Précédent|Prochain

Recent advances in remote sensing of vegetation phenology： Retrieval algorithm and validation strategy

  • role： First author第一作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • Email：wangminyu2021@163.com
  • Introduction：王敏钰，研究方向为基于静止卫星的植被物候遥感应用。E-mail： wangminyu2021@163.com
WANG Minyu1,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

LUO Yi1,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

ZHANG Zhengyang1,  
  • Affiliation：

    School of Life Sciences, University of Technology Sydney, Sydney NSW 2007, Australia

XIE Qiaoyun2,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

WU Xiaodan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • Email：xlma@lzu.edu.cn
  • Introduction：马轩龙，研究方向为植被与生态遥感。E-mail： xlma@lzu.edu.cn
MA Xuanlong1*

résumé

In the context of climate change, vegetation phenology, as a direct manifestation of the ecosystem’s response to environmental changes, has attracted increasing attention from the academic community. Obtaining long-term, continuous, multi-scale vegetation phenology data is the basis of phenological research, and the phenological parameters obtained by satellite remote sensing have become an important indicator of terrestrial ecosystem change. Remote sensing phenological parameters play an important role in the fields of agricultural production management, ecosystem monitoring, land use type mapping, human health, and ecosystem climate change response. In this context, key scientific issues and application fields must be combined to systematically sort out the progress in remote sensing phenological parameter extraction, verification, and product development and to predict to future development trends. First, this article discussed the development of emerging sunlight-induced chlorophyll fluorescence and vegetation optical thickness in addition to the traditional vegetation indices in phenological monitoring. Second, this paper discusses the advantages, disadvantages, and applicability of different time series data preprocessing and phenological metrics retrieval algorithms. Then, this article sorts out the development context of multi-source and -scale verification methods from the development of traditional phenological observations, phenological cameras, flux observations, and unmanned aerial vehicles. Meanwhile, this article introduces the development status of domestic and foreign phenology remote sensing products in recent years, with emphasis on product accuracy. Finally, this article systematically discusses the propagation of errors to the retrieved phenological metrics resulting from different aspects of data preprocessing, parameter extraction methods, and remote sensing data sources. On this basis, this article points out that future research in the field of vegetation phenology remote sensing should focus on the following: (1) better comparability between different research results should be targeted by improving the quality of remote sensing data sources and spatial and temporal consistency; (2) the subjectivity in the phenological retrieval algorithms should be reduced by developing universal algorithms; (3) the complete ground validation scheme should be established by leveraging the development in theory and method of quantitative remote sensing validation field; (4) the experience of using Chinese satellite data for monitoring vegetation phenology should be accumulated by actively extending the application of different Chinese spaceborne sensors. Through the above development, the overarching aim is to meet the demand for high-quality vegetation phenology remote sensing products in various scientific and practical applications.

mots-clés

remote sensing;vegetation phenology;growing season;Vegetation Dynamics;Ecosystem parameters;ground validation

References

  1. 1.
    Ahrends H E, Etzold S, Kutsch W L, Stoeckli R, Bruegger R, Jeanneret F, Wanner H, Buchmann N and Eugster W. 2009. Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale. Climate Research, 39(3): 261-274
  2. 2.
    Alemu W G and Henebry G M. 2017. Comparing passive microwave with visible-to-near-infrared phenometrics in croplands of northern Eurasia. Remote Sensing, 9(6): 613
  3. 3.
    Atkinson P M, Jeganathan C, Dash J and Atzberger C. 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123: 400-417
  4. 4.
    Badgley G, Field C B and Berry J A. 2017. Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3(3): e1602244
  5. 5.
    Baldocchi D. 2014. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere - the state and future of the eddy covariance method. Global Change Biology, 20(12): 3600-3609
  6. 6.
    Beck P S A, Atzberger C, Høgda K A, Johansen B and Skidmore A K. 2006. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sensing of Environment, 100(3): 321-334
  7. 7.
    Berra E F, Gaulton R and Barr S. 2017. Commercial off-the-shelf digital cameras on unmanned aerial vehicles for multitemporal monitoring of vegetation reflectance and NDVI. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 4878-4886
  8. 8.
    Berra E F, Gaulton R and Barr S. 2019. Assessing spring phenology of a temperate woodland: a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations. Remote Sensing of Environment, 223: 229-242
  9. 9.
    Bhandari S, Phinn S and Gill T. 2011. Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Australia. International Journal of Remote Sensing, 32(22): 7513-7538
  10. 10.
    Bórnez K, Richardson A D, Verger A, Descals A and Peñuelas J. 2020. Evaluation of VEGETATION and PROBA-V phenology using PhenoCam and eddy covariance data. Remote Sensing, 12(18): 3077
  11. 11.
    Broich M, Huete A, Paget M, Ma X L, Tulbure M, Coupe N R, Evans B, Beringer J, Devadas R, Davies K and Held A. 2015. A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications. Environmental Modelling and Software, 64: 191-204
  12. 12.
