Generating high spatiotemporal resolution LAI based on MODIS/GF-1 data and combined Kriging-Cressman interpolation

Liu Zhenhua, Huang Rugen, Hu Yueming, Fan Shudi, Feng Peihua

Abstract


Abstract: Generation of high spatial and temporal resolution LAI (leaf area index) products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa. In this study, a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer (MODIS) characterized by coarse spatial resolution and high temporal resolution and Gaofen-1 (GF-1) with fine spatial resolution and coarse temporal resolution. This method was applied to the Huangpu district of Guangzhou, Guangdong, China. The results showed that compared to field observation, the predicted values of LAI had an acceptable accuracy of 73.12%. Using Moran’s I index and Kolmogorov-Smirnov tests, it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions. Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38% compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging. Moreover, the regression models that accounts for the relationship between NDVI (Normalized Difference Vegetation Index) and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870. Finally, the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps, which showed reasonably spatial and temporal variability. The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.
Keywords: data fusion, MODIS, GF-1, LAI, spatiotemporal resolution, spatial interpolation, remote sensing
DOI: 10.3965/j.ijabe.20160905.1777

Citation: Liu Z H, Huang R G, Hu Y M, Fan S D, Feng P H. Generating high spatiotemporal resolution LAI based on MODIS/GF-1 data and combined Kriging-Cressman interpolation. Int J Agric & Biol Eng, 2016; 9(5): 120-131.

Keywords


data fusion, MODIS, GF-1, LAI, spatiotemporal resolution, spatial interpolation, remote sensing

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References


Myneni R B, Hoffman S, Knyazikhin Y, Privette J L, Glassy J, Tian 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. DOI: 10.1016/s0034-4257(02)00074-3.

Veroustraete F, Patyn J, Myneni R B. Estimating net ecosystem exchange of carbon using the normalized difference vegetation index and an ecosystem model. Remote Sensing of Environment, 1996; 58(1): 115–130. DOI: 10.1016/0034-4257(95)00258-8.

Huang H P. Scale issues in object-oriented image analysis. Beijing: Graduate University of Chinese Academy of Sciences, 2003. (in Chinese with English abstract)

Zhou M, Zhang J L. Review on scale transformation for remote sensing image and selection of optimal spatial resolution. World Nuclear Geoscience, 2011; 28(2): 94–98. (in Chinese with English abstract)

Wang Q L. Study on object-oriented remote sensing image classification and its application-taking urban vegetation extraction in Futian, Shenzhen city for example. Nanjing: Nanjing Forestry University, 2008. (in Chinese with English

abstract)

He M. Land use information extraction by object-oriented technology based on remote sensing image. Sichuan: Southeast University of Science and Technology, 2006. (in Chinese with English abstract)

Guo L M. An analysis on scale transformation in remote sensing-based on fractal theory. Shaanxi: Southwest University, 2008. (in Chinese with English abstract)

Hay G J, Niemann K O. Spatial thresholds, image-objects, and up scaling: a multi-scale evaluation. Remote Sensing of Environment, 1997; 62(1): 1–19. DOI: 10.1016/s0034- 4257(97)81622-7.

Atkinson P M, Tate N J. Spatial scale problems and geostatistical solutions: A review. The Professional Geographer, 2000; 52(4): 607–623. DOI: 10.1111/0033- 0124.00250.

Crow W T, Ryu D, Famiglietti J S. Upscaling of field-scale soil moisture measurements using distributed land surface modeling. Advances in Water Resources, 2005; 28(1): 1–14. DOI: 10.1016/j.advwatres.2004.10.004.

Famiglietti J S, Devereaux J A, Laymon C A, Tsegaye T, Houser P R, Jackson T J, et al. Ground-based investigation of soil moisture variability within remote sensing footprints during the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Water Resources Research, 1999; 35(6): 1839–1851. DOI: 10.1029/1999wr900047.

Mohanty B P, Skaggs T H. Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation. Advances in Water Resources, 2001; 24(9): 1051–1067. DOI: 10.1016/s0309-1708(01)00034-3.

