山区LAI遥感产品对比分析及影响因子评价
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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。
The validation of LAI products not only provides quantitative quality description,but also plays an important part in the improvement of retrieval algorithm and understanding of uncertainties regarding to land surface process models over rugged surfaces. This study evaluated the performance of MODIS(C5, C6), Geoaldn2(GEOV1, GEOV2) LAI products using intercomparison methods over Southwestern China. The spatiotemporal distribution of integrity and consistencies, such as the percentage of main algorithm, smoothness index,average value during growing season, yearly mean LAI bias and root mean square error(RMSE), respectively, were investigated during the period 2001-2016. Meanwhile, different versions of the same data source LAI products were compared so as to get a clear understanding of improvement about the new one over heterogeneously hilly regions. Lastly,four factors, such as topography(altitude and relief amplitude), vegetation types, and climate regionalization were selected to assess the influence of different underlying surfaces on LAI products using the Geodetector. The results show that spatial and temporal consistency of these LAI products is good over most areas. All LAI products exhibit a higher percentage of good quality data(i. e. successful retrieval) in mountainous areas, and GEOV2 LAI is higher than others. The percentage in altitude and higher relief amplitude area seems to be low. GEOV2 LAI shows smoother temporal profiles than others, and Geoland2 is smoother than MODIS LAI. It is clear that MODIS C5 is smoother than MODIS C6, and GEOV2 is superior to GEOV1. RMSE and yearly mean LAI bias between one and another LAI product are vulnerable to topographic indices, especially to altitude. Q-statistic in Geodetector is smallest related to relief amplitude, biggest to vegetation for MODIS LAI product, and biggest to climate for Geoland2 LAI. Altitude, vegetation, and climate play a dominant role in spatial distribution of LAI. The validation experience demonstrates the importance of topography,vegetation and climate for LAI estimation over mountainous areas. Considerable attention will be paid to the production of higher quality LAI products in topographically complex terrain.
The validation of LAI products not only provides quantitative quality description,but also plays an important part in the improvement of retrieval algorithm and understanding of uncertainties regarding to land surface process models over rugged surfaces. This study evaluated the performance of MODIS(C5, C6), Geoaldn2(GEOV1, GEOV2) LAI products using intercomparison methods over Southwestern China. The spatiotemporal distribution of integrity and consistencies, such as the percentage of main algorithm, smoothness index,average value during growing season, yearly mean LAI bias and root mean square error(RMSE), respectively, were investigated during the period 2001-2016. Meanwhile, different versions of the same data source LAI products were compared so as to get a clear understanding of improvement about the new one over heterogeneously hilly regions. Lastly,four factors, such as topography(altitude and relief amplitude), vegetation types, and climate regionalization were selected to assess the influence of different underlying surfaces on LAI products using the Geodetector. The results show that spatial and temporal consistency of these LAI products is good over most areas. All LAI products exhibit a higher percentage of good quality data(i. e. successful retrieval) in mountainous areas, and GEOV2 LAI is higher than others. The percentage in altitude and higher relief amplitude area seems to be low. GEOV2 LAI shows smoother temporal profiles than others, and Geoland2 is smoother than MODIS LAI. It is clear that MODIS C5 is smoother than MODIS C6, and GEOV2 is superior to GEOV1. RMSE and yearly mean LAI bias between one and another LAI product are vulnerable to topographic indices, especially to altitude. Q-statistic in Geodetector is smallest related to relief amplitude, biggest to vegetation for MODIS LAI product, and biggest to climate for Geoland2 LAI. Altitude, vegetation, and climate play a dominant role in spatial distribution of LAI. The validation experience demonstrates the importance of topography,vegetation and climate for LAI estimation over mountainous areas. Considerable attention will be paid to the production of higher quality LAI products in topographically complex terrain.
引文
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[17]王劲峰,徐成东.地理探测器:原理与展望.地理学报,2017,72(1):116-134.[WANG J F,XU C D.Geodetector:Principle and prospective.Acta Geographica Sinica,2017,72(1):116-134.]
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[21]南希,李爱农,陈昱,等.竖版中国数字山地图(1∶670万)的设计与编制.遥感技术与应用,2016,31(3):451-458.[NAN X,LI A N,CHEN Y,et al.Design and compilation of digital mountain map of China(1∶6700000)in vertical layout.Remote Sensing Technology and Application,2016,31(3):451-458.]
[22]KNYAZIKHIN Y,MARTONCHIK J,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.Journal of Geophysical Research:Atmospheres,1998,103(D24):32257-32275.
[23]YANG W Z,TAN B,HUANG D,et al.MODIS leaf area index products:From validation to algorithm improvement.IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1885-1898.
[24]BARET F,WEISS M,LACAZE R,et al.GEOV1:LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products.Part1:Principles of development and production.Remote Sensing of Environment,2013,137:299-309.