    Brown M E and de Beurs K M. 2008. Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sensing of Environment, 112(5): 2261-2271
  13. 13.
    Brown T B, Hultine K R, Steltzer H, Denny E G, Denslow M W, Granados J, Henderson S, Moore D, Nagai S, Sanclements M, Sánchez-Azofeifa A, Sonnentag O, Tazik D and Richardson A D. 2016. Using phenocams to monitor our changing Earth: toward a global phenocam network. Frontiers in Ecology and the Environment, 14(2): 84-93
  14. 14.
    Cai Z Z, Jönsson P, Jin H X and Eklundh L. 2017. Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sensing, 9(12): 1271
  15. 15.
    Cao R Y, Chen Y, Shen M G, Chen J, Zhou J, Wang C and Yang W. 2018. A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter. Remote Sensing of Environment, 217: 244-257
  16. 16.
    Caparros-Santiago J A, Rodriguez-Galiano V and Dash J. 2021. Land surface phenology as indicator of global terrestrial ecosystem dynamics: a systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 171: 330-347
  17. 17.
    Chen J, Jönsson P, Tamura M, Gu Z H, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote sensing of Environment, 91(3/4): 332-344
  18. 18.
    Chen J M, Deng F and Chen M Z. 2006. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2230-2238
  19. 19.
    Chen R, Yin G F, Liu G X, Li J and Verger A. 2020a. Evaluation and normalization of topographic effects on vegetation indices. Remote Sensing, 12(14): 2290
  20. 20.
    Chen X H, Wang W Q, Chen J, Zhu X L, Shen M G, Gan L Q and Cao X. 2020b. Does any phenological event defined by remote sensing deserve particular attention? An examination of spring phenology of winter wheat in Northern China. Ecological Indicators, 116: 106456
  21. 21.
    Chen X Q, Wang D W, Chen J, Wang C and Shen M G. 2018. The mixed pixel effect in land surface phenology: a simulation study. Remote Sensing of Environment, 211: 338-344
  22. 22.
    Chen X Q and Wang L H. 2009. Progress in remote sensing phenological research. Progress in Geography, 28(1): 33-40
  23. 23.
    Chen X Q and Yu R. 2007. Spatial and temporal variations of the vegetation growing season in warm-temperate Eastern China during 1982 to 1999. Acta Geographica Sinica, 62(1): 41-51
  24. 24.
    Chen Y, Cao R Y, Chen J, Liu L C and Matsushita B. 2021. A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter. ISPRS Journal of Photogrammetry and Remote Sensing, 180: 174-190
  25. 25.
    Cheng Y, Vrieling A, Fava F, Meroni M, Marshall M and Gachoki S. 2020. Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2. Remote Sensing of Environment, 248: 112004
  26. 26.
    Cremonese E. 2009. PHENOALP: a new project on phenology in the Western Alps. EGU General Assembly
  27. 27.
    Dai J H, Wang H J and Ge Q S. 2013. Changes of spring frost risks during the flowering period of woody plants in temperate monsoon area of China over the past 50 years. Acta Geographica Sinica, 68(5): 593-601
  28. 28.
    Dannenberg M, Wang X, Yan D and Smith W. 2020. Phenological characteristics of global ecosystems based on optical, fluorescence, and microwave remote sensing. Remote Sensing, 12(4): 671
  29. 29.
    De Grave C, Verrelst J, Morcillo-Pallarés P, Pipia L, Rivera-Caicedo J P, Amin E, Belda S and Moreno J. 2020. Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources. Remote Sensing of Environment, 251: 112101
  30. 30.
    Descals A, Verger A, Yin G F and Peñuelas J. 2020. Improved estimates of arctic land surface phenology using Sentinel-2 time series. Remote Sensing, 12(22): 3738
  31. 31.
    Didan K and Barreto A. 2016. Vegetation index and phenology (VIP) phenology EVI2 yearly global 0.05Deg CMG [Data set]. NASA EOSDIS Land Processes DAAC
  32. 32.
    Fan D Q, Zhao X S, Zhu W Q and Zheng Z T. 2016. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data. Progress in Geography, 35(3): 304-319
  33. 33.
    Fan L, Wigneron J P, Ciais P, Chave J, Brandt M, Fensholt R, Saatchi S S, Bastos A, Al-Yaari A, Hufkens K, Qin Y W, Xiao X M, Chen C, Myneni R B, Fernandez-Moran R, Mialon A, Rodriguez-Fernandez N J, Kerr Y, Tian F and Peñuelas J. 2019. Satellite-observed pantropical carbon dynamics. Nature Plants, 5(9): 944-951
  34. 34.
    Filippa G, Cremonese E, Migliavacca M, Galvagno M, Folker M, Richardson A D and Tomelleri E. 2020. phenopix: process digital images of a vegetation cover. R package version 2.4.2[EB/OL]. [2021-12-01].
  35. 35.