Cosh M H, Jackson T J, Bindlish R, Prueger J H. Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates. Remote Sensing of Environment, 2004; 92(4): 427–435. DOI: 10.1016/j.rse.2004.02.016.

Li H B, Lin Z H, Liu S X. Application of Kriging technique in estimating soil moisture in China. Geographical Research, 2001; 22(5): 472-478. (in Chinese with English abstract)

Zhang J G, Chen H S, Su Y R, Zhang W, Kong X L. Spatial variability of soil moisture in surface layer in depressed karst region and its scale effect. Acta Pedologica Sinica, 2008; 45(3): 544–549. (in Chinese with English abstract)

Grayson R B, Western A W. Towards areal estimation of soil water content from point measurements: time and space stability of mean response. Journal of Hydrology, 1998; 207(1): 68–82. DOI: 10.1016/s0022-1694(98)00096-1.

Woodcock C E, Strahler A H. The factor of scale in remote

sensing. Remote Sensing of Environment, 1987; 21(3): 311–332. DOI: 10.1016/0034-4257(87)90015-0.

Doraiswamy P C, Sinclair T R, Hollinger S, Akhmedov B, Stern A, Prueger J. Application of MODIS derived parameters for regional crop yield assessment. Remote Sensing of Environment, 2005; 97(2): 192–202. DOI: 10.1016/j.rse.2005.03.015.

Liu Y. Atmospheric correction on MODIS satellite image based on FLAASH model. Geomatics & Spatial Information Technology, 2013; 36(3): 47–49. (in Chinese with English abstract)

Goovaerts P. Geostatistics for natural resources evaluation. Oxford University Press, 1997.

Cressie N. Statistics for spatial data. Revised Edition. John Wiley & Sons, Inc. 2015; pp.149-165.

Kyriakidis P C. A geostatistical gramework for area-to-point spatial interpolation. Geographical Analysis, 2004; 36(3): 259–289. DOI: 10.1111/j.1538-4632.2004. tb01135.x.

Yuan H, Dai Y J, Xiao Z Q, Wei S G. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sensing of Environment, 2011; 115(5): 1171–1187. DOI: 10.1016/j.rse.2011.01.001. (in Chinese with English abstract)

Wang Q C, Zhang X H, Zhang L Y, Niu S K. Forest landscape diversity of Labagoumen Nature Reserve in Beijing. Journal of Beijing Forestry University, 2002; 24(3): 54–60. (in Chinese with English abstract)

Lu P, Peng P Q, Song B L, Tang G Y, Zou Y, Huang D Y, Xiao H A, Wu J S, Su Y R. Geostatistical and GIS analyses on soil total P in the typical area of Dongting Lake plain. Scientia Agricultura Sinica, 2005; 38(6): 1204–1212.

Mao S S, Cheng Y M, Pu X L. Probability & Statistics (The Second Edition). Beijing: China Higher Education Press.

Zheng Y, Chen T, Chen H, Wu H, Zhou J, Luo J, et al. The spatial structure and distribution of Ni contents in soils of suburbs of Beijing. Acta Geographica Sinica, 2003, 58(3): 470–476. (in Chinese with English abstract)

Peng X, Zhang S W. Research on rice growth status based on NDVI and LAI. Remote Sensing Technology and Application, 2002; 17(1): 12–15. (in Chinese with English abstract)

Wang F M, Huang J F, Tang Y L, Wang X Z. Estimation of rice LAI using NDVI at different spectral bandwidths. Chinese Journal of Applied Ecology, 2007; 18(11): 2444–2450. (in Chinese with English abstract)

Gan Y W, Cheng P, Zhou C B, Luo Y J, Kuang Y, Zhang X H. Study on pertinence between the vegetation indexes and the aboveground biomass of Sub-alpine meadow of Zoige County. Journal of Natural Resources, 2009; 24(11): 1963–1972. (in Chinese with English abstract)




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