Abstract
High spatial resolution and high temporal frequency fractional vegetation cover (FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite (HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index (NDVI) was acquired by using the continuous correction (CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product (GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors (MEs) of forest, cropland, and grassland were −0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809, root-mean-square deviation (RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude (RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.
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Bacour, C., F. Baret, D. Béal, et al., 2006: Neural network estimation of LAI, fAPAR, fCover and LAI × Cab, from top of canopy MERIS reflectance data: Principles and validation. Remote Sens. Environ., 105, 313–325, doi: https://doi.org/10.1016/j.rse.2006.07.014.
Baret, F., O. Hagolle, B. Geiger, et al., 2007: LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens. Environ., 110, 275–286, doi: https://doi.org/10.1016/j.rse.2007.02.018.
Baret, F., M. Weiss, R. Lacaze, et al., 2013: GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sens. Environ., 137, 299–309, doi: https://doi.org/10.1016/j.rse.2012.12.027.
Becker-Reshef, I., C. Justice, M. Sullivan, et al., 2010: Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) Project. Remote Sens., 2, 1589–1609, doi: https://doi.org/10.3390/rs2061589.
Bhandari, S., S. Phinn, and T. Gill, 2012: Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sens., 4, 1856–1886, doi: https://doi.org/10.3390/rs4061856.
Broxton, P. D., X. B. Zeng, W. Scheftic, et al., 2014: A MODIS-based global 1-km maximum green vegetation fraction dataset. J. Appl. Meteor. Climatol., 53, 1996–2004, doi: https://doi.org/10.1175/JAMC-D-13-0356.1.
Busetto, L., M. Meroni, and R. Colombo, 2008: Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series. Remote Sens. En-viron., 112, 118–131, doi: https://doi.org/10.1016/j.rse.2007.04.004.
Cai, H. K., X. Feng, Q. L. Chen, et al., 2017: Spatial and temporal features of the frequency of cloud occurrence over China based on CALIOP. Adv. Meteor., 2017, 4548357, doi: https://doi.org/10.1155/2017/4548357.
Cai, W. W., J. L. Song, J. D. Wang, et al., 2011: High spatial-and temporal-resolution NDVI produced by the assimilation of MODIS and HJ-1 data. Can. J. Remote Sens., 37, 612–627, doi: https://doi.org/10.5589/m12-004.
Camacho, F., J. Cernicharo, R. Lacaze, et al., 2013: GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ., 137, 310–329, doi: https://doi.org/10.1016/j.rse.2013.02.030.
Carlson, T. N., and D. A. Ripley, 1997: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62, 241–252, doi: https://doi.org/10.1016/S0034-4257(97)00104-1.
Channan, S., K. Collins, and W. R. Emanuel, 2014: Global Mosaics of the Standard MODIS Land Cover Type Data. University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA, 30 pp.
Chen, J., J. Chen, A. P. Liao, et al., 2015: Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogr. Remote Sens., 103, 7–27, doi: https://doi.org/10.1016/j.is-prsjprs.2014.09.002.
Choudhury, B. J., N. U. Ahmed, S. B. Idso, et al., 1994: Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ., 50, 1–17, doi: https://doi.org/10.1016/0034-4257(94)90090-6.
DeFries, R. S., J. R. G. Townshend, and M. C. Hansen, 1999: Continuous fields of vegetation characteristics at the global scale at 1-km resolution. J. Geophys. Res. Atmos., 104, 16,911–16,923, doi: https://doi.org/10.1029/1999JD900057.
Ding, Y. L., X. M. Zheng, T. Jiang, et al., 2015: Comparison and validation of long time serial global GEOV1 and regional Australian MODIS fractional vegetation cover products over the Australian continent. Remote Sens., 7, 5718–5733, doi: https://doi.org/10.3390/rs70505718.
Ding, Y. L., X. M. Zheng, K. Zhao, et al., 2016: Quantifying the impact of NDVIsoil determination methods and NDVIsoil variability on the estimation of fractional vegetation cover in Northeast China. Remote Sens., 8, 29, doi: https://doi.org/10.3900/rs8010029.
Donohue, R. J., T. R. McVicar, and M. L. Roderick, 2009: Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006. Glob. Change Biol., 15, 1025–1039, doi: https://doi.org/10.1111/j.1365-2486.2008.01746.x.
Emelyanova, I. V., T. R. McVicar, T. G. Van Niel, et al., 2013: Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection. Remote Sens. Environ., 133, 193–209, doi: https://doi.org/10.1016/j.rse.2013.02.007.
Fu, D. J., B. Z. Chen, J. Wang, et al., 2013: An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sens., 5, 6346–6360, doi: https://doi.org/10.3390/rs5126346.