    Filippa G, Cremonese E, Migliavacca M, Galvagno M, Sonnentag O, Humphreys E, Hufkens K, Ryu Y, Verfaillie J, di Cella U M and Richardson A D. 2018. NDVI derived from near-infrared-enabled digital cameras: applicability across different plant functional types. Agricultural and Forest Meteorology, 249: 275-285
  36. 36.
    Fischer A. 1994. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment, 48(2): 220-230
  37. 37.
    Friedl M. 2020. MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V001 [Data set]. NASA EOSDIS Land Processes DAAC
  38. 38.
    Fu Y H, Zhao H F, Piao S L, Peaucelle M, Peng S S, Zhou G Y, Ciais P, Huang M T, Menzel A, Peñuelas J, Song Y, Vitasse Y, Zeng Z Z and Janssens I A. 2015. Declining global warming effects on the phenology of spring leaf unfolding. Nature, 526(7571): 104-107
  39. 39.
    Galvão L S, Breunig F M, dos Santos J R and de Moura Y M. 2013. View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest. International Journal of Applied Earth Observation and Geoinformation, 21: 291-300
  40. 40.
    Ganguly S, Friedl M A, Tan B, Zhang X Y and Verma M. 2010. Land surface phenology from MODIS: characterization of the Collection 5 global land cover dynamics Product. Remote Sensing of Environment, 114(8): 1805-1816
  41. 41.
    Gao F and Zhang X Y. 2021. Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities. Journal of Remote Sensing, 2021: 8379391
  42. 42.
    Gao X J, Gray J M and Reich B J. 2021. Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261: 112484
  43. 43.
    Gatis N, Anderson K, Grand-Clement E, Luscombe D J, Hartley I P, Smith D and Brazier R E. 2017. Evaluating MODIS vegetation products using digital images for quantifying local peatland CO2 gas fluxes. Remote Sensing in Ecology and Conservation, 3(4): 217-231
  44. 44.
    Ge M X, Zhao J, Zhong B and Yang A X. 2017. Comparison of the Vegetation Indexes between FY-3/VIRR, FY-3/MERSI and EOS/MODIS Data. Remote Sensing Technology and Application, 32(2): 262-273
  45. 45.
    Ge Q S, Dai J H and Zheng J Y. 2010. The progress of phenology studies and challenges to modern phenology research in China. Bulletin of the Chinese Academy of Sciences, 25(3): 310-316
  46. 46.
    Ge Q S, Wang H J and Dai J H. 2013. Shifts in spring phenophases, frost events and frost risk for woody plants in temperate China. Climate Research, 57(3): 249-258
  47. 47.
    Grabska E, Hostert P, Pflugmacher D and Ostapowicz K. 2019. Forest stand species mapping using the Sentinel-2 time series. Remote Sensing, 11(10): 1197
  48. 48.
    Gray J, Sulla-Menashe D and Friedl M. 2019a. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC
  49. 49.
    Gray J, Sulla-Menashe D and Friedl M A. 2019b. User Guide to Collection 6 MODIS Land Cover Dynamics (MCD12Q2) Product[EB/OL]. [2021-12-01].
  50. 50.
    Guo Q H, Hu T Y, Ma Q, Xu K X, Yang Q L, Sun Q H, Li Y M and Su Y J. 2020. Advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology, 44(4): 418-435
  51. 51.
    Guo Y S, Liu Q S, Liu G H and Huang C. 2017. Extraction of main crops in yellow river delta based on MODIS NDVI time series. Journal of Natural Resources, 32(10): 1808-1818
  52. 52.
    Hanes J M, Liang L and Morisette J T. 2014. Land surface phenology//Biophysical Applications of Satellite Remote Sensing. Berlin, Heidelberg: Springer: 99-125
  53. 53.
    Hänninen H. 2006. Climate warming and the risk of frost damage to boreal forest trees: identification of critical ecophysiological traits. Tree Physiology, 26(7): 889-898
  54. 54.
    Hao D L, Xiao Q, Wen J G, You D Q, Wu X D, Lin X W and Wu S B. 2018. Advances in upscaling methods of quantitative remote sensing. Journal of Remote Sensing, 22(3): 408-423
  55. 55.
    Henebry G M and de Beurs K M. 2013. Remote sensing of land surface phenology: a prospectus//Schwartz M D. Phenology: An Integrative Environmental Science. 2nd ed. Dordrecht: Springer: 385-411
  56. 56.
    Hilker T, Gitelson A, Coops N C, Hall F G and Black T A. 2011. Tracking plant physiological properties from multi-angular tower-based remote sensing. Oecologia, 165(4): 865-876
  57. 57.
    Hill A C, Vázquez-Lule A and Vargas R. 2021. Linking vegetation spectral reflectance with ecosystem carbon phenology in a temperate salt marsh. Agricultural and Forest Meteorology, 307: 108481
  58. 58.
    Hinojo-Hinojo C and Goulden M L. 2020. Plant traits help explain the tight relationship between vegetation indices and gross primary production. Remote Sensing, 12(9): 1405
  59. 59.
