Volume 29 Issue 5
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ZUO Lu, LIU Ronggao, LIU Yang, SHANG Rong. Effect of Mathematical Expression of Vegetation Indices on the Estima-tion of Phenology Trends from Satellite Data[J]. Chinese Geographical Science, 2019, 20(5): 756-767. doi: 10.1007/s11769-019-1070-y
Citation: ZUO Lu, LIU Ronggao, LIU Yang, SHANG Rong. Effect of Mathematical Expression of Vegetation Indices on the Estima-tion of Phenology Trends from Satellite Data[J]. Chinese Geographical Science, 2019, 20(5): 756-767. doi: 10.1007/s11769-019-1070-y

Effect of Mathematical Expression of Vegetation Indices on the Estima-tion of Phenology Trends from Satellite Data

doi: 10.1007/s11769-019-1070-y
Funds:  Under the auspices of the Strategic Priority Research Program of the Chinese Academy Sciences (No. XDA19080303), the National Key Research and Development Program for Global Change and Adaptation (No. 2016YFA0600201), the Distinctive Institutes Development Program, Chinese Academy of Sciences (No. TSYJS04), the National Natural Sciences Foudation of China (No. 41171285)
  • Received Date: 2019-01-24
  • Rev Recd Date: 2019-04-18
  • Publish Date: 2019-10-01
  • Vegetation indices (VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI (normalized difference vegetation index) and SR (simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date (SOS) and end date (EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.
  • [1] Atzberger C, Klisch A, Mattiuzzi M et al., 2014. Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series. Remote Sensing, 6(1):257-284. doi: 10.3390/rs6010257
    [2] Balzarolo M, Vicca S, Nguy-Robertson A L et al., 2016. Matching the phenology of Net Ecosystem Exchange and vegetation in-dices estimated with MODIS and FLUXNET in-situ observa-tions. Remote Sensing of Environment, 174:290-300. doi: 10.1016/j.rse.2015.12.017
    [3] Beck P S A, Atzberger C, Høgda K A et al., 2006. Improved mon-itoring of vegetation dynamics at very high latitudes:a new method using MODIS NDVI. Remote Sensing of Environment, 100(3):321-334. doi: 10.1016/j.rse.2005.10.021
    [4] Bradley B A, Jacob R W, Hermance J F et al., 2007. A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment, 106(2):137-145. doi: 10.1016/j.rse.2006.08.002
    [5] Buitenwerf R, Rose L, Higgins S I, 2015. Three decades of mul-ti-dimensional change in global leaf phenology. Nature Climate Change, 5(4):364-368. doi: 10.1038/nclimate2533
    [6] Chen J M, Pavlic G, Brown L et al., 2002. Derivation and valida-tion of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measure-ments. Remote Sensing of Environment, 80(1):165-184. doi: 10.1016/S0034-4257(01)00300-5
    [7] Chuine I, Morin X, Bugmann H, 2010. Warming, Photoperiods, and Tree Phenology. Science, 329(5989):277-278. doi: 10.1126/science.329.5989.277-e
    [8] Cong N, Wang T, Nan H J et al., 2013. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010:a multimethod analysis. Global Change Biology, 19(3):881-891. doi:10.1111/gcb. 12077
    [9] de Beurs K M, Henebry G M, 2005. Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects. Global Change Biology, 11(5):779-790. doi:10.1111/j.1365-2486.2005. 00949.x
    [10] Delpierre N, Dufrêne E, Soudani K et al., 2009. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agricultural and Forest Meteorology, 149(6-7):938-948. doi: 10.1016/j.agrformet.2008.11.014
    [11] Ding M J, Li L H, Zhang Y L et al., 2015. Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data. Journal of Geographical Sciences, 25(2):131-148. doi: 10.1007/s11442-015-1158-y
    [12] D'Odorico P, Gonsamo A, Gough C M et al., 2015. The match and mismatch between photosynthesis and land surface phenology of deciduous forests. Agricultural and Forest Meteorology, 214-215:25-38. doi:10.1016/j.agrformet.2015.07. 005
    [13] Fu Y H, Zhao H F, Piao S L et al., 2015. Declining global warm-ing effects on the phenology of spring leaf unfolding. Nature, 526(7571):104-107. doi: 10.1038/nature15402
    [14] Garonna I, de Jong R, Schaepman M E, 2016. Variability and evolution of global land surface phenology over the past three decades (1982-2012). Global Change Biology, 22(4):1456-1468. doi: 10.1111/gcb.13168.
