The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products
"> Figure 1
<p>Data processing flowchart of the support vector regression (SVR) inversion method with two training sample-selecting methods: (<b>A</b>) the homogeneous and pure pixel filter method and (<b>B</b>) the pixel unmixing method.</p> "> Figure 2
<p>The two study areas: (<b>a</b>) Baoding, Hebei Province, China; (<b>b</b>) Des Moines, Iowa, United States. The grid in (<b>b</b>) shows the MODIS pixel cells at the 500-m resolution.</p> "> Figure 2 Cont.
<p>The two study areas: (<b>a</b>) Baoding, Hebei Province, China; (<b>b</b>) Des Moines, Iowa, United States. The grid in (<b>b</b>) shows the MODIS pixel cells at the 500-m resolution.</p> "> Figure 3
<p>The MODIS leaf area index (LAI) pixel filtering process. From left to right: (<b>a</b>) original MODIS LAI; (<b>b</b>) MODIS LAI pixels for cropland with the highest retrieval quality (main algorithm and not saturated); (<b>c</b>) CV map of NIR band4 from Landsat5; and (<b>d</b>) final selected homogeneous and pure MODIS LAI pixels.</p> "> Figure 4
<p>Classification products of the Soil Moisture Experiment 2002 (SMEX02) near Des Moines, Iowa, United States: (<b>a</b>) MCD12Q1_IGBP; (<b>b</b>) GlobeLand30 in 2000; (<b>c</b>) GlobeLand30 in 2010; (<b>d</b>) 30-m classification product. Notes: (<b>a</b>) 12: agricultural land; 13: urban and construction area; 14: junction of agricultural land and natural vegetation. (<b>b</b>–<b>d</b>) 10: arable land; 20: forest; 30: grassland; 50: wetland; 60: water body; 80: artificial surfaces; 90: bare land.</p> "> Figure 4 Cont.
<p>Classification products of the Soil Moisture Experiment 2002 (SMEX02) near Des Moines, Iowa, United States: (<b>a</b>) MCD12Q1_IGBP; (<b>b</b>) GlobeLand30 in 2000; (<b>c</b>) GlobeLand30 in 2010; (<b>d</b>) 30-m classification product. Notes: (<b>a</b>) 12: agricultural land; 13: urban and construction area; 14: junction of agricultural land and natural vegetation. (<b>b</b>–<b>d</b>) 10: arable land; 20: forest; 30: grassland; 50: wetland; 60: water body; 80: artificial surfaces; 90: bare land.</p> "> Figure 5
<p>The comparison between the retrieved LAI by three approaches and field measurements: (<b>a</b>) the results of dataset A1 (upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products); (<b>b</b>) the results of dataset A2 (MODIS reflectance and LAI products); (<b>c</b>) the results of dataset B (unmixed MODIS surface reflectance and LAI products at 30-m resolution).</p> "> Figure 6
<p>Comparison of inversion LAI in time series with field measurements. (<b>a</b>–<b>c</b>) The results of dataset A1; (<b>d</b>–<b>f</b>) the results of dataset A2; (<b>g</b>–<b>i</b>) the results of dataset B.</p> "> Figure 7
<p>Comparison of inversion LAI with field measurements at the Baoding study area; (<b>a</b>) the results of datasets A1 and A2; (<b>b</b>) the results of dataset B.</p> "> Figure 8
<p>The LAI inversion in time series at the SMEX02 site (23 June, 1 July, and 8 July): (<b>a</b>) the results of dataset A1; (<b>b</b>) the results of dataset A2; (<b>c</b>) the results of dataset B.</p> "> Figure 9
<p>LAI inversion in time series at the Baoding area (MODIS DOY: 89–129), based on the homogeneous and pure pixel filter method and the pixel unmixing method. (<b>a</b>) pixel one; (<b>b</b>) pixel two; (<b>c</b>) pixel three; (<b>d</b>) pixel four; (<b>e</b>) pixel five and (<b>f</b>) pixel six.</p> "> Figure 10