    Hou X H, Niu Z and Gao S and Huang N. 2013. Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data. Transactions of the Chinese Society of Agricultural Engineering, 29(01): 142-150
  60. 60.
    Hou X H, Sui X Y, Liang S Z, Wang M and Dong M. 2017. Comparison of five methods for phenology extraction of winter wheat. Remote Sensing Information, 32(6): 65-70
  61. 61.
    Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213
  62. 62.
    Ibrahim S, Balzter H, Tansey K, Tsutsumida N and Mathieu R. 2018. Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data. International Journal of Remote Sensing, 39(9): 2718-2745
  63. 63.
    Jeong S J, Schimel D, Frankenberg C, Drewry D T, Fisher J B, Verma M, Berry J A, Lee J E and Joiner J. 2017. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sensing of Environment, 190: 178-187
  64. 64.
    Ji L and Brown J F. 2017. Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics. International Journal of Applied Earth Observation and Geoinformation, 62: 215-223
  65. 65.
    Jiang Z Y, Huete A R, Kim Y and Didan K. 2007. 2-band enhanced vegetation index without a blue band and its application to AVHRR data//Proceedings Volume 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV. San Diego, California, United States: SPIE
  66. 66.
    Jin H X and Eklundh L. 2014. A physically based vegetation index for improved monitoring of plant phenology. Remote Sensing of Environment, 152: 512-525
  67. 67.
    Jones M O, Jones L A, Kimball J S and McDonald K C. 2011. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sensing of Environment, 115(4): 1102-1114
  68. 68.
    Jönsson P and Eklundh L. 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824-1832
  69. 69.
    Justice C O, Townshend J R G, Holben B N and Tucker C J. 1985. Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6(8): 1271-1318
  70. 70.
    Klosterman S, Melaas E, Wang J A, Martinez A, Frederick S, O’Keefe J, Orwig D A, Wang Z S, Sun Q S, Schaaf C, Friedl M and Richardson A D. 2018. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agricultural and Forest Meteorology, 248: 397-407
  71. 71.
    Kong D D, Zhang Q, Huang W L and Gu X H. 2017. Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors. Acta Geographica Sinica, 72(1): 39-52
  72. 72.
    Li H, Peng R C, Li W K, Zhu X L, Huang Y T and Nie Q. 2019. Filtering algorithms of HJ-1 A/B NDVI time series data and phenology of typical tree species in Xiamen. Chinese Journal of Ecology, 38(11): 3460-3471
  73. 73.
    Li J, Xia C F, Liu Q H, Zhao J, Wang C and Peng J J. 2017. 1 km growing season length data product over China-ASEAN 2013. Journal of Global Change Data and Discovery, 1(3): 278-281
  74. 74.
    Li N, Zhan P, Pan Y Z, Zhu X F, Li M Y and Zhang D J. 2020. Comparison of remote sensing time-series smoothing methods for grassland spring phenology extraction on the Qinghai-Tibetan Plateau. Remote Sensing, 12(20): 3383
  75. 75.
    Li Q, Shen M G, Chen X H, Wang C, Chen J, Cao X, Fei S L and Cui X H. 2021. Optimal color composition method for generating high-quality daily photographic time series from PhenoCam. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 6179-6193
  76. 76.
    Liang L and Schwartz M D. 2009. Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landscape Ecology, 24(4): 465-472
  77. 77.
    Liang L, Schwartz M D and Fei S L. 2011. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Environment, 115(1): 143-157
  78. 78.
    Liang S L. 2021. Some thoughts on the development of quantitative remote sensing in China. National Remote Sensing Bulletin, 25(9)：1889-1895
  79. 79.
    Liu J W and Zhou Y K. 2018. Comparison and application of NDVI time-series reconstruction methods at site scale on the Tibetan Plateau. Progress in Geography, 37(3): 427-437
  80. 80.
    Liu L C, Cao R Y, Shen M G, Chen J, Wang J M and Zhang X Y. 2019. How does scale effect influence spring vegetation phenology estimated from satellite-derived vegetation indexes?. Remote Sensing, 11(18): 2137
  81. 81.
    Liu X T, Zhou L, Shi H, Wang S Q and Chi Y G. 2018. Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyII fluorescence and CO2 flux data. Acta Ecologica Sinica, 38(10): 3482-3494
  82. 82.
    Lobell D B, Burke M B, Tebaldi C, Mastrandrea M D, Falcon W P and Naylor R L. 2008. Prioritizing climate change adaptation needs for food security in 2030. Science, 319(5863): 607-610
  83. 83.
    Luo Y C, Zhang Z, Chen Y, Li Z Y and Tao F L. 2020. ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000-2015 based on leaf area index (LAI) products. Earth System Science Data, 12(1): 197-214
  84. 84.
    Ma X F, Chen S Y, Deng J, Feng Q S and Huang X D. 2016. Vegetation phenology dynamics and its response to climate change on the Tibetan Plateau. Acta Prataculturae Sinica, 25(1): 13-21
  85. 85.
