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纸质出版日期： 2024-02-07 ，
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王丹彤，屈永华.2024.黑河中游农作物MODIS叶面积指数产品时间序列精度验证与变化特征分析.遥感学报，28（2）： 359-374
Wang D T and Qu Y H. 2024. Time-series accuracy validation and variation characteristic analysis of MODIS leaf-area index products for crop in the middle reaches of the Heihe River. National Remote Sensing Bulletin， 28（2）：359-374
叶面积指数LAI（Leaf Area Index）是表征植被生长状态的一个重要的冠层结构参数。MODIS LAI产品是全球常用的遥感LAI产品之一。然而，由于地表异质性、数据质量、模型精度等多方面的差异，MODIS LAI产品质量各有不同。基于无线传感器网络的LAINet仪器可以自动获取时间频率更密集的LAI实测数据，为验证卫星遥感LAI产品质量提供了有力支持。本文基于2018年和2019年黑河中游时间序列地面实测LAI数据与高空间分辨率卫星遥感植被指数数据，建立经验回归模型。将该模型反演高空间分辨率卫星遥感LAI作为参考LAI真值，对MODIS LAI产品进行了精度验证与稳定性评价，分析了MODIS LAI与LAINet地面测量的差异原因。结果表明：与Landsat 8参考真值相比，MODIS LAI生长季的质量（RMSE
2018
=1.17，RMSE
2019
=1.14）优于衰落季（RMSE
2018
=1.39，RMSE
2019
=1.84），MODIS LAI总体低估，尤其是生长季后期。时间序列上，MODIS LAI产品能够刻画植被生长和凋落的季节特征，但生长前期波动性要强于后期。与LAINet观测方式的差异是MODIS LAI低估的主要原因，即遥感传感器从太空平台向下观测，LAI值在生长季后期受到叶绿素降低的影响，而LAINet仪器从冠层下向上观测，主要受到冠层间隙率的影响，因此对叶片内色素变化不敏感。对MODIS LAI产品的精度验证与稳定性评价结果表明，可以利用地面实测数据和卫星遥感数据反演时间序列LAI，但是，在使用类似玉米作物的生长季后期数据的时候，需要考虑到MODIS LAI与LAINet LAI观测对象与算法原理的差异。本研究可为MODIS LAI产品的使用者和算法研究者提供参考。
Leaf-Area Index (LAI) is an important canopy structural parameter that accounts for the qualities of the growth state of vegetation. MODIS LAI product is one of the most commonly used remote-sensing LAI products in the world. However
the quality of MODIS LAI products varies with different situations because of variations in surface heterogeneity
data quality
and model accuracy
among others. The LAINet instrument
which is based on the wireless sensor network
can automatically obtain the LAI measured data with more intensive time frequency. It can provide strong support for the validation of satellite remote-sensing LAI products.
This article aims to validate the accuracy and evaluate the stability of MCD15A3H LAI products (Colletion 6) with time-series ground-observation data. The specific objectives include the following: (1) generation of reference products that meet MODIS LAI product validation based on ground network observation time-series data
(2) validation of accuracy of MODIS LAI products based on reference products
(3) evaluation of the time-series stability of MODIS LAI products
and (4) analysis of the reasons for the difference between the MODIS LAI product and the measured LAI.
This work adopts an indirect comparison method
that is
establishing an empirical regression model based on time-series ground-measured LAI and high-spatial-resolution satellite remote-sensing vegetation index to obtain a high-spatial-resolution satellite remote-sensing LAI reference map. The resolution of the reference map is upscaled to the same resolution as those of MODIS LAI products. Finally
we validate the accuracy and evaluate the stability of MODIS LAI products with the upscaled satellite remote-sensing LAI image.