Gan, M. Y., J. S. Deng, X. Y. Zheng, et al., 2014: Monitoring urban greenness dynamics using multiple endmember spectral mixture analysis. PLoS ONE, 9, e112202, doi: https://doi.org/10.3771/journal.pone.0112202.
Gao, F., J. Masek, M. Schwaller, et al., 2006: On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens., 44, 2207–2218, doi: https://doi.org/10.1109/TGRS.2006.872081.
Gao, L., X. F. Wang, B. A. Johnson, et al., 2020: Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS J. Photogr. Remote Sens., 159, 364–377, doi: https://doi.org/10.1016/j.isprsjprs.2019.11.018.
García-Haro, F. J., F. Camacho de Coca, J. Meliá, et al., 2005a: Operational derivation of vegetation products in the framework of the LSA SAF project. Proceedings of EUMETSAT Meteorological Satellite Conference, Dubrovnik, Croatia, 19–23 September, 247–254.
García-Haro, F. J., S. Sommer, and T. Kemper, 2005b: A new tool for variable multiple endmember spectral mixture analysis (VMESMA). Int. J. Remote Sens., 26, 2135–2162, doi: https://doi.org/10.1080/01431160512331337817.
Gong, P., J. Wang, L. Yu, et al., 2013: Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens., 34, 2607–2654, doi: https://doi.org/10.1080/01431161.2012.748992.
Guan, K., E. F. Wood, and K. K. Caylor, 2012: Multi-sensor derivation of regional vegetation fractional cover in Africa. Remote Sens. Environ., 124, 653–665, doi: https://doi.org/10.1016/j.rse.2012.06.005.
Guerschman, J. P., P. F. Scarth, T. R. McVicar, et al., 2015: Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data. Remote Sens. Environ., 161, 12–26, doi: https://doi.org/10.1016/j.rse.2015.01.021.
Gutman, G., and A. Ignatov, 1998: The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens., 19, 1533–1543, doi: https://doi.org/10.1080/014311698215333.
Hu, Z. Z., and D. G. Zhang, 2006: Country Pasture/Forage Resource Profiles: China. Food and Agriculture Organization of the United Nations (FAO), Rome, 63 pp. Available online at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.476.3411. Accessed on 20 January 2021.
Jarihani, A. A., T. R. McVicar, T. G. Van Niel, et al., 2014: Blending Landsat and MODIS data to generate multispectral indices: A comparison of “Index-then-Blend” and “Blend-then-Index” approaches. Remote Sens., 6, 9213–9238, doi: https://doi.org/10.3390/rs6109213.
Jia, K., S. L. Liang, S. H. Liu, et al., 2015: Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens., 53, 4787–4796, doi: https://doi.org/10.1109/TGRS.2015.2409563.
Jiapaer, G., X. Chen, and A. M. Bao, 2011: A comparison of methods for estimating fractional vegetation cover in arid regions. Agric. For. Meteor., 151, 1698–1710, doi: https://doi.org/10.1016/j.agrformet.2011.07.004.
Jiménez-Muñoz, J. C., J. A. Sobrino, A. Plaza, et al., 2009: Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA/CHRIS data over an agricultural area. Sensors, 9, 768–793, doi: https://doi.org/10.3390/s90200768.
Lacaze, R., P. Richaume, O. Hautecoeur, et al., 2003: Advanced algorithms of the ADEOS-2/POLDER-2 land surface process line: Application to the ADEOS-1/POLDER-1 data. 2003 IEEE International Geoscience and Remote Sensing Symposium Proceedings, IEEE, Toulouse, France, 3260–3262, doi: https://doi.org/10.1109/IGARSS.2003.1294749.
Li, Q. Z., X. Cao, K. Jia, et al., 2014: Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data. Int. J. Remote Sens., 35, 6076–6088, doi: https://doi.org/10.1080/01431161.2014.943325.
Liang, S. L., X. Zhao, S. H. Liu, et al., 2013: A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies. Int. J. Digit. Earth, 6, 5–33, doi: https://doi.org/10.1080/17538947.2013.805262.
Lobell, D. B., and G. P. Asner, 2002: Moisture effects on soil reflectance. Soil Sci. Soc. Amer. J., 66, 722–727, doi: https://doi.org/10.2136/sssaj2002.7220.
Lu, H., M. R. Raupach, T. R. McVicar, et al., 2003: Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sens. Environ., 86, 1–18, doi: https://doi.org/10.1016/S0034-4257(03)00054-3.
Lucht, W., C. B. Schaaf, and A. H. Strahler, 2000: An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Trans. Geosci. Remote Sens., 38, 977–998, doi: https://doi.org/10.1109/36.841980.