    Ma X L, Huete A, Moore C E, Cleverly J, Hutley L B, Beringer J, Leng S, Xie Z Y, Yu Q and Eamus D. 2020. Spatiotemporal partitioning of savanna plant functional type productivity along NATT. Remote Sensing of Environment,246: 111855. [DOI ]
  86. 86.
    Ma X L, Huete A and Tran N N. 2019. Interaction of seasonal sun-angle and savanna phenology observed and modelled using MODIS. Remote Sensing, 11(12): 1398
  87. 87.
    Ma X L, Huete A, Yu Q, Coupe N R, Davies K, Broich M, Ratana P, Beringer J, Hutley L B, Cleverly J, Boulain N and Eamus D. 2013. Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect. Remote Sensing of Environment, 139: 97-115
  88. 88.
    Macfarlane C, Ryu Y, Ogden G N and Sonnentag O. 2014. Digital canopy photography: exposed and in the raw. Agricultural and Forest Meteorology, 197: 244-253
  89. 89.
    Maeda E E and Galvão L S. 2015. Sun-sensor geometry effects on vegetation index anomalies in the Amazon rainforest. GIScience and Remote Sensing, 52(3): 332-343
  90. 90.
    Meng J H and Wu B F. 2013. The feasibility analysis on satellite data based crop mature date prediction. Remote Sensing Technology and Application, 28(2): 165-173
  91. 91.
    Miura T, Nagai S, Takeuchi M, Ichii K and Yoshioka H. 2019. Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data. Scientific Reports, 9(1): 15692
  92. 92.
    Moon M, Richardson A D and Friedl M A. 2021. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery. Remote Sensing of Environment, 266: 112716
  93. 93.
    Moon M, Zhang X Y, Henebry G M, Liu L L, Gray J M, Melaas E K and Friedl M A. 2019. Long-term continuity in land surface phenology measurements: a comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products. Remote Sensing of Environment, 226: 74-92
  94. 94.
    Mou C X, Sun G, Luo P, Wang Z Y and Luo G R. 2013. Flowering responses of alpine meadow plant in the Qinghai-Tibetan Plateau to extreme drought imposed in different periods. Chinese Journal of Applied and Environmental Biology, 19(2): 272-279
  95. 95.
    Moulin S, Kergoat L, Viovy N and Dedieu G. 1997. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. Journal of Climate, 10(6): 1154-1170
  96. 96.
    Nagai S, Nasahara K N, Inoue T, Saitoh T M and Suzuki R. 2016. Review: advances in in situ and satellite phenological observations in Japan. International Journal of Biometeorology, 60(4): 615-627
  97. 97.
    Nasahara K N and Nagai S. 2015. Review: development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN). Ecological Research, 30(2): 211-223
  98. 98.
    Ni L, Wu J, Li C B, Qin G X, Li Z and Kong J. 2020. Temporal and spatial variations in natural grassland phenology in China over the last 30 years. Acta Prataculturae Sinica, 29(1): 1-12
  99. 99.
    Norris J R and Walker J J. 2020. Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States. Remote Sensing of Environment, 249: 112013
  100. 100.
    Peng D L, Zhang X Y, Wu C Y, Huang W J, Gonsamo A, Huete A R, Didan K, Tan B, Liu X J and Zhang B. 2017a. Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States. Agricultural and Forest Meteorology, 242: 33-46
  101. 101.
    Peng D L, Zhang X Y, Zhang B, Liu L Y, Liu X J, Huete A R, Huang W J, Wang S Y, Luo S Z, Zhang X and Zhang H L. 2017b. Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States. ISPRS Journal of Photogrammetry and Remote Sensing, 132: 185-198
  102. 102.
    Petri C A and Galvão L S. 2019. Sensitivity of seven MODIS vegetation indices to BRDF effects during the Amazonian dry season. Remote Sensing, 11(14): 1650
  103. 103.
    Piao S L, Ciais P, Friedlingstein P, Peylin P, Reichstein M, Luyssaert S, Margolis H, Fang J Y, Barr A, Chen A P, Grelle A, Hollinger D Y, Laurila T, Lindroth A, Richardson A D and Vesala T. 2008. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(7174): 49-52
  104. 104.
    Piao S L, Fang J Y, Zhou L M, Ciais P and Zhu B. 2006. Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12(4): 672-685
  105. 105.
    Piao S L, Liu Q, Chen A P, Janssens I A, Fu Y S, Dai J H, Liu L L, Lian X, Shen M G and Zhu X L. 2019. Plant phenology and global climate change: current progresses and challenges. Global Change Biology, 25(6): 1922-1940
  106. 106.
    Qi W L and Dubayah R O. 2016. Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping. Remote Sensing of Environment, 187: 253-266
  107. 107.
    Qi W L, Lee S K, Hancock S, Luthcke S, Tang H, Armston J and Dubayah R. 2019. Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data. Remote Sensing of Environment, 221: 621-634
  108. 108.
    Reed B C, Brown J F, Vanderzee D, Loveland T R, Merchant J W and Ohlen D O. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5): 703-714
  109. 109.