Results show that compared with the reference true value of Landsat 8
the quality of the growing stage (RMSE
2018
=1.17
RMSE
2019
=1.14) is better than that of the senescence stage (RMSE
2018
=1.39
RMSE
2019
=1.84)
and MODIS LAI is generally underestimated to Landsat LAI
especially in the late growing stage. MODIS LAI products can portray the seasonal characteristics in the vegetation growth and falling stages in time series
but the instability in the early period of growth is stronger than that in the later period. The difference in observation methods is the main reason for the underestimation of MODIS LAI
that is
the LAI value of the remote-sensing sensor is affected by the decrease in chlorophyll in the late growing season because the sensor observes from the space platform in the downward direction. Conversely
the LAINet instrument observes from the bottom of the canopy in the upward direction
which is primarily affected by the canopy-gap fraction. However
it is insensitive to changes in the pigment of leaves.
The accuracy validation and stability evaluation results of MODIS LAI products show that the time-series LAI can be retrieved using ground-based data and satellite remote-sensing data. However
considering the difference between the observation objects and algorithm principles of MODIS LAI and LAINet LAI is necessary when using the late-growing-season data of corn crops.
MODIS叶面积指数LAINet精度验证稳定性评价
MODISleaf area indexLAINetaccuracy validationstability evaluation
Adams III W W and Demmig-Adams B. 1994. Carotenoid composition and down regulation of photosystem II in three conifer species during the winter. Physiologia Plantarum, 92(3): 451-458 [DOI: 10.1111/j.1399-3054.1994.tb08835.xhttp://dx.doi.org/10.1111/j.1399-3054.1994.tb08835.x]
Brede B, Gastellu-Etchegorry J P, Lauret N, Baret F, Clevers J G P W, Verbesselt J and Herold M. 2018. Monitoring forest phenology and leaf area index with the autonomous, low-cost transmittance sensor PASTiS-57. Remote Sensing, 10(7): 1032 [DOI: 10.3390/rs10071032http://dx.doi.org/10.3390/rs10071032]
Brown L A, Ogutu B O and Dash J. 2020. Tracking forest biophysical properties with automated digital repeat photography: a fisheye perspective using digital hemispherical photography from below the canopy. Agricultural and Forest Meteorology, 287: 107944 [DOI: 10.1016/j.agrformet.2020.107944http://dx.doi.org/10.1016/j.agrformet.2020.107944]
Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429 [DOI: 10.1111/j.1365-3040.1992.tb00992.xhttp://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x]
Cohen W B, Maiersperger T K, Yang Z Q, Gower S T, Turner D P, Ritts W D, Berterretche M and Running S W. 2003. Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sensing of Environment, 88(3): 233-255 [DOI: 10.1016/j.rse.2003.06.006http://dx.doi.org/10.1016/j.rse.2003.06.006]
Ensminger I, Sveshnikov D, Campbell D A, Funk C, Jansson S, Lloyd J, Shibistova O and Öquist G. 2004. Intermittent low temperatures constrain spring recovery of photosynthesis in boreal Scots pine forests. Global Change Biology, 10(6): 995-1008 [DOI: 10.1111/j.1365-2486.2004.00781.xhttp://dx.doi.org/10.1111/j.1365-2486.2004.00781.x]
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 [DOI: 10.1016/j.rse.2011.12.006http://dx.doi.org/10.1016/j.rse.2011.12.006]
Fu L Z, Qu Y H and Wang J D. 2017. Bias analysis and validation method of the MODIS LAI product. Journal of Remote Sensing, 21(2): 206-217
付立哲, 屈永华, 王锦地. 2017. MODIS LAI产品真实性检验与偏差分析. 遥感学报, 21(2): 206-217 [DOI: 10.11834/jrs.20175336http://dx.doi.org/10.11834/jrs.20175336]
Gamon J A, Huemmrich K F, Wong C Y S, Ensminger I, Garrity S, Hollinger D Y, Noormets A and Peñuelas J. 2016. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proceedings of the National Academy of Sciences, 113(46): 13087-13092 [DOI: 10.1073/pnas.1606162113http://dx.doi.org/10.1073/pnas.1606162113]
Gitelson A A, Peng Y, Arkebauer T J and Schepers J. 2014. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sensing of Environment, 144: 65-72 [DOI: 10.1016/j.rse.2014.01.004http://dx.doi.org/10.1016/j.rse.2014.01.004]
Hu S Y and Zhang W C. 2005. A quality assessment of MODIS LAI product in Heihe and Hanjiang river basins. Remote Sensing Information, (4): 22-27
胡少英, 张万昌. 2005. 黑河及汉江流域MODIS叶面积指数产品质量评价. 遥感信息, (4): 22-27 [DOI: 10.3969/j.issn.1000-3177.2005.04.007http://dx.doi.org/10.3969/j.issn.1000-3177.2005.04.007]
Jia S Z, Ma M G and Yu W P. 2014. Validation of the LAI product in Heihe River Basin. Remote Sensing Technology and Application, 29(6): 1037-1045
家淑珍, 马明国, 于文凭. 2014. 黑河中游LAI产品的真实性检验研究. 遥感技术与应用, 29(6): 1037-1045 [DOI: 10.11873/j.issn.1004-0323.2014.6.1037http://dx.doi.org/10.11873/j.issn.1004-0323.2014.6.1037]
Kanemasu E T. 1974. Seasonal canopy reflectance patterns of wheat, sorghum, and soybean. Remote Sensing of Environment, 3(1): 43-47 [DOI: 10.1016/0034-4257(74)90037-6http://dx.doi.org/10.1016/0034-4257(74)90037-6]
Öquist G and Huner N P A. 2003. Photosynthesis of overwintering evergreen plants. Annual Review of Plant Biology, 54: 329-355 [DOI: 10.1146/ANNUREV.ARPLANT.54.072402.115741http://dx.doi.org/10.1146/ANNUREV.ARPLANT.54.072402.115741]
Ottander C, Campbell D and Öquist G. 1995. Seasonal changes in photosystem II organisation and pigment composition in Pinus sylvestris. Planta, 197(1): 176-183 [DOI: 10.1007/BF00239954http://dx.doi.org/10.1007/BF00239954]
Qu Y H, Han W C, Fu L Z, Li C R, Song J L, Zhou H M, Bo Y C and Wang J D. 2014. LAINet - A wireless sensor network for coniferous forest leaf area index measurement: design, algorithm and validation. Computers and Electronics in Agriculture, 108: 200-208 [DOI: 10.1016/j.compag.2014.08.003http://dx.doi.org/10.1016/j.compag.2014.08.003]
Qu Y H, Wang J D, Dong J and Jiang F B. 2012. Design and experiment of crop structural parameters automatic measurement system. Transactions of the CSAE, 28(2): 160-165
屈永华, 王锦地, 董健, 姜富斌. 2012. 农作物冠层结构参数自动测量系统设计与试验. 农业工程学报, 28(2): 160-165 [DOI: 10.3969/j.issn.1002-6819.2012.02.028http://dx.doi.org/10.3969/j.issn.1002-6819.2012.02.028]
Sun C X, Liu L Y and Guan L L. 2013. Validation of the GLASS LAI products in Xilinhot grassland. Remote Sensing Technology and Application, 28(6): 949-954
孙晨曦, 刘良云, 关琳琳. 2013. 内蒙古锡林浩特草原GLASS LAI产品的真实性检验. 遥感技术与应用, 28(6): 949-954
Tan B, Hu J N, Zhang P, Huang D, Shabanov N, Weiss M, Knyazikhin Y and Myneni R B. 2005. Validation of moderate resolution imaging spectroradiometer leaf area index product in croplands of Alpilles, France. Journal of Geophysical Research: Atmospheres, 110(D1): D01107 [DOI: 10.1029/2004JD004860http://dx.doi.org/10.1029/2004JD004860]
Tang Y L, Huang J F, Wang X Z and Cai S H. 2008. Study on hyper spectral characteristics of corn leaves and their correlation to chrolophyll and carotenoid. Journal of Maize Sciences, 16(2): 71-76
唐延林, 黄敬峰, 王秀珍, 蔡绍洪. 2008. 玉米叶片高光谱特征及与叶绿素、类胡萝卜素相关性的研究. 玉米科学, 16(2): 71-76
Xie R Z, Zhou S L, Wang J H and Jiang H R. 2006. Comparison of the leaf hyperspectral reflectance and absorptance in relation to the leaf pigment contents in corn plants. Journal of Maize Sciences, 14(3): 70-73
谢瑞芝, 周顺利, 王纪华, 蒋海荣. 2006. 玉米叶片高光谱反射率、吸收率与色素含量的关系比较分析. 玉米科学, 14(3): 70-73 [DOI: 10.13597/j.cnki.maize.science.2006.03.021http://dx.doi.org/10.13597/j.cnki.maize.science.2006.03.021]
Xu X J, Du H Q, Zhou G M and Li P H. 2016. Method for improvement of MODIS leaf area index products based on pixel-to-pixel correlations. European Journal of Remote Sensing, 49(1): 57-72 [DOI: 10.5721/EuJRS20164904http://dx.doi.org/10.5721/EuJRS20164904]
Yang F, Li Z W, Bao Y H, Li X Y, Zhang B H and Xin X P. 2016. Comparation of different LAI products in hulunber meadow steppe. Transactions of the Chinese Society of Agricultural Engineering, 32(S1): 153-160
杨帆, 李振旺, 包玉海, 李晓宇, 张保辉, 辛晓平. 2016. 不同叶面积指数遥感产品在呼伦贝尔草甸草原的应用对比. 农业工程学报, 32(S1): 153-160 [DOI: 10.11975/j.issn.1002-6819.2016.z1.022http://dx.doi.org/10.11975/j.issn.1002-6819.2016.z1.022]
Yang W Z, Tan B, Huang D, Rautiainen M, Shabanov N V, Wang Y, Privette J L, Huemmrich K F, Fensholt R, Sandholt I, Weiss M, Ahl D E, Gower S T, Nemani R R, Knyazikhin Y and Myneni R B. 2006. MODIS leaf area index products: from validation to algorithm improvement. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1885-1898 [DOI: 10.1109/TGRS.2006.871215http://dx.doi.org/10.1109/TGRS.2006.871215]
Yu L H, Shang J L, Cheng Z Q, Gao Z B, Wang Z X, Tian L, Wang D T, Che T, Jin R, Liu J G, Dong T F and Qu Y H. 2020. Assessment of cornfield LAI retrieved from multi-source satellite data using continuous field LAI measurements based on a wireless sensor network. Remote Sensing, 12(20): 3304 [DOI: 10.3390/rs12203304http://dx.doi.org/10.3390/rs12203304]
Zhou S L, Xie R Z, Jiang H R and Wang J H. 2006. Selection of the wavelengths of peak values in analyzing leaf water status by using leaf reflectance, transmittance and absorptance in corn plants. Transactions of the Chinese Society of Agricultural Engineering, 22(5): 28-31
周顺利, 谢瑞芝, 蒋海荣, 王纪华. 2006. 用反射率、透射率和吸收率分析玉米叶片水分含量时的峰值波长选择. 农业工程学报, 22(5): 28-31 [DOI: 10.3321/j.issn:1002-6819.2006.05.007http://dx.doi.org/10.3321/j.issn:1002-6819.2006.05.007]
Zhu X C, Yuan G F, Yi X B and Du T. 2014. Leaf area index inversion of riparian forest in the lower basin of Tarim River based on Landsat 8 OLI images. Arid Land Geography, 37(6): 1248-1256
朱绪超, 袁国富, 易小波, 杜涛. 2014. 基于Landsat 8 OLI影像的塔里木河下游河岸林叶面积指数反演. 干旱区地理, 37(6): 1248-1256 [DOI: 10.13826/j.cnki.cn65-1103/x.2014.06.019http://dx.doi.org/10.13826/j.cnki.cn65-1103/x.2014.06.019]
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