<p>Histogram of LAI from the LAI-SR samples of SMEX02. (<b>a</b>) dataset A1 (upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products); (<b>b</b>) dataset A2 (MODIS reflectance and LAI products); (<b>c</b>) dataset B (unmixed MODIS surface reflectance and LAI products at 30-m resolution).</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Methods of Training Sample Selection
2.1.1. The Homogeneous and Pure Pixel Filter Method
2.1.2. The Pixel Unmixing Method
2.2. Support Vector Regression
3. Study Area and Data Description
3.1. Study Area
3.2. Data and Preprocessing
4. Experiments and Analysis
4.1. SVR Training Samples
4.1.1. Homogeneous and Pure Pixel Filter
4.1.2. Pixel Unmixing
4.2. Comparison of Regions
4.2.1. Retrieved LAI on SMEX02
4.2.2. Retrieved LAI at the Baoding Study Area
4.2.3. Temporal Trends of LAI at the 30-m Resolution
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Chen, J.M.; Black, T.A. Measuring leaf area index of plant canopies with branch architecture. Agric. For. Meteorol. 1991, 57, 1–12. [Google Scholar] [CrossRef]
- Barbu, A.L.; Calvet, J.C.; Mahfouf, J.F.; Albergel, C.; Lafont, S. Assimilation of soil wetness index and leaf area index into the ISBA-A-gs land surface model: Grassland case study. Biogeosci. Discuss. 2011, 8, 1971–1986. [Google Scholar] [CrossRef] [Green Version]
- Anav, A.; Murray-Tortarolo, G.; Friedlingstein, P.; Sitch, S.; Piao, S.; Zhu, Z. Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models. Remote Sens. 2013, 5, 3637–3661. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Z.; Bi, J.; Pan, Y.; Ganguly, S.; Anav, A.; Xu, L.; Samanta, A.; Piao, S.; Nemani, R.; Myneni, R. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 2013, 5, 927–948. [Google Scholar] [CrossRef]
- Claverie, M.; Matthews, J.; Vermote, E.; Justice, C. A 30+ year AVHRR LAI and FPAR climate data record: Algorithm description and validation. Remote Sens. 2016, 8, 263. [Google Scholar] [CrossRef]
- Myneni, R.B.; Hoffman, S.; Knyazikhin, Y.; Privette, J.L.; Glassy, J.; Tian, Y. 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]
- Xiao, Z.; Liang, S.; Wang, J.; Chen, P.; Yin, X.; Zhang, L.; Song, J. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens. 2014, 52, 209–223. [Google Scholar] [CrossRef]
- Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Niño, F.; Weiss, M.; Samain, O.; et al. LAI, FPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sens. Environ. 2007, 110, 275–286. [Google Scholar] [CrossRef] [Green Version]
- Baret, F.; Weiss, M.; Lacaze, R.; Camacho, F.; Makhmara, H.; Pacholcyzk, P.; Smets, B. Geov1: LAI and FPAR essential climate variables and fCover global time series capitalizing over existing products. Part 1: Principles of development and production. Remote Sens. Environ. 2013, 137, 299–309. [Google Scholar] [CrossRef]
- Hu, J.; Tan, B.; Shabanov, N.; Crean, K.A.; Martonchik, J.V.; Diner, D.J.; Knyazikhin, Y.; Myneni, R.B. Performance of the MISR LAI and FPAR algorithm: A case study in africa. Remote Sens. Environ. 2003, 88, 324–340. [Google Scholar] [CrossRef]
- Valderrama-Landeros, L.H.; España-Boquera, M.L.; Baret, F. Deforestation in Michoacan, Mexico, from CYCLOPES-LAI time series (2000–2006). IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 1–8, 1–8. [Google Scholar] [CrossRef]
