote Sensing, 8(3): 263
Deng F, Chen J M, Plummer S, Chen M Z and Pisek J. 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2219–2229
Fang H L, Jiang C Y, Li W J, Wei S S, Baret F, Chen J M, Garcia‐Haro J, Liang S L, Liu R G, Myneni R B, Pinty B, Xiao Z Q and Zhu Z C. 2013. Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: analysis of climatologies and theoretical uncertainties. Journal of Geophysical Research: Biogeosciences, 118(2): 529–548
Fang H L, Wei S S and Liang S L. 2012. Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment, 119: 43–54
Fernandes R, Butson C, Leblanc S and Latifovic R. 2003. Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data. Canadian Journal of Remote Sensing, 29(2): 241–258
Garrigues S, Shabanov N V, Swanson K, Morisette J T, Baret F and Myneni R B. 2008. Intercomparison and sensitivity analysis of leaf area index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands. Agricultural and Forest Meteorology, 148(8/9): 1193–1209
Hu J N, Su Y, Tan B, Huang D, Yang W Z, Schull M, Bull M A, Martonchik J V, Diner D J, Knyazikhin Y and Myneni R B. 2007. Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency. Remote Sensing of Environment, 107(1/2): 334–347
Knyazikhin Y, Martonchik J V, Diner D J, Myneni R B, Verstraete M, Pinty B and Gobron N. 1998. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data. Journal of Geophysical Research: Atmospheres, 103(D24): 32239–32256
李静, 柳钦火, 赵静, 林尚荣, 仲波, 吴善龙. 2017a. 一带一路及其毗邻区域1 km/5 d分辨率叶面积指数数据集(2010). [DOI: 10.3974/geodb.2017.04.03.V1]
Li J, Liu Q H, Zhao J, Lin S R, Zhong B and Wu S L. 2017a. MuSyQ-AMLAI-1km-2010. Global Change Research Data Publishing and Repository, (4)
李静, 柳钦火, 赵静, 林尚荣, 仲波, 吴善龙. 2017b. 一带一路及其毗邻区域1 km/5 d分辨率叶面积指数数据集(2011). [DOI: 10.3974/geodb.2017.04.04.V1]
Li J, Liu Q H, Zhao J, Lin S R, Zhong B and Wu S L. 2017b. MuSyQ-AMLAI-1km-2011. Global Change Research Data Publishing and Repository, (4)
李静, 柳钦火, 赵静, 林尚荣, 仲波, 吴善龙. 2017c. 一带一路及其毗邻区域1 km/5 d分辨率叶面积指数数据集(2012). [DOI: 10.3974/geodb.2017.04.05.V1]
Li J, Liu Q H, Zhao J, Lin S R, Zhong B and Wu S L. 2017c. MuSyQ-AMLAI-1km-2012. Global Change Research Data Publishing and Repository, (4)
李静, 柳钦火, 赵静, 林尚荣, 仲波, 吴善龙. 2017d. 一带一路及其毗邻区域1 km/5 d分辨率叶面积指数数据集(2013). [DOI: 10.3974/geodb.2017.04.07.V1]
Li J, Liu Q H, Zhao J, Lin S R, Zhong B and Wu S L. 2017d. MuSyQ-AMLAI-1km-2013. Global Change Research Data Publishing and Repository, (4)
李静, 柳钦火, 赵静, 林尚荣, 仲波, 吴善龙. 2017e. 一带一路及其毗邻区域1 km/5 d分辨率叶面积指数数据集(2014). [DOI: 10.3974/geodb.2017.04.07.V1]
Li J, Liu Q H, Zhao J, Lin S R, Zhong B and Wu S L. 2017e. MuSyQ-AMLAI-1km-2014. Global Change Research Data Publishing and Repository, (4)
李静, 柳钦火, 赵静, 林尚荣, 仲波, 吴善龙. 2017f. 一带一路及其毗邻区域1 km/5 d分辨率叶面积指数数据集(2015). [DOI: 10.3974/geodb.2017.04.08.V1]
Li J, Liu Q H, Zhao J, Lin S R, Zhong B and Wu S L. 2017f. MuSyQ-AMLAI-1km-2015. Global Change Research Data Publishing and Repository, (4)
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(sup1): 5–33
Masson V, Champeaux J L, Chauvin F, Meriguet C and Lacaze R. 2003. A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate, 16(9): 1261–1282
Myneni R B, Hoffman S, Knyazikhin Y, Privette J L, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith G R, Lotsch A, Friedl M, Morisette J T, Votava P, Nemani R R and Running S W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1/2): 214–231
Pisek J and Chen J M. 2007. Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America. Remote Sensing of Environment, 109(1): 81–94
