A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed

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Abstract

A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.

Keywords

Leaf area index Poyang Lake GF-1 MODIS LAI products 
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Notes

Acknowledgements

We thank the National Natural Science Foundation of China (41461080), the Natural Science Foundation of Jiangxi (20171ACB21051), and Jiangxi key Research and Development Program (20161BBG70052).

References

  1. Bacour, C., Baret, F., Béal, D., et al. (2006). Neural network estimation of LAI, fAPAR, fCover, and LAI × Cab, from top of canopy MERIS reflectance data: Principles and validation. Remote Sensing of Environment, 105(4), 313–325.CrossRefGoogle Scholar
  2. Baret, F., & Buis, S. (2008). Estimating canopy characteristics from remote sensing observations: Review of methods and associated problems. In S. Liang (Ed.), Advances in land remote sensing (pp. 173–201). Dordrecht, The Netherlands: Springer.Google Scholar
  3. Baret, F., Hagolle, O., Geiger, B., Bicheron, P., et al. (2007). LAI, f APAR and f Cover CYCLOPES global products derived from VEGETATION. Remote Sensing of Environment, 110, 275–286.CrossRefGoogle Scholar
  4. Breunig, F. M., Galvão, L. S., Santos, J. R. D., et al. (2015). Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle. International Journal of Applied Earth Observation and Geoinformation, 35(35), 294–304.CrossRefGoogle Scholar
  5. Che, M., Chen, B., Zhang, H., et al. (2014). A new equation for deriving vegetation phenophase from time series of leaf area index (LAI) data. Remote Sensing, 6(6), 5650–5670.CrossRefGoogle Scholar
  6. Chen, J. M., & Black, T. A. (1992). Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4), 421–429.CrossRefGoogle Scholar
  7. Chen, J. M., & Cihlar, J. (1996). Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55, 153–162.CrossRefGoogle Scholar
  8. Chen, H. Y., Niu, Z., Huang, W. J., Huang, N., & Zhang, Y. (2012). Estimation of winter wheat LAI using hotspot-signature vegetation indices. Transactions of the Chinese Society of Agricultural Engineering, 28(1), 167–172.Google Scholar
  9. Feng, L., Li, J., Gong, W. S., et al. (2016). Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8OLI images: A solution for large view angle associated problems. Remote Sensing of Environment, 174, 56–68.CrossRefGoogle Scholar
  10. Fensholt, R., Sandholt, I., & Rasmussen, M. S. (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91, 490–507.CrossRefGoogle Scholar
  11. FLAASH USER’S GUIDE, ENVI FLAASH Version 4.1, September, 2004 Edition, pp. 1–80 (Boulder, CO: Research Systems, Inc.Google Scholar
  12. Fortin, J. G., Anctil, F., & Parent, L. E. (2013). Comparison of physically based and empirical models to estimate corn (Zea mays L) LAI from multispectral data in eastern Canada. Canadian Journal of Remote Sensing, 39(1), 89–99.CrossRefGoogle Scholar
  13. Gao, F., Anderson, M. C., Kustas, W. P., et al. (2014). Retrieving leaf area index from landsat using MODIS LAI products and field measurements. IEEE Geoscience and Remote Sensing Letters, 11(4), 773–777.CrossRefGoogle Scholar
  14. Hicks, S. K., & Lascano, R. J. (1995). Estimation of leaf area index for cotton canopies using the LI-COR LAI-2000 plant canopy analyzer. Agronomy Journal, 87(3), 458–464.CrossRefGoogle Scholar
  15. Hill, M. J., Senarath, U., Lee, A., et al. (2006). Assessment of the MODIS LAI product for Australian ecosystems. Remote Sensing of Environment, 101(4), 495–518.CrossRefGoogle Scholar
  16. Jia, Y. Q., Li, B., Cheng, Y. Z., et al. (2015). Comparison between GF-1 images and Landsat-8 images in monitoring maize LAI. Transactions of the Chinese Society of, Agricultural Engineering, 31(9), 173–179.Google Scholar
  17. Kandasamy, S., Neveux, P., Verger, A., et al. (2012). Improving the consistency and continuity of MODIS 8 day leaf area index products. International Journal of Electronics & Telecommunications, 58(2), 141–146.CrossRefGoogle Scholar
  18. Knyazikhin, Y., Glassy, J., Privette, J. L., et al (1999). MODIS leaf area index (LAI) and fraction of photosynthetically activeradiation absorbed by vegetation (FPAR) product (MOD15) algorithm. Theoretical Basis Document, NASA Goddard Space Flight Center, Greenbelt, MD, USA.Google Scholar