[25]LI Z,TANG H,ZHANG B,et al.Evaluation and intercomparison of MODIS and GEOV1 global leaf area index products over four sites in North China.Sensors(Basel),2015,15(3):6196-6216.
[26]VERGER A,BARET F,WEISS M,et al.GEOV2/VGT:Near real time estimation of global biophysical variables from VEGETATION-P data//Analysis of Multi-Temporal Remote Sensing Images,Multitemp 2013.IEEE,2014.
[27]FRIEDL M A,SULLA-MENASHE D,TAN B,et al.MODIS Collection 5 global land cover:Algorithm refinements and characterization of new datasets.Remote Sensing of Environment,2010,114(1):168-182.
[28]郑景云,尹云鹤,李炳元.中国气候区划新方案.地理学报,2010,65(1):3-12.[ZHENG J Y,YIN Y H,LI B Y.A new scheme for climate regionalization in China.Acta Geographica Sinica,2010,65(1):3-12.]
[29]WEISS M,BARET F,GARRIGUES S,et al.LAI and fAPAR CYCLOPES global products derived from VEGETA-TION.Part 2:Validation and comparison with MODIS collection 4 products.Remote Sensing of Environment,2007,110(3):317-331.
[30]PIAO S L,FANG J Y,JI W,et al.Variation in a satellite-based vegetation index in relation to climate in China.Journal of Vegetation Science,2004,15(2):219-226.
[31]彭飞,孙国栋.1982-1999年中国区域叶面积指数变化及其与气候变化的关系.气候与环境研究,2017,22(2):162-176.[PENG F,SUN G D.Variation of leaf area index in China from 1982 to 1999 and its relationship with climate change.Climatic and Environmental Research,2017,22(2):162-176.]
[32]WANG J F,LI X H,CHRISTAKOS G,et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region,China.International Journal of Geographical Information Science,2010,24(1):107-127.
[33]WANG J F,ZHANG T L,FU B J.A measure of spatial stratified heterogeneity.Ecological Indicators,2016,67:250-256.
[34]GONSAMO A,CHEN J M.Evaluation of the GLC 2000 and NALC 2005 land cover products for LAI retrieval over Canada.Canadian Journal of Remote Sensing,2011,37(3):302-313.
[35]CHEN B,HUANG B,XU B.Constucting a unified framework for multi-source remotely sensed data fusion//Geoscience and Remote Sensing Symposium.IEEE,2016.
[2]CLAVERIE M,MATTHEWS J L,VERMOTE E F,et al.A 30+year AVHRR LAI and FAPAR climate data record:Algorithm description and validation.Remote Sensing,2016,8(3):263.
[3]CHANEY N W,METCALFE P,WOOD E F.Hydroblocks:A field-scale resolving land surface model for application over continental extents.Hydrological Processes,2016,30(20):3543-3559.
[4]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.Remote Sensing of Environment,2002,83(1):214-231.
[5]YAN K,PARK T,YAN G J,et al.Evaluation of MODIS LAI/FPAR product collection 6.Part 1:Consistency and improvements.Remote Sensing,2016,8(5):359.
[6]VERGER A,BARET F,WEISS M.Near real-time vegetation monitoring at global scale.IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2014,7(8):3473-3481.
[7]XIAO Z Q,LIANG S L,WANG J D,et al.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,2014,52(1):209-223.
[8]BARET F,MORISSETTE J T,FERNANDES R A,et al.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,2006,44(7):1794-1803.
[9]MA M G,CHE T,LI X,et al.A prototype network for remote sensing validation in China.Remote Sensing,2015,7(5):5187-5202.
[10]JIN H A,LI A L,BIAN J H,et al.Intercomparison and validation of MODIS and GLASS leaf area index(LAI)products over mountain areas:A case study in Southwestern China.International Journal of Applied Earth Observation and Geoinformation,2017,55:52-67.
[11]CAMACHO F,CERNICHARO J,LACAZE R,et al.GEOV1:LAI,FAPAR essential climate variables and FCOVERglobal time series capitalizing over existing products.Part 2:Validation and intercomparison with reference products.Remote Sensing of Environment,2013,137:310-329.
[12]JIANG C Y,RYU Y,FANG H L,et al.Inconsistencies of interannual variability and trends in long-term satellite leaf area index products.Global Change Biology,2017,23(10):4133-4146.
[13]PASOLLI L,ASAM S,CASTELLI M,et al.Retrieval of leaf area index in mountain grasslands in the Alps from MO-DIS satellite imagery.Remote Sensing of Environment,2015,165:159-174.
[14]杨勇帅,李爱农,靳华安,等.中国西南山区GEOV1、GLASS和MODIS LAI产品的对比分析.遥感技术与应用,2016,31(3):438-450.[YANG Y S,LI A N,JIN H A,et al.Intercomparison among GEOV1,GLASS and MODIS LAIproducts over mountainous area in Southwestern China.Remote Sensing Technology and Application,2016,31(3):438-450.]