- Wang, J.; Wang, J.; Zhou, H.; Xiao, Z. Detecting forest disturbance in northeast China from GLASS LAI time series data using a dynamic model. Remote Sens. 2017, 9, 1293. [Google Scholar] [CrossRef]
- Wang, Q.; Tenhunen, J.; Dinh, N.; Reichstein, M.; Otieno, D.; Granier, A.; Pilegarrd, K. Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites. Remote Sens. Environ. 2005, 96, 475–484. [Google Scholar] [CrossRef]
- Verrelst, J.; Rivera, J.P.; Veroustraete, F.; Muñoz-Marí, J.; Clevers, J.G.P.W.; Camps-Valls, G.; Moreno, J. Experimental sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods—A comparison. ISPRS J. Photogramm. Remote Sens. 2015, 108, 260–272. [Google Scholar] [CrossRef]
- Zhang, W.; Chen, Y.; Hu, S. Retrieving LAI in the heihe and the hanjiang river basins using Landsat images for accuracy evaluation on MODIS LAI product. Int. Geosci. Remote Sens. Symp. (IGARSS) 2007, 3417–3421, 3417–3421. [Google Scholar] [CrossRef]
- Chen, J.M.; Pavlic, G.; Brown, L.; Cihlar, J.; Leblanc, S.G. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens. Environ. 2002, 80, 165–184. [Google Scholar] [CrossRef]
- Duan, S.B.; Li, Z.L.; Wu, H.; Tang, B.H.; Ma, L.; Zhao, E.; Li, C. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 12–20. [Google Scholar] [CrossRef]
- Durbha, S.S.; King, R.L.; Younan, N.H. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sens. Environ. 2007, 107, 348–361. [Google Scholar] [CrossRef]
- Huang, J.; Li, X.; Liu, D.; Ma, H.; Tian, L.; Wei, S. Comparison of winter wheat yield estimation by sequential assimilation of different spatio-temporal resolution remotely sensed LAI datasets. Trans. Chin. Soc. Agric. Mach. 2015, 46, 240–248. [Google Scholar] [CrossRef]
- Chen, S.; Zhao, Y.; Shen, S. Applicability of pywofost model based on ensemble kalman filter in simulating maize yield in northeast China. Chin. J. Agrometeorol. 2012, 33, 245–253. [Google Scholar]
- Gao, F.; Anderson, M.C.; Kustas, W.P.; Wang, Y. Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference. J. Appl. Remote Sens. 2012, 6, 063554. [Google Scholar] [CrossRef]
- Viña, A.; Gitelson, A.A.; Nguy-Robertson, A.L.; Peng, Y. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ. 2011, 115, 3468–3478. [Google Scholar] [CrossRef]
- Wang Qing, L.J.; Kun-Yong, Y. Inversion of masson pine forest LAI by multiple-perspective vegetation index. Plant Sci. J. 2017, 35, 48–55. [Google Scholar] [CrossRef]
- Linna, C.; Yonghua, Q.; Lixin, Z.; Shunlin, L.; Jindi, W. Estimating time series leaf area index based on recurrent neural networks. Adv. Earth Sci. 2009, 24, 756–768. [Google Scholar] [CrossRef]
- Liang, S.L.; Cheng, J.; Jia, K.; Jiang, B.; Liu, Q.; Liu, S.H.; Xiao, Z.Q.; Xie, X.H.; Yao, Y.J.; Yuan, W.P.; et al. Recent progress in land surface quantitative remote sensing. J. Remote Sens. 2016, 20, 875–898. [Google Scholar] [CrossRef]
- Lv, J. Hyperspectral Remote Sensing Inversion Models of Crop Chlorophy || Content Based on Machine Learning and Radiative Transfer Models; China University of Geosciences: Beijing, China, 2012. [Google Scholar]
- Awad, M.; Mariette, R.; Khanna, R. Support vector regression. Neural Inf. Process. Lett. Rev. 2007, 11, 203–224. [Google Scholar]
- Ichoku, C.; Karnieli, A. A review of mixture modeling techniques for sub-pixel land cover estimation. Remote Sens. Rev. 1996, 13, 161–186. [Google Scholar] [CrossRef]