  19. Knyazikhin, Y., Martonchik, J. V., Myeni, R. B., et al. (1998). 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, 103, 32257–32274.CrossRefGoogle Scholar
  20. Li, J., Chen, X., Tian, L., et al. (2015). Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations. ISPRS Journal of Photogrammetry & Remote Sensing, 106, 145–156.CrossRefGoogle Scholar
  21. Li, Z., Tang, H., Xin, X., et al. (2014). Assessment of 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.CrossRefGoogle Scholar
  22. Myneni, R. B., Hoffman, S., Knyazikhin, Y., et al. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1–2), 14–231.Google Scholar
  23. Myneni, R. B., & Knyazikhin, Y. (2002). Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83(1), 232–243.Google Scholar
  24. Myneni, R., Running, S. W., Glassy, J., & Votova, P. (2003). User’s guide: fPAR, LAI (ESDT: MOD15A2) 8-day composite. NASA MODIS Land Algorithm.Google Scholar
  25. Pasolli, L., Asam, S., Castelli, M., et al. (2015). Retrieval of leaf area index in mountain grasslands in the Alps from MODIS satellite imagery. Remote Sensing of Environment, 165, 159–174.CrossRefGoogle Scholar
  26. Qu, Y., Han, W., & Ma, M. (2015). Retrieval of a temporal high-resolution leaf area index (LAI) by combining MODIS LAI and ASTER reflectance data. Remote Sensing, 7(1), 195–210.CrossRefGoogle Scholar
  27. Rouse, J. W., Haas, R. H., Schell, J. A., et al. (1974). Monitoring vegetation systems in the great plains with erts. Nasa Special Publication, 351, 309.Google Scholar
  28. Ruiliang, Pu. (2012). Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery. International Journal of Remote Sensing, 33(20), 6600–6622.CrossRefGoogle Scholar
  29. Salomonson, V. V., Barnes, W. L., Maymon, P. W., et al. (1989). MODIS: Advanced facility instrument for studies of the Earth as a system. IEEE Transactions on Geoscience and Remote Sensing, 27(2), 145–153.CrossRefGoogle Scholar
  30. Shi, Y., Wang, J., Qin, J., et al. (2015). An upscaling algorithm to obtain the representative ground truth of LAI time series in heterogeneous land surface. Remote Sensing, 7(10), 12887–12908.CrossRefGoogle Scholar
  31. Tian, Y. H., Woodcock, C. E., Wang, Y. J., et al. (2002). Multiscale analysis and validation of the MODIS LAI product : II. Sampling strategy. Remote Sensing of Environment, 83(3), 431–441.CrossRefGoogle Scholar
  32. Verrelst, J., Rivera, J. P., Veroustraete, F., et al. (2015). Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods—A comparison. ISPRS Journal of Photogrammetry & Remote Sensing, 108, 260–272.CrossRefGoogle Scholar
  33. Vuolo, F., Atzberger, C., Richter, K., D‘Urso, G., & Dash, J. (2010). Retrieval of biophysicalvegetation products from rapid eye imagery. In W. Wagner & B. Székely (Eds.), Proceedings of the ISPRS technical commission VII symposium—100 years ISPRS—Advancing remote sensing science, IAPRS 38, Part 7A, Vienna, July 5–7.Google Scholar
  34. White, J. D., Running, S. W., Nemani, R., et al. (1997). Measurement and remote sensing of LAI in Rocky Mountain montane ecosystems. Canadian Journal of Forest Research, 27(11), 1714–1727.CrossRefGoogle Scholar
  35. Xiao, Z., Liang, S., Wang, J., et al. (2011). Real-time retrieval of leaf area index from MODIS time series data. Remote Sensing of Environment, 115(1), 97–106.CrossRefGoogle Scholar
  36. Yuan, H., Dai, Y., Xiao, Z., et al. (2011). Reprocessing the MODIS leaf area index products for land surface and climate modelling. Remote Sensing of Environment, 115(5), 1171–1187.CrossRefGoogle Scholar
  37. Zhang, Y., Tian, Y., Knyazikhin, Y., et al. (2000). Prototyping of MISR LAI and FPAR algorithm with POLDER data over Africa. IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2402–2418.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2017

Authors and Affiliations

  1. 1.National and Local Joint Engineering Laboratory of Hydraulic Engineering Safety and Efficient Utilization of Water Resources in Poyang Lake BasinNanchang Institute of TechnologyNanChangChina

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