[15]FU G,WU J S.Validation of MODIS collection 6 FPAR/LAI in the alpine grassland of the Northern Tibetan Plateau.Remote Sensing Letters,2017,8(9):831-838.
[16]YAN K,PARK T,YAN G,et al.Evaluation of MODIS LAI/FPAR Product Collection 6.Part 2:Validation and Intercomparison.Remote Sensing,2016,8(6):460.
[17]王劲峰,徐成东.地理探测器:原理与展望.地理学报,2017,72(1):116-134.[WANG J F,XU C D.Geodetector:Principle and prospective.Acta Geographica Sinica,2017,72(1):116-134.]
[18]LI X W,XIE Y F,WANG J F,et al.Influence of planting patterns on fluoroquinolone residues in the soil of an intensive vegetable cultivation area in northern China.Science of the Total Environment,2013,458:63-69.
[19]REN Y,DENG L Y,ZUO S D,et al.Quantifying the influences of various ecological factors on land surface temperature of urban forests.Environmental Pollution,2016,216:519-529.
[20]李俊刚,闫庆武,熊集兵,等.贵州省煤矿区植被指数变化及其影响因子分析.生态与农村环境学报,2016,32(3):374-378.[LI J G,YAN Q W,XIONG J B,et al.Variation of vegetation index in coal mining areas in Guizhou province and its affecting factors.Journal of Ecology and Rural Environment,2016,32(3):374-378.]
[21]南希,李爱农,陈昱,等.竖版中国数字山地图(1∶670万)的设计与编制.遥感技术与应用,2016,31(3):451-458.[NAN X,LI A N,CHEN Y,et al.Design and compilation of digital mountain map of China(1∶6700000)in vertical layout.Remote Sensing Technology and Application,2016,31(3):451-458.]
[22]KNYAZIKHIN Y,MARTONCHIK J,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.Journal of Geophysical Research:Atmospheres,1998,103(D24):32257-32275.
[23]YANG W Z,TAN B,HUANG D,et al.MODIS leaf area index products:From validation to algorithm improvement.IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1885-1898.
[24]BARET F,WEISS M,LACAZE R,et al.GEOV1:LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products.Part1:Principles of development and production.Remote Sensing of Environment,2013,137:299-309.
[25]LI Z,TANG H,ZHANG B,et al.Evaluation and intercomparison of MODIS and GEOV1 global leaf area index products over four sites in North China.Sensors(Basel),2015,15(3):6196-6216.
[26]VERGER A,BARET F,WEISS M,et al.GEOV2/VGT:Near real time estimation of global biophysical variables from VEGETATION-P data//Analysis of Multi-Temporal Remote Sensing Images,Multitemp 2013.IEEE,2014.
[27]FRIEDL M A,SULLA-MENASHE D,TAN B,et al.MODIS Collection 5 global land cover:Algorithm refinements and characterization of new datasets.Remote Sensing of Environment,2010,114(1):168-182.
[28]郑景云,尹云鹤,李炳元.中国气候区划新方案.地理学报,2010,65(1):3-12.[ZHENG J Y,YIN Y H,LI B Y.A new scheme for climate regionalization in China.Acta Geographica Sinica,2010,65(1):3-12.]
[29]WEISS M,BARET F,GARRIGUES S,et al.LAI and fAPAR CYCLOPES global products derived from VEGETA-TION.Part 2:Validation and comparison with MODIS collection 4 products.Remote Sensing of Environment,2007,110(3):317-331.
[30]PIAO S L,FANG J Y,JI W,et al.Variation in a satellite-based vegetation index in relation to climate in China.Journal of Vegetation Science,2004,15(2):219-226.
[31]彭飞,孙国栋.1982-1999年中国区域叶面积指数变化及其与气候变化的关系.气候与环境研究,2017,22(2):162-176.[PENG F,SUN G D.Variation of leaf area index in China from 1982 to 1999 and its relationship with climate change.Climatic and Environmental Research,2017,22(2):162-176.]
[32]WANG J F,LI X H,CHRISTAKOS G,et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region,China.International Journal of Geographical Information Science,2010,24(1):107-127.
[33]WANG J F,ZHANG T L,FU B J.A measure of spatial stratified heterogeneity.Ecological Indicators,2016,67:250-256.
[34]GONSAMO A,CHEN J M.Evaluation of the GLC 2000 and NALC 2005 land cover products for LAI retrieval over Canada.Canadian Journal of Remote Sensing,2011,37(3):302-313.
[35]CHEN B,HUANG B,XU B.Constucting a unified framework for multi-source remotely sensed data fusion//Geoscience and Remote Sensing Symposium.IEEE,2016.