Quan X W, He B B, Yebra M, Yin C M, Liao Z M and Li X. 2017. Retrieval of forest fuel moisture content using a coupled radiative transfer model. Environmental Modelling and Software, 95: 290–302
Roujean J L and Lacaze R. 2002. Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: a pragmatic method and its validation. Journal of Geophysical Research: Atmospheres, 107(D12): ACL 6-1–ACL 6-14
Sellers P J, Dickinson R E, Randall D A, Betts A K, Hall F G, Berry J A, Collatz G J, Denning A S, Mooney H A, Nobre C A, Sato N, Field C B and Henderson-Sellers A. 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 275(5299): 502–509
Tum M, Günther K, Böttcher M, Baret F, Bittner M, Brockmann C and Weiss M. 2016. Global gap-free MERIS LAI time series (2002-2012). Remote Sensing, 8(1): 69
Weiss M, Baret F, Garrigues S and Lacaze R. 2007. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sensing of Environment, 110(3): 317–331
向阳, 肖志强, 梁顺林, 王锦地, 宋金玲. 2014. GLASS叶面积指数产品验证. 遥感学报, 18(3): 573–596
Xiang Y, Xiao Z Q, Liang S L, Wang J D and Song J L. 2014. Validation of Global LAnd Surface Satellite (GLASS) leaf area index product. Journal of Remote Sensing, 18(3): 573–596
Xiao Z Q, Liang S L and Jiang B. 2017. Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246: 218–230
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
Xiao Z Q, Liang S L, Wang J D, Xiang Y, Zhao X and Song J L. 2016. Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54(9): 5301–5318
徐保东. 2018. 非均质地表叶面积指数反演及产品真实性检验, 北京: 中国科学院大学(中国科学院遥感与数字地球研究所, 70-72)
Xu B D. 2018. Leaf Area Index Retrieval and Product Validation over Heterogeneous Land Surfaces. Beijing: University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, 70-72)
Xu B D, Li J, Liu Q H, Huete A R, Yu Q, Zeng Y L, Yin G F, Zhao J and Yang L. 2016. Evaluating spatial representativeness of station observations for remotely sensed leaf area index products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7): 3267–3282
Xu B D, Li J, Park T, Liu Q H, Zeng Y L, Yin G F, Zhao J, Fan W L, Yang L, Knyazikhin Y and Myneni R B. 2018. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209: 134–151
Yang W Z, Huang D, Tan B, Stroeve J C, Shabanov N V, Knyazikhin Y, Nemani R R and Myneni R B. 2006a. Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the terra MODIS sensor: 2000-2005. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1829–1842
Yin G F, Li J, Liu Q H, Zhong B and Li A N. 2016. Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data. Remote Sensing Letters, 7(8): 771–780
于文涛, 李静, 柳钦火, 曾也鲁, 尹高飞, 赵静, 徐保东. 2016. 中国地表覆盖异质性参数提取与分析. 地球科学进展, 31(10): 1067–1077
Yu W T, Li J, Liu Q H, Zeng Y L, Yin G F, Zhao J and Xu B D. 2016. Extraction and analysis of land cover heterogeneity over China. Advances in Earth Science, 31(10): 1067–1077
曾也鲁, 李静, 柳钦火. 2012. 全球LAI地面验证方法及验证数据综述. 地球科学进展, 27(2): 165–174
Zeng Y L, Li J and Liu Q H. 2012. Global LAI ground validation dataset and product validation framework: a review. Advances in Earth Science, 27(2): 165–174
Zeng Y L, Li J, Liu Q G, Huete A R, Xu B D, Yin G F, Zhao J, Yang L, Fan W L, Wu S B and Yan K. 2016. An Iterative BRDF/NDVI inversion algorithm based on a posteriori variance estimation of observation errors. IEEE Transactions on Geoscience and Remote Sensing, 54(11): 6481–6496
Zhu Z C, Bi J, Pan Y Z, Ganguly S, Anav A, Xu L, Samanta A, Piao S L, Nemani R R and Myneni R B. 2013. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sensing, 5(2): 927–948
相关文章
相关作者
相关机构
京公网安备11010802024621