Meng, J. H., B. F. Wu, X. Du, et al., 2011: Method to construct high spatial and temporal resolution NDVI DataSetSTAVFM. J. Remote Sens., 15, 44–59. (in Chinese)
Montandon, L. M., and E. E. Small, 2008: The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sens. Environ., 112, 1835–1845, doi: https://doi.org/10.1016/j.rse.2007.09.007.
Mu, X. H., S. Huang, H. Z. Ren, et al., 2015: Validating GEOV1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 439–446, doi: https://doi.org/10.1109/JSTARS.2014.2342257.
Mu, X. H., W. J. Song, Z. Gao, et al., 2018: Fractional vegetation cover estimation by using multi-angle vegetation index. Remote Sens. Environ., 216, 44–56, doi: https://doi.org/10.1016/j.rse.2018.06.022.
Naqvi, H. R., J. Mallick, L. M. Devi, et al., 2013: Multi-temporal annual soil loss risk mapping employing Revised Universal Soil Loss Equation (RUSLE) model in Nun Nadi Watershed, Uttrakhand (India). Arabian J. Geosci., 6, 4045–4056, doi: https://doi.org/10.1007/s12517-012-0661-z.
Obata, K., T. Miura, and H. Yoshioka, 2012: Analysis of the scaling effects in the area-averaged fraction of vegetation cover retrieved using an NDVI-isoline-based linear mixture model. Remote Sens., 4, 2156–2180, doi: https://doi.org/10.3390/rs4072156.
O’Neill, A. L., 1994: Reflectance spectra of microphytic soil crusts in semi-arid Australia. Int. J. Remote Sens., 15, 675–681, doi: https://doi.org/10.1080/01431169408954106.
Pan, J. H., and Y. Wen, 2014: Estimation of soil erosion using RUSLE in Caijiamiao watershed, China. Nat. Hazards, 71, 2187–2205, doi: https://doi.org/10.1007/s11069-013-1006-2.
Post, D. F., A. Fimbres, A. D. Matthias, et al., 2000: Predicting soil albedo from soil color and spectral reflectance data. Soil Sci. Soc. Amer. J., 64, 1027–1034, doi: https://doi.org/10.2136/sssaj2000.6431027x.
Price, J. C., 1992: Estimating vegetation amount from visible and near infrared reflectances. Remote Sens. Environ., 41, 29–34, doi: https://doi.org/10.1016/0034-4257(92)90058-R.
Purevdorj, T., R. Tateishi, T. Ishiyama, et al., 1998: Relationships between percent vegetation cover and vegetation indices. Int. J. Remote Sens., 19, 3519–3535, doi: https://doi.org/10.1080/014311698213795.
Roujean, J.-L., and R. Lacaze, 2002: Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation. J. Geophys. Res. Atmos., 107, 4150, doi: https://doi.org/10.1029/2001JD000751.
Roy, D. P., J. C. Ju, P. Lewis, et al., 2008: Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens. Environ., 112, 3112–3130, doi: https://doi.org/10.1016/j.rse.2008.03.009.
Sexton, J. O., X.-P. Song, M. Feng, et al., 2013: Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth, 6, 427–448, doi: https://doi.org/10.1080/17538947.2013.786146.
Singh, R. P., S. Goroshi, N. K. Sharma, et al., 2011: Remote sensing based biophysical characterization of tropical deciduous forest in central India. ISPRS Bhopal 2011 Workshop, Bhopal, India, XXXVIII-8/W20, 145–149, doi: https://doi.org/10.5194/isprsarchives-XXXVIII-8-W20-145-2011.
Song, W. J., X. H. Mu, G. Y. Ruan, et al., 2017: Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. Int. J. Appl. Earth Obs. Geoinf., 58, 168–176, doi: https://doi.org/10.1016/j.jag.2017.01.015.
Verger, A., F. Baret, and M. Weiss, 2014: Near real-time vegetation monitoring at global scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 3473–3481, doi: https://doi.org/10.1109/JSTARS.2014.2328632.
Verger, A., F. Baret, M. Weiss, et al., 2015: GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999–2010. Remote Sens. Environ., 166, 126–137, doi: https://doi.org/10.1016/j.rse.2015.05.027.
Walker, J. J., K. M. de Beurs, and R. H. Wynne, 2014: Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data. Remote Sens. Environ., 144, 85–97, doi: https://doi.org/10.1016/j.rse.2014.01.007.
Wang, J. A., and W. Zuo, 2010: Geographic Atlas of China. SinoMaps Press, Beijing, 362 pp.
Weiss, D. J., P. M. Atkinson, S. Bhatt, et al., 2014: An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS J. Photogr. Remote Sens., 98, 106–118, doi: https://doi.org/10.1016/j.isprsjprs.2014.10.001.
Weiss, M., F. Baret, S. Garrigues, et al., 2007: LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS collection 4 products. Remote Sens. Environ., 110, 317–331, doi: https://doi.org/10.1016/j.rse.2007.03.001.
Wilson, A. M., and W. Jetz, 2016: Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol., 14, e1002415, doi: https://doi.org/10.1371/journal.pbio.1002415.
Wu, D. H., H. Wu, X. Zhao, et al., 2014: Evaluation of spatiotemporal variations of global fractional vegetation cover based on GIMMS NDVI data from 1982 to 2011. Remote Sens., 6, 4217–4239, doi: https://doi.org/10.3390/rs6054217.
Xiao, J. F., and A. Moody, 2005: A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sens. Environ., 98, 237–250, doi: https://doi.org/10.1016/j.rse.2005.07.011.
Xie, Y. Y., and A. M. Wilson, 2020: Change point estimation of deciduous forest land surface phenology. Remote Sens. Environ., 240, 111698, doi: https://doi.org/10.1016/j.rse.2020.111698.
Yan, G., X. Mu, and Y. Liu, 2012: Fractional vegetation cover. Advanced Remote Sensing, Liang, S. L., X. W. Li, and J. D. Wang, Eds., Academic Press, Amsterdam, 415–438.
Yang, L. Q., K. Jia, S. L. Liang, et al., 2017: A robust algorithm for estimating surface fractional vegetation cover from Landsat data. Remote Sens., 1, 857, doi: https://doi.org/10.3390/rs9080857.
Zeng, X. B., R. E. Dickinson, A. Walker, et al., 2000: Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. J. Appl. Meteor., 39, 826–839, doi: https://doi.org/10.1175/1520-0450(2000)039<0826:DAEOGK>2.0.CO;2.
Zhang, X., G. Yan, Q. Li, et al., 2006: Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model. Int. J. Remote Sens., 27, 5359–5372, doi: https://doi.org/10.1080/01431160600658107.
Zhang, X. P., D. L. Pan, J. Y. Chen, et al., 2013: Using long time series of Landsat data to monitor impervious surface dynamics: A case study in the Zhoushan Islands. J. Appl. Remote Sens., 7, 073515, doi: https://doi.org/10.1117/1.jrs.7.073515.
Zhang, X. S., 1993: A vegetation-climate classification system for global change studies in China. Quat. Sci., 13, 157–169. (in Chinese)
Zhang, X. W., and B. F. Wu, 2015: A temporal transformation method of fractional vegetation cover derived from high and moderate resolution remote sensing data. Acta Ecol. Sinica, 35, 1155–1164, doi: https://doi.org/10.5846/stxb201305020904. (in Chinese)
Zhang, Y. S., A. Harris, and H. Balzter, 2015: Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+. Int. J. Remote Sens., 36, 4213–4232, doi: https://doi.org/10.1080/01431161.2015.1079344.
Zhang, Z. X., X. Wang, X. L. Zhao, et al., 2014: A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images. Remote Sens. Environ., 149, 142–154, doi: https://doi.org/10.1016/j.rse.2014.04.004.
Zhu, X. L., J. Chen, F. Gao, et al., 2010: An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ., 114, 2610–2623, doi: https://doi.org/10.1016/j.rse.2010.05.032.
Acknowledgments
We thank China Centre for Resources Satellite Data and Application for providing HJ-1 data, Resource and Environment Science and Data Center (RESDC) for providing the land cover data, European Space Agency for providing free GEOV1 data, and the LP-DAAC and MODIS science team for providing free MODIS products. Thanks go to Wenwen Cai, Shuai Huang, and Jingyi Jiang for the data processing. We thank Professor Wenbo Zhang for sharing the FVC on soil erosion in watersheds and Jun Chen for drawing some of the important figures. We especially appreciate the contribution from Academician Xiaowen Li. We thank the two reviewers and Editor for helpful comments that improved an earlier version of this paper.
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Supported by the National Key Research and Development Program of China (2018YFC1506501, 2018YFA0605503, and 2016YFB0501502), Special Program of Gaofen Satellites (04-Y30B01-9001-18/20-3-1), and National Natural Science Foundation of China (41871230 and 41871231).
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Mu, X., Zhao, T., Ruan, G. et al. High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation. J Meteorol Res 35, 128–147 (2021). https://doi.org/10.1007/s13351-021-0017-2
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DOI: https://doi.org/10.1007/s13351-021-0017-2