    Reed B C, Schwartz M D and Xiao X M. 2009. Remote sensing phenology//Noormets A. Phenology of Ecosystem Processes: Applications in Global Change Research. New York, NY: Springer: 231-246
  110. 110.
    Restrepo-Coupe N, Huete A, Davies K, Cleverly J, Beringer J, Eamus D, Van Gorsel E, Hutley L B and Meyer W S. 2016. MODIS vegetation products as proxies of photosynthetic potential along a gradient of meteorologically and biologically driven ecosystem productivity. Biogeosciences, 13(19): 5587-5608
  111. 111.
    Richardson A D, Anderson R S, Arain M A, Barr A G, Bohrer G, Chen G S, Chen J M, Ciais P, Davis K J, Desai A R, Dietze M C, Dragoni D, Garrity S R, Gough C M, Grant R, Hollinger D Y, Margolis H A, McCaughey H, Migliavacca M, Monson R K, Munger J W, Poulter B, Raczka B M, Ricciuto D M, Sahoo A K, Schaefer K, Tian H Q, Vargas R, Verbeeck H, Xiao J F and Xue Y K. 2012. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Global Change Biology, 18(2): 566-584
  112. 112.
    Richardson A D, Hufkens K, Li X L and Ault T R. 2019. Testing Hopkins’ Bioclimatic Law with PhenoCam data. Applications in Plant Sciences, 7(3): e01228
  113. 113.
    Richardson A D, Hufkens K, Milliman T, Aubrecht D M, Chen M, Gray J M, Johnston M R, Keenan T F, Klosterman S T, Kosmala M, Melaas E K, Friedl M A and Frolking S. 2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data, 5(1): 180028
  114. 114.
    Rouse J W, Haas R H, Schell J A and Deering D W. 1973. Monitoring vegetation systems in the Great Plains with ERTS//3rd ERTS-1 Symposium. Washington: NASA: 309-317
  115. 115.
    Running S W and Hunt E R. 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models//Scaling Physiological Processes: Leaf to Globe. San Diego, USA: Academic Press: 141-158
  116. 116.
    Sakamoto T, Van Nguyen N, Ohno H, Ishitsuka N and Yokozawa M. 2006. Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sensing of Environment, 100(1): 1-16
  117. 117.
    Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N and Ohno H. 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3/4): 366-374
  118. 118.
    Salas E A L. 2021. Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review. Geo-spatial Information Science, 24(2): 179-200
  119. 119.
    Sellers P J, Tucker C J, Collatz G J, Los S O, Justice C O, Dazlich D A and Randall D A. 1994. A global 1° by 1° NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing, 15(7): 3519-3545
  120. 120.
    Shen M G, Piao S L, Dorji T, Liu Q, Cong N, Chen X Q, An S, Wang S P, Wang T and Zhang G X. 2015. Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges. National Science Review, 2(4): 454-467
  121. 121.
    Shen M G, Tang Y H, Chen J and Yang W. 2012. Specification of thermal growing season in temperate China from 1960 to 2009. Climatic Change, 114(3/4): 783-798
  122. 122.
    Song D Y, Wang Z Y, Li Y and Hu Y. 2018. Cropland phenology detection based on HJ-1A/B CCD data in Jianghan plain. Geomatics and Spatial Information Technology, 41(3): 128-131, 134
  123. 123.
    Sun H S, Xu A G, Lin H and Zhang L P. 2012. Analysis of denoising effect of time series vegetation index based on different algorithms. Jiangsu Agricultural Sciences, 40(5): 375-379
  124. 124.
    Taylor S D, Browning D M, Baca R A and Gao F. 2021. Constraints and opportunities for detecting land surface phenology in drylands. Journal of Remote Sensing, 2021: 9859103
  125. 125.
    Tian F, Cai Z Z, Jin H X, Hufkens K, Scheifinger H, Tagesson T, Smets B, Van Hoolst R, Bonte K, Ivits E, Tong X Y, Ardö J and Eklundh L. 2021a. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment, 260: 112456
  126. 126.
    Tian F, Wigneron J P, Ciais P, Chave J, Ogée J, Peñuelas J, Ræbild A, Domec J C, Tong X Y, Brandt M, Mialon A, Rodriguez-Fernandez N, Tagesson T, Al-Yaari A, Kerr Y, Chen C, Myneni R B, Zhang W M, Ardö J and Fensholt R. 2018. Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite. Nature Ecology and Evolution, 2(9): 1428-1435
  127. 127.
    Tian J Q, Zhu X L, Chen J, Wang C, Shen M G, Yang W, Tan X Y, Xu S and Li Z L. 2021b. Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency. ISPRS Journal of Photogrammetry and Remote Sensing, 180: 29-44
  128. 128.
    Tong X Y, Tian F, Brandt M, Liu Y, Zhang W M and Fensholt R. 2019. Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992-2012. Remote Sensing of Environment, 232: 111307
  129. 129.
    Verbesselt J, Hyndman R, Newnham G and Culvenor D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106-115
  130. 130.