- Chang chun, L.V. A review of pixel unmixing models. Remote Sens. Inf. 2003, 55–58, 55–58. [Google Scholar] [CrossRef]
- Settle, J.J.; Drake, N.A. Linear mixing and the estimation of ground cover proportions. Int. J. Remote Sens. 1993, 14, 1159–1177. [Google Scholar] [CrossRef] [Green Version]
- Tobler, W.R. A computer movie simulating urban growth in the detroit region. Econ. Geogr. 1970, 46, 234–240. [Google Scholar] [CrossRef]
- Liao, L.; Song, J.; Wang, J.; Xiao, Z.; Wang, J. Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI. Remote Sens. 2016, 8, 452. [Google Scholar] [CrossRef]
- Vapnik, V.; Golowich, S.E.; Smola, A. Support vector method for function approximation, regression estimation, and signal processing. Adv. Neural Inf. Process. Syst. 1997, 9, 281–287. [Google Scholar]
- Chang, C.C.; Lin, C.J. Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2011, 2, 27. [Google Scholar] [CrossRef]
- Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168–182. [Google Scholar] [CrossRef]
- Chen, J.; Ban, Y.; Li, S. China: Open access to earth land-cover map. Nature 2014, 514, 434. [Google Scholar]
- Chen, J.; Liao, A.; Chen, J.; Peng, S.; Chen, L.; Zhang, H. 30-Meter Global Land Cover Data Product-Globe Land30; Geomatics World: Beijing, China, 2017. [Google Scholar]
- Knyazikhin, Y.; Glassy, J.; Privette, J.L.; Tian, Y.; Lotsch, A.; Zhang, Y.; Wang, Y.; Morisette, J.T.; Votava, P.; Myneni, R.B.; et al. MODIS Leaf Area Index (LAI) and Fractionof Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15) Algorithm Theoretical Basis Document. 1999. Available online: http://eospso.gsfc.nasa.gov/atbd/modistables.html (accessed on 11 May 2018).
- Yang, W.; Huang, D.; Tan, B.; Stroeve, J.C.; Shabanov, N.V.; Knyazikhin, Y.; Nemani, R.R.; Myneni, R.B. Analysis of leaf area index and fraction of par absorbed by vegetation products from the terra modis sensor 2000–2005. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1829–1842. [Google Scholar] [CrossRef]
- Anderson, M.C.; Neale, C.M.U.; Li, F.; Norman, J.M.; Kustas, W.P.; Jayanthi, H.; Chavez, J. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens. Environ. 2004, 92, 447–464. [Google Scholar] [CrossRef]
- Liang, D.; Yang, Q.; Huang, W.; Peng, D.; Zhao, J.; Huang, L.; Zhang, D.; Song, X. Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat. Infrared Laser Eng. 2015, 44, 335–340. [Google Scholar] [CrossRef]
Band | TM (μm) | ETM+ (μm) | OLI (μm) | MODIS (μm) |
---|---|---|---|---|
Blue | 0.45–0.53 | 0.45–0.53 | 0.45–0.51 | 0.459–0.479 |
Green | 0.52–0.60 | 0.52–0.60 | 0.53–0.59 | 0.545–0.565 |
Red | 0.63–0.69 | 0.63–0.69 | 0.64–0.67 | 0.62–0.67 |
NIR | 0.76–0.90 | 0.76–0.90 | 0.85–0.88 | 0.841–0.876 |
SWIR | 1.55–1.75 | 1.55–1.75 | 1.57–1.65 | 1.628–1.652 |
© 2018 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
Zhou, J.; Zhang, S.; Yang, H.; Xiao, Z.; Gao, F. The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products. Remote Sens. 2018, 10, 1187. https://doi.org/10.3390/rs10081187
Zhou J, Zhang S, Yang H, Xiao Z, Gao F. The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products. Remote Sensing. 2018; 10(8):1187. https://doi.org/10.3390/rs10081187
Chicago/Turabian StyleZhou, Jianmin, Shan Zhang, Hua Yang, Zhiqiang Xiao, and Feng Gao. 2018. "The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products" Remote Sensing 10, no. 8: 1187. https://doi.org/10.3390/rs10081187