Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands
"> Figure 1
<p>Land cover types (<b>a</b>) and growing season mean leaf area index (LAI) of grasslands (<b>b</b>) for the test area in 2011.</p> "> Figure 2
<p>Flow chart of the Enhanced Ecosystem-Dependent Interpolation (EEDI) algorithm.</p> "> Figure 3
<p>Statistics of the number of point in LAI time series during the iterative process.</p> "> Figure 4
<p>Overall interpolation accuracies of the EEDI and EDI algorithms.</p> "> Figure 5
<p>The original and interpolated LAI time series of a pixel in 2011.</p> "> Figure 6
<p>Interpolation accuracies of the EEDI and EDI algorithms for different proportions of missing data (PMD) quantified by (<b>a</b>) coefficient of determination R<sup>2</sup>, (<b>b</b>) root mean square error (RMSE), (<b>c</b>) slope, and (<b>d</b>) intercept.</p> "> Figure 7
<p>Interpolation accuracies of the EEDI and EDI algorithms for different seasons of missing data (SMD) quantified by (<b>a</b>) R<sup>2</sup>, (<b>b</b>) RMSE, (<b>c</b>) slope, and (<b>d</b>) intercept.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Area
2.2. MODIS LAI Product
2.3. Algorithm Development
2.3.1. Extracting High Quality LAI Data
2.3.2. Temporal Interpolation of LAI Time Series with Phenological Links
2.4. Algorithm Validation and Comparison
3. Results
3.1. Determination of Iteration Times
3.2. Effects of PMD on Interpolation
3.3. Effects of SMD on Interpolation
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Chen, J.M.; Black, T.A. Defining leaf-area index for non-flat leaves. Plant Cell Environ. 1992, 15, 421–429. [Google Scholar] [CrossRef]
- Myneni, R.B.; Hoffman, S.; Knyazikhin, Y.; Privette, J.L.; Glassy, J.; Tian, Y.; Wang, Y.; Song, X.; Zhang, Y.; Smith, G.R.; et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 2002, 83, 214–231. [Google Scholar] [CrossRef]
- Ganguly, S.; Schull, M.A.; Samanta, A.; Shabanov, N.V.; Milesi, C.; Nemani, R.R.; Knyazikhin, Y.; Myneni, R.B. Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory. Remote Sens. Environ. 2008, 112, 4333–4343. [Google Scholar] [CrossRef]
- Yang, W.; Shabanov, N.V.; Huang, D.; Wang, W.; Dickinson, R.E.; Nemani, R.R.; Knyazikhin, Y.; Myneni, R.B. Analysis of leaf area index products from combination of MODIS Terra and Aqua data. Remote Sens. Environ. 2006, 104, 297–312. [Google Scholar] [CrossRef]
- Stoeckli, R.; Rutishauser, T.; Baker, I.; Liniger, M.A.; Denning, A.S. A global reanalysis of vegetation phenology. J. Geophys. Res. 2011, 116, G03020. [Google Scholar] [CrossRef]
- Verger, A.; Filella, I.; Baret, F.; Penuelas, J. Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sens. Environ. 2016, 178, 1–14. [Google Scholar] [CrossRef]
- Chen, J.; Jonsson, P.; Tamura, M.; Gu, Z.H.; Matsushita, B.; Eklundh, L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 2004, 91, 332–344. [Google Scholar] [CrossRef]
- Olsson, L.; Eklundh, L. Fourier-series for analysis of temporal sequences of satellite sensor imagery. Int. J. Remote Sens. 1994, 15, 3735–3741. [Google Scholar] [CrossRef]
- Roerink, G.J.; Menenti, M.; Verhoef, W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens. 2000, 21, 1911–1917. [Google Scholar] [CrossRef]
- Moody, A.; Johnson, D.M. Land-surface phenologies from AVHRR using the discrete Fourier transform. Remote Sens. Environ. 2001, 75, 305–323. [Google Scholar] [CrossRef]
- Yang, G.; Shen, H.; Zhang, L.; He, Z.; Li, X. A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data. IEEE Trans. Geosci. Remote Sens. 2015, 53, 6008–6021. [Google Scholar] [CrossRef]
- Sakamoto, T.; Yokozawa, M.; Toritani, H.; Shibayama, M.; Ishitsuka, N.; Ohno, H. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 2005, 96, 366–374. [Google Scholar] [CrossRef]
- Lu, X.; Liu, R.; Liu, J.; Liang, S. Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm. Eng. Remote Sens. 2007, 73, 1129–1139. [Google Scholar] [CrossRef]
- Qiu, B.; Feng, M.; Tang, Z. A simple smoother based on continuous wavelet transform: Comparative evaluation based on the fidelity, smoothness and efficiency in phenological estimation. Int. J. Appl. Earth Obs. Geoinf. 2016, 47, 91–101. [Google Scholar] [CrossRef]
- Jonsson, P.; Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 2002, 40, 1824–1832. [Google Scholar] [CrossRef]
- Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84, 471–475. [Google Scholar] [CrossRef]
- Hird, J.N.; McDermid, G.J. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sens. Environ. 2009, 113, 248–258. [Google Scholar] [CrossRef]
- Atkinson, P.M.; Jeganathan, C.; Dash, J.; Atzberger, C. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 2012, 123, 400–417. [Google Scholar] [CrossRef]
- Kandasamy, S.; Baret, F.; Verger, A.; Neveux, P.; Weiss, M. A comparison of methods for smoothing and gap filling time series of remote sensing observations—Application to MODIS LAI products. Biogeosciences 2013, 10, 4055–4071. [Google Scholar] [CrossRef]
- Kandasamy, S.; Fernandes, R. An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20 year NOAA AVHRR data over Canada. Remote Sens. Environ. 2015, 164, 114–129. [Google Scholar] [CrossRef]
- Geng, L.; Ma, M.; Wang, X.; Yu, W.; Jia, S.; Wang, H. Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China. Remote Sens. 2014, 6, 2024–2049. [Google Scholar] [CrossRef]
- Zhou, J.; Jia, L.; Menenti, M.; Gorte, B. On the performance of remote sensing time series reconstruction methods—A spatial comparison. Remote Sens. Environ. 2016, 187, 367–384. [Google Scholar] [CrossRef]
- Moody, E.G.; King, M.D.; Platnick, S.; Schaaf, C.B.; Gao, F. Spatially complete global spectral surface albedos: Value-added datasets derived from Terra MODIS land products. IEEE Trans. Geosci. Remote Sens. 2005, 43, 144–158. [Google Scholar] [CrossRef]
- Fang, H.; Liang, S.; Townshend, J.R.; Dickinson, R.E. Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America. Remote Sens. Environ. 2008, 112, 75–93. [Google Scholar] [CrossRef]
- Gao, F.; Morisette, J.T.; Wolfe, R.E.; Ederer, G.; Pedelty, J.; Masuoka, E.; Myneni, R.; Tan, B.; Nightingale, J. An algorithm to produce temporally and spatially continuous MODIS-LAI time series. IEEE Geosci. Remote Sens. Lett. 2008, 5, 60–64. [Google Scholar] [CrossRef]
- Borak, J.S.; Jasinski, M.F. Effective interpolation of incomplete satellite-derived leaf-area index time series for the continental United States. Agric. For. Meteorol. 2009, 149, 320–332. [Google Scholar] [CrossRef]
- Yuan, H.; Dai, Y.; Xiao, Z.; Ji, D.; Shangguan, W. Reprocessing the MODIS leaf area index products for land surface and climate modelling. Remote Sens. Environ. 2011, 115, 1171–1187. [Google Scholar] [CrossRef]
- Vuolo, F.; Ng, W.-T.; Atzberger, C. Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data. Int. J. Appl. Earth Obs. Geoinf. 2017, 57, 202–213. [Google Scholar] [CrossRef]
- Elmore, A.J.; Guinn, S.M.; Minsley, B.J.; Richardson, A.D. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Chang. Biol. 2012, 18, 656–674. [Google Scholar] [CrossRef]
- Klosterman, S.T.; Hufkens, K.; Gray, J.M.; Melaas, E.; Sonnentag, O.; Lavine, I.; Mitchell, L.; Norman, R.; Friedl, M.A.; Richardson, A.D. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 2014, 11, 4305–4320. [Google Scholar] [CrossRef] [Green Version]
- De Beurs, K.M.; Henebry, G.M. Spatio-temporal statistical methods for modeling land surface phenology. In Phenological Research: Methods for Environmental and Climate Change Analysis; Hudson, I.L., Keatley, M.R., Eds.; Springer: Dordrecht, The Netherlands, 2010; pp. 177–208. [Google Scholar]
- Zheng, H.Y.; Li, J.D. Saline Plants in Songnen Plain and Restoration of Alkaline-Saline Grass; Science Press: Beijing, China, 1999; pp. 179–206. [Google Scholar]
- Shang, Z.B.; Gao, Q.; Dong, M. Impacts of grazing on the alkalinized-salinized meadow steppe ecosystem in the Songnen plain, China—A simulation study. Plant Soil 2003, 249, 237–251. [Google Scholar] [CrossRef]
- Bai, L.; Wang, C.; Zang, S.; Zhang, Y.; Hao, Q.; Wu, Y. Remote sensing of soil alkalinity and salinity in the Wuyu’er-Shuangyang river basin, northeast China. Remote Sens. 2016, 8, 163. [Google Scholar] [CrossRef]
- Knyazikhin, Y.; Martonchik, J.V.; Myneni, R.B.; Diner, D.J.; Running, S.W. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res. Atmos. 1998, 103, 32257–32275. [Google Scholar] [CrossRef]
- Viovy, N.; Arino, O.; Belward, A.S. The best index slope extraction (BISE)—A method for reducing noise in NDVI time-series. Int. J. Remote Sens. 1992, 13, 1585–1590. [Google Scholar] [CrossRef]
- Verger, A.; Baret, F.; Weiss, M.; Kandasamy, S.; Vermote, E. The CACAO method for smoothing, gap filling, and characterizing seasonal anomalies in satellite time series. IEEE Trans. Geosci. Remote Sens. 2013, 51, 1963–1972. [Google Scholar] [CrossRef]
- Chen, J.; Rao, Y.; Shen, M.; Wang, C.; Zhou, Y.; Ma, L.; Tang, Y.; Yang, X. A simple method for detecting phenological change from time series of vegetation index. IEEE Trans. Geosci. Remote Sens. 2016, 54, 3436–3449. [Google Scholar] [CrossRef]
Items | Flags of High Quality Data 1 |
---|---|
Five-level confidence score | Main method used, best result possible (no saturation); Main method used with saturation |
Cloud | Significant clouds not present (clear) |
Cloud shadow | No cloud shadow detected |
Cirrus | No cirrus detected |
Snow/Ice | No snow/ice detected |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ding, C.; Liu, X.; Huang, F. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands. Remote Sens. 2017, 9, 968. https://doi.org/10.3390/rs9090968
Ding C, Liu X, Huang F. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands. Remote Sensing. 2017; 9(9):968. https://doi.org/10.3390/rs9090968
Chicago/Turabian StyleDing, Chao, Xiangnan Liu, and Fang Huang. 2017. "Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands" Remote Sensing 9, no. 9: 968. https://doi.org/10.3390/rs9090968