    Verhegghen A, Bontemps S and Defourny P. 2014. A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations. International Journal of Remote Sensing, 35(7): 2440-2471
  131. 131.
    Wan C J, Wu X D and Lin X W. 2019. Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements. Journal of Remote Sensing, 23(6): 1064-1077
  132. 132.
    Wang C, Beringer J, Hutley L B, Cleverly J, Li J, Liu Q H and Sun Y. 2019. Phenology dynamics of dryland ecosystems along the North Australian tropical transect revealed by satellite solar-induced chlorophyll fluorescence. Geophysical Research Letters, 46(10): 5294-5302
  133. 133.
    Wang C, Chen J, Wu J, Tang Y H, Shi P J, Black T A and Zhu K. 2017a. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sensing of Environment, 196: 1-12
  134. 134.
    Wang C, Li J, Liu Q H, Zhong B, Wu S L and Xia C F. 2017b. Analysis of differences in phenology extracted from the enhanced vegetation index and the leaf area index. Sensors, 17(9): 1982
  135. 135.
    Wang S H and Zhang Y G. 2020. Long-term (1982-2018) global gross primary production dataset based on NIRv. National Tibetan Plateau Data Center
  136. 136.
    Wang X F, Xiao J F, Li X, Cheng G D, Ma M G, Che T, Dai L Y, Wang S Y and Wu J K. 2017c. No consistent evidence for advancing or delaying trends in spring phenology on the Tibetan Plateau. Journal of Geophysical Research: Biogeosciences, 122(12): 3288-3305
  137. 137.
    Wang Y W, Xu W F, Yuan W P, Chen X Z, Zhang B W, Fan L, He B, Hu Z M, Liu S G, Liu W and Piao S L. 2021. Higher plant photosynthetic capability in autumn responding to low atmospheric vapor pressure deficit. The Innovation, 2(4): 100163
  138. 138.
    Wei J, Li Z Q, Peng Y R and Sun L. 2019. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison. Atmospheric Environment, 201: 428-440
  139. 139.
    Wen G and Fu C B. 2000. Large scale features of the seasonal phenological responses to the monsoon climate in East China: Multi-year average results. Chinese Journal of Atmospheric Sciences, 24(5): 676-682
  140. 140.
    Westerling A L, Hidalgo H G, Cayan D R and Swetnam T W. 2006. Warming and earlier spring increase western U.S. forest wildfire activity. Science, 313(5789): 940-943
  141. 141.
    White M A, de Beurs K M, Didan K, Inouye D W, Richardson A D, Jensen O P, O’Keefe J, Zhang G, Nemani R R, Van Leeuwen W J D, Brown J F, De Wit A, Schaepman M, Lin X M, Dettinger M, Bailey A S, Kimball J, Schwartz M D, Baldocchi D D, Lee J T and Lauenroth W K. 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
  142. 142.
    White M A and Nemani R R. 2006. Real-time monitoring and short-term forecasting of land surface phenology. Remote Sensing of Environment, 104(1): 43-49
  143. 143.
    Wingate L, Ogée J, Cremonese E, Filippa G, Mizunuma T, Migliavacca M, Moisy C, Wilkinson M, Moureaux C, Wohlfahrt G, Hammerle A, Hörtnagl L, Gimeno C, Porcar-Castell A, Galvagno M, Nakaji T, Morison J, Kolle O, Knohl A, Kutsch W, Kolari P, Nikinmaa E, Ibrom A, Gielen B, Eugster W, Balzarolo M, Papale D, Klumpp K, Köstner B, Grünwald T, Joffre R, Ourcival J M, Hellstrom M, Lindroth A, George C, Longdoz B, Genty B, Levula J, Heinesch B, Sprintsin M, Yakir D, Manise T, Guyon D, Ahrends H, Plaza-Aguilar A, Guan J H and Grace J. 2015. Interpreting canopy development and physiology using a European phenology camera network at flux sites. Biogeosciences, 12(20): 5995-6015
  144. 144.
    Wong C Y S, D’Odorico P, Bhathena Y, Arain M A and Ensminger I. 2019. Carotenoid based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf-scale in deciduous and evergreen trees. Remote Sensing of Environment, 233: 111407
  145. 145.
    Wu S B, Wang J, Yan Z B, Song G Q, Chen Y, Ma Q, Deng M F, Wu Y T, Zhao Y Y, Guo Z F, Yuan Z Q, Dai G H, Xu X T, Yang X, Su Y J, Liu L L and Wu J. 2021. Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations. ISPRS Journal of Photogrammetry and Remote Sensing, 171: 36-48
  146. 146.
    Wu X D, Wen J G, Xiao Q, Li X, Liu Q, Tang Y, Dou B C, Peng J J, You D Q and Li X W. 2015. Advances in validation methods for remote sensing products of land surface parameters. Journal of Remote Sensing, 19(1): 75-92
  147. 147.
    Wu Y F, Li M S and Song J Q. 2008. Advance in vegetation phenology monitoring based on remote sensing. Journal of Meteorology and Environment, 24(3): 51-58
  148. 148.
    Wu Y Q and Huang L R. 2004. A new interpolation method in time series analyzing. Journal of Geodesy and Geodynamics, 24(4): 43-47
  149. 149.
    Wulder M A, Hilker T, White J C, Coops N C, Masek J G, Pflugmacher D and Crevier Y. 2015. Virtual constellations for global terrestrial monitoring. Remote Sensing of Environment, 170(1): 62-76. ]DOI ]
  150. 150.
    Xi X and Zhao G X. 2020. Chlorophyll content in winter wheat: inversion and monitoring based on UAV multi-spectral remote sensing. Chinese Agricultural Science Bulletin, 36(20): 119-126
  151. 151.
    Xia C F, Li J and Liu Q H. 2013. Review of advances in vegetation phenology monitoring by remote sensing. Journal of Remote Sensing, 17(1): 1-16
  152. 152.
    Xiang M T, Wei W and Wu W B. 2018. Review of vegetation phenology estimation by using remote sensing. China Agricultural Information, 30(1): 55-66
  153. 153.
    Xiang Y, Xiao Z Q, Liang S L, Wang J D and Song J L. 2014. Validation of Global Land Surface Satellite (GLASS) leaf area index product. Journal of Remote Sensing, 18(3): 573-596
  154. 154.
    Xiao W W, Sun Z G, Wang Q X and Yang Y H. 2013. Evaluating MODIS phenology product for rotating croplands through ground observations. Journal of Applied Remote Sensing, 7(1): 073562
  155. 155.
    Xiao X M, Zhang J H, Yan H M, Wu W X and Biradar C. 2009. Land surface phenology//Phenology of Ecosystem Processes. New York: Springer-Verlag: 247-270
  156. 156.
    Xie Y Y and Wilson A M. 2020. Change point estimation of deciduous forest land surface phenology. Remote Sensing of Environment, 240: 111698
  157. 157.
    Xu D W, Wang C, Chen J, Shen M G, Shen B B, Yan R R, Li Z W, Karnieli A, Chen J Q, Yan Y C, Wang X, Chen B R, Yin D M and Xin X P. 2021. The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Remote Sensing of Environment, 264: 112578
  158. 158.
    Yan D, Zhang X Y, Nagai S, Yu Y Y, Akitsu T, Nasahara K N, Ide R and Maeda T. 2019. Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network. International Journal of Applied Earth Observation and Geoinformation, 79: 71-83
  159. 159.
    Yan Y and Zhang C J. 2020. Preliminary study on remote sensing monitoring of grassland returning green period in the north slope of Tianshan Mountain based on different vegetation indexes. Grass-Feeding Livestock, (2): 40-45
  160. 160.
    Yin G F, Verger A, Filella I, Descals A and Peñuelas J. 2020. Divergent estimates of forest photosynthetic phenology using structural and physiological vegetation indices. Geophysical Research Letters, 47(18): e2020GL089167
  161. 161.
    Yu L, Wen J, Chang C Y, Frankenberg C and Sun Y. 2019. High-resolution global contiguous SIF of OCO-2. Geophysical Research Letters, 46(3): 1449-1458
  162. 162.
    Yu W T, Li J, Liu Q H, Zhao J, Dong Y D, Zhu X R, Lin S R, Zhang H and Zhang Z X. 2021. Gap filling for historical Landsat NDVI time series by integrating climate data. Remote Sensing, 13(3): 484
  163. 163.
    Yu X F and Zhuang D F. 2006. Monitoring forest phenophases of northeast China based on MODIS NDVI data. Resources Science, 28(4): 111-117
  164. 164.
    Yuan J W and Ni J. 2007. Plant signals and ecological evidences of climate change in China. Arid Land Geography, 30(4): 465-473
  165. 165.
    Zahawi R A, Dandois J P, Holl K D, Nadwodny D, Reid J L and Ellis E C. 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation, 186: 287-295
  166. 166.
    Zeng L L, Wardlow B D, Xiang D X, Hu S and Li D R. 2020. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment, 237: 111511
  167. 167.
    Zhang L, Wang C Z, Yang H X, Zhang B H and Zheng Y. 2017. Phenological metrics dataset, land cover types map for the Tibetan Plateau and grassland biomass dataset for Qinghai Lake Basin. China Scientific Data, 2(2): 79-94
  168. 168.
    Zhang X Y, Friedl M A and Schaaf C B. 2006. Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research: Biogeosciences, 111(G4): G04017
  169. 169.
    Zhang X Y, Friedl M A and Schaaf C B. 2009. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing, 30(8): 2061-2074
  170. 170.
    Zhang X Y, Friedl M A, Schaaf C B and Strahler A H. 2004. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10(7): 1133-1145
  171. 171.
    Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, Reed B C and Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475
  172. 172.
    Zhang X X, Ge Q S and Zheng J Y. 2004. Relationships between climate change and vegetation in Beijing using remote sensed data and phenological data. Acta Phytoecologica Sinica, 28(4): