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Bias analysis and validation method of the MODIS LAI product

  • role： First author第一作者
  • Affiliation：

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, State Key Laboratory of Remote Sensing Science, Beijing Normal University and the Institute of Remote Sensing, Beijing 100875, China

  • Email：fulizhe0312@163.com
  • Introduction：付立哲(1988—　)，男，硕士研究生。研究方向为定量遥感。E-mail：fulizhe0312@163.com
FU Lizhe,  
  • role： Corresponding author通信作者
  • Affiliation：

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, State Key Laboratory of Remote Sensing Science, Beijing Normal University and the Institute of Remote Sensing, Beijing 100875, China

  • Email：qyh@bnu.edu.cn
  • Introduction：屈永华(1972—　)，男，副教授，研究方向为农业遥感，无线传感器网络。E-mail：qyh@bnu.edu.cn
QU Yonghua*,  
  • Affiliation：

    Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, State Key Laboratory of Remote Sensing Science, Beijing Normal University and the Institute of Remote Sensing, Beijing 100875, China

WANG Jindi

Resümee

An assessment of the MODIS Leaf Area Index (LAI) product shows a clear focus on the consistency in value and temporal trend between remote sensing products and the “ground truth.” However, few studies have comprehensively analyzed the bias sources and quantified the contribution of different biases on the global deviation. The current study evaluates the MODIS LAI product by analyzing the MODIS LAI bias in terms of three aspects: algorithm, reflectance data, and clumping effect. We then quantify the individual influence. The bias analysis and validation of the MODIS LAI product are conducted by utilizing measured data of corn plants in a field in Huailai County, Hebei Province. Results indicate an evident underestimation of the MODIS LAI by as much as 34.14% in this area. The mean LAI values of the reference LAI and MODIS LAI are 3.53 m2/m2 and 2.33 m2/m2, respectively. The reflectance data in the bias analysis has the most significant effect on the total deviation. The bias caused by the difference between the MODIS reflectance and Landsat 8 OLI reflectance is 57.50% of the total; the clumping effect accounts for 28.33% of the total; and the bias caused by algorithm is the smallest at only 14.17% of the total. The proposed method positively influences the validation of the remote sensing product and uncertainties analysis.

Schlüsselwort

Leaf Area Index (LAI);MODIS LAI products;validation;bias analysis;clumping effect

References

  1. 1.
    Baret F and Guyot G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2/3): 161–173
  2. 2.
    Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves.Plant, Cell and Environment, 15(4): 421–429
  3. 3.
    Chen J M, Pavlic G, Brown L, Cihlar J, Leblanc S G, White H P, Hall R J, Peddle D R, King D J,Trofymow J A, Swift E, Van der Sanden J and Pellikka PKE. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of environment, 80(1): 165–184
  4. 4.
    Fang X Q, Zhang W C and Liu S C. 2004. The estimation ofLAI in Heihe river basin using remotely sensed data. Remote Sensing for Land and Resources, (1): 27–31
  5. 5.
    Garrigues S, Allard D, Baret F and Weiss D. 2006.Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105(4): 286–298
  6. 6.
    Hu S Y and Zhang W C. 2005. A quality assessment of MODIS LAI product in Heihe and Hanjiang river basins. Remote SensingInformation, (4): 22–27
  7. 7.
    Jin H, Li A N, Bian J H, Lei G B, Tan J B and Xia H M. 2014. Validation of MODISglobal LAI products in forested terrain //Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).Quebec City:IEEE: 3053–3056 [DOI: 10.1109/IGARSS.2014.6947121]
  8. 8.
    Li Z W, Tang H, Xin X P, Zhang B H and Wang D L. 2014. Assessmentof the MODIS LAI product using ground measurement data and HJ-1A/1B imagery in the meadow steppe of Hulunber, China. Remote Sensing, 6(7): 6242–6265
  9. 9.
    Liu L Y. 2014. Simulation and correction of spatial scaling effects for leaf area index. Journal of Remote Sensing, 18(6): 1158–1168
  10. 10.
    Morisette J T, Baret F, Privette J L, Myneni R B, Nickeson J E, Garrigues S, Shabanov N V, Weiss M, Fernandes R A, Leblanc S G, Kalacska M, Sanchez-Azofeifa G A, Chubey M, Rivard B, Stenberg P, Rautiainen M, Voipio P, Manninen T, Pilant A N, Lewis T E, Iiames J S, Colombo R, Meroni M, Busetto L, Cohen W B, Turner D P, Warner E D, Petersen G W, Seufert G and Cook R. 2006.Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1804–1817
  11. 11.
    Pandya M R, Singh R P, Chauhari K N, Bairagi G D, Sharma R, Dadhwal V K and Parihar J S. 2006. Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1858–1865
  12. 12.
    Privette J L, Myneni R B, Knyazikhin Y, Mukelabai M, Roberts G, Tian Y, Wang Y and Leblanc S G. 2002. Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83(1/2): 232–243
  13. 13.
    Qu Y H, Wang J D, Dong J and Jiang F B. 2012. Design and experimentof crop structural parameters automatic measurement system. Transaction of the CSAE, 28(2): 160–165
  14. 14.
    Qu Y H. 2013. LAINet Datasets of Cron at Experiment Station at Huaiai, Hebei Province. Beijing: Beijing Normal University
  15. 15.
    Qu Y H, Fu L Z, Han W C, Zhu Y Q and Wang J D. 2014a. MLAOS: amulti-point linear array of optical sensors for coniferous foliage clumping index measurement. Sensors, 14(5): 9271–9289
  16. 16.
    Qu Y H, Han W C, Fu L Z, Li C R, Song J L, Zhou H M, Bo Y C andWang J D. 2014b. LAINet – A wireless sensor network for coniferous forest leaf area index measurement: design, algorithm and validation. Computers and Electronics in Agriculture, 108: 200–208
  17. 17.
    Qu Y H, Zhu Y Q, Han W C, Wang J D and Ma M G. 2014c. Crop leafarea index observations with a wireless sensor network and its potential for validating remote sensing products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2): 431–444
  18. 18.
    Ryu Y, Nilson T, Kobayashi H, Sonnentag O, Law B E and Baldocchi DD. 2010. On the correct estimation of effective leaf area index: does it reveal information on clumping effects. Agricultural and Forest Meteorology, 150(3): 463–472
  19. 19.
    Serbin S P, Ahl D E and Gower S T. 2013. Spatial and temporal validation of the MODIS LAI and FPAR products across a boreal forest wildfire chronosequence. Remote Sensing of Environment, 133: 71–84
  20. 20.
    Sun C X, Liu L Y, Guan L L, Jiao Q J and Peng D L. 2014. Validation and error analysis ofthe MODIS LAI product in Xilinhot grassland. Journal of Remote Sensing, 18(3): 518–536
  21. 21.
    Tian Y H, Woodcock C E, Wang Y J, Privette J L, Shabanov N V, ZhouL M, Zhang Y, Buermann W, Dong J R,Veikkanen B,Häme T, Andersson K, Ozdogan M, Knyazikhin Y and Myneni R B. 2002. Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment. Remote Sensing of Environment, 83(3): 414–430
  22. 22.
    Wang Y J, Woodcock C E, Buermann W, Stenberg P, Voipio P, Smolander P, Häme T, Tian Y H, Hu J N,Knyazikhin Y and Myneni R B. 2004. Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote sensing of Environment, 91(1): 114–127
  23. 23.
    Wu X D, Xiao Q, Wen J G, Liu Q, Peng J J and Li X W. 2014. Advancesin uncertainty analysis for the validation of remote sensing products: take leaf area index for example. Journal of Remote Sensing, 18(5): 1011–1023
  24. 24.
    Yang F, Sun J L, Zhang B, Yao Z F, Wang Z M, Wang J L and Yue X F. 2010. Assessment of MODIS LAI product accuracy based on the PROSAIL model, TM and field measurements. Transaction of the CSAE, 26(4): 192–197
  25. 25.
    Yang W Z, Tan B, Huang D, Rautiainen M, Shabanov N V, Wang Y J, Privette J L, Huemmrich K F, Fensholt R,Sandholt I, Weiss M, Ahl DE, Gower ST, Nemani RR, Knyazikhin Y and Myneni RB. 2006. MODIS leaf area index products: from validation to algorithm improvement. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1885–1898
  26. 26.
    Zhang RH, Tian J, Li Z L, Su H B, Chen S H and Tang X Z. 2010. Principles andmethods for the validation of quantitativeremote sensing products. Science ChinaEarth Sciences, 53(5): 741–751
  27. 27.
    Zhu X H, Feng X M, Zhao Y S and Song X N. 2010. Scale effect and error analysis of crop LAI inversion. Journal of Remote Sensing,14(3): 579–592

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