    [15] Garrity S R, Bohrer G, Maurer K D et al., 2011. A comparison of multiple phenology data sources for estimating seasonal tran-sitions in deciduous forest carbon exchange. Agricultural and Forest Meteorology, 151(12):1741-1752. doi: 10.1016/j.agrformet.2011.07.008
    [16] Ge Q S, Wang H J, Rutishauser T et al., 2015. Phenological re-sponse to climate change in China:a meta-analysis. Global Change Biology, 21(1):265-274. doi: 10.1111/gcb.12648
    [17] Guo L, An Ning, Kaicun W, 2016. Reconciling the discrepancy in ground-and satellite-observed trends in the spring phenology of winter wheat in China from 1993 to 2008. Journal of Geo-physical Research:Atmospheres, 121:1027-42. doi: 10.1002/2015JD023969
    [18] Helman D, 2018. Land surface phenology:what do we really ‘see’ from space? Science of the Total Environment, 618:665-673. doi: 10.1016/j.scitotenv.2017.07.237
    [19] Hird J N, McDermid G J, 2009. Noise reduction of NDVI time series:an empirical comparison of selected techniques. Remote Sensing of Environment, 113(1):248-258. doi: 10.1016/j.rse.2008.09.003
    [20] Huete A, Didan K, Miura T et al., 2002. Overview of the radio-metric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2):195-213. doi: 10.1016/S0034-4257(02)00096-2
    [21] Jeganathan C, Dash J, Atkinson P M, 2014. Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sensing of Environment, 143:154-170. doi: 10.1016/j.rse.2013.11.020
    [22] Jeong S J, Ho C H, Gim H J et al., 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008. Global Change Biology, 17(7):2385-2399. doi:10.1111/j.1365-2486. 2011.02397.x
    [23] Jin H X, Eklundh L, 2014. A physically based vegetation index for improved monitoring of plant phenology. Remote Sensing of Environment, 152:512-525. doi: 10.1016/j.rse.2014.07.010
    [24] Karkauskaite P, Tagesson T, Fensholt R, 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for start-of-season trend analysis of the northern hemisphere boreal zone. Remote Sensing, 9(5):485. doi: 10.3390/rs9050485
    [25] Liang L, Schwartz M D, Fei S L, 2011. Validating satellite phe-nology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Envi-ronment, 115(1):143-157. doi: 10.1016/j.rse.2010.08.013
    [26] Lieth H, Radford J S, 1971. Phenology, resource management, and synagraphic computer mapping. BioScience, 21(881):62-70. doi: 10.2307/1295541
    [27] Liu R G, Liu Y, 2013. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sensing of Environment, 133:21-37. doi: 10.1016/j.rse.2013.01.019
    [28] Liu R G, Shang R, Liu Y et al., 2017. Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise re-sistance and curve stability. Remote Sensing of Environment, 189:164-179. doi: 10.1016/j.rse.2016.11.023
    [29] Menzel A, Sparks T H, Estrella N et al., 2006. European pheno-logical response to climate change matches the warming pattern. Global Change Biology, 12(10):1969-1976. doi: 10.1111/j.1365-2486.2006.01193.x.
    [30] Mutanga O, Skidmore A K, 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. In-ternational Journal of Remote Sensing, 25(19):3999-4014. doi: 10.1080/01431160310001654923
    [31] Nagai S, Nasahara K N, Muraoka H et al., 2010. Field experiments to test the use of the normalized-difference vegetation index for phenology detection. Agricultural and Forest Meteorology, 150(2):152-160. doi:10.1016/j.agrformet.2009.09. 010
    [32] Peng S S, Piao S L, Ciais P et al., 2013. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature, 501(7465):88-92. doi:10.1038/nature 1243
    [33] Peñuelas J, Filella I, 2001. Phenology; Responses to a warming world. Science, 294(5543):793-795. doi:10.1126/science. 1066860
    [34] Piao S L, Fang J Y, Zhou L M et al., 2006. Variations in satel-lite-derived phenology in China's temperate vegetation. Global Change Biology, 12(4):672-685. doi:10.1111/j.1365-2486. 2006.01123.x
    [35] Piao S L, Tan J G, Chen A P et al., 2015. Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 6:6911. doi: 10.1038/ncomms7911
    [36] Richardson A D, Black T A, Ciais P et al., 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B:Biological Sciences, 365(1555):3227-3246. doi: 10.1098/rstb.2010.0102
    [37] Schwartz M D, Ahas R, Aasa A, 2006. Onset of spring starting earlier across the Northern Hemisphere. Global Change Biol-ogy, 12(2):343-351. doi: 10.1111/j.1365-2486.2005.01097.x
    [38] Slayback D A, Pinzon J E, Los S O et al., 2003. Northern hemi-sphere photosynthetic trends 1982-99. Global Change Biology, 9(1):1-15. doi: 10.1046/j.1365-2486.2003.00507.x
    [39] Steltzer H, Post E, 2009. Seasons and Life Cycles. Science, 324(5929):886-887. doi: 10.1126/science.1171542
    [40] Studer S, Stockli R, Appenzeller C et al., 2007. A comparative study of satellite and ground-based phenology. International Journal of Biometeorology, 51(5):405-414. doi: 10.1007/s00484-006-0080-5
    [41] Vermote E F, Kotchenova S, 2008. Atmospheric correction for the monitoring of land surfaces. Journal of Geophysical Re-search-Atmospheres, 113(D23):D23S90. doi:10.1029/2007 JD009662
    [42] Viña A, Gitelson A A, Nguy-Robertson A L et al., 2011. Compar-ison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115(12):3468-3478. doi:10.1016/j.rse.2011. 08.010
    [43] White K, Pontius J, Schaberg P, 2014. Remote sensing of spring phenology in northeastern forests:a comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment, 148:97-107. doi: 10.1016/j.rse.2014.03.017
    [44] White M A, de Beurs K M, Didan K et al., 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 15(10):2335-2359. doi: 10.1111/j.1365-2486.2009.01910.x
    [45] Wu C Y, Gonsamo A, Gough C M et al., 2014. Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sensing of En-vironment, 147:79-88. doi: 10.1016/j.rse.2014.03.001
    [46] Wu C Y, Hou X H, Peng D L et al., 2016. Land surface phenology of China's temperate ecosystems over 1999-2013:Spa-tial-temporal patterns, interaction effects, covariation with climate and implications for productivity. Agricultural and Forest Meteorology, 216:177-187. doi:10.1016/j.agrformet. 2015.10.015
    [47] Yang Y T, Guan H D, Shen M G et al., 2015. Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010. Global Chang Biology, 21(2):652-665. doi:10.1111/gcb. 12778
    [48] Zhang G L, Zhang Y J, Dong J W et al., 2013. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proceedings of the National Academy of Sciences of the United States of America, 110(11):4309-4314. doi: 10.1073/pnas.1210423110
    [49] Zhang X Y, Friedl M A, Schaaf C B et al., 2003. Monitoring veg-etation phenology using MODIS. Remote Sensing of Environ-ment, 84(3):471-475. doi:10.1016/S0034-4257(02) 00135-9
    [50] Zhang X Y, Friedl M A, Schaaf C B et al., 2004. Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data. Global Change Biology, 10(7):1133-1145. doi: 10.1111/j.1529-8817.2003.00784.x
    [51] Zhang X Y, Friedl M A, Schaaf C B, 2006. Global vegetation phenology from Moderate Resolution Imaging Spectroradi-ometer (MODIS):evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Re-search-Biogeosciences, 111(G4):G04017. doi:10.1029/2006 JG000217
    [52] Zhao Hu, Yang Zhengwei, Li Lin et al., 2011. Improvement and comparative analysis of indices of crop growth condition mon-itoring by remote sensing. Transactions of the Chinese Socie-ty of Agricultural Engineering, 27(1):243-249. (in Chinese)
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Effect of Mathematical Expression of Vegetation Indices on the Estima-tion of Phenology Trends from Satellite Data

doi: 10.1007/s11769-019-1070-y
Funds:  Under the auspices of the Strategic Priority Research Program of the Chinese Academy Sciences (No. XDA19080303), the National Key Research and Development Program for Global Change and Adaptation (No. 2016YFA0600201), the Distinctive Institutes Development Program, Chinese Academy of Sciences (No. TSYJS04), the National Natural Sciences Foudation of China (No. 41171285)

Abstract: Vegetation indices (VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI (normalized difference vegetation index) and SR (simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date (SOS) and end date (EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.

ZUO Lu, LIU Ronggao, LIU Yang, SHANG Rong. Effect of Mathematical Expression of Vegetation Indices on the Estima-tion of Phenology Trends from Satellite Data[J]. Chinese Geographical Science, 2019, 20(5): 756-767. doi: 10.1007/s11769-019-1070-y
Citation: ZUO Lu, LIU Ronggao, LIU Yang, SHANG Rong. Effect of Mathematical Expression of Vegetation Indices on the Estima-tion of Phenology Trends from Satellite Data[J]. Chinese Geographical Science, 2019, 20(5): 756-767. doi: 10.1007/s11769-019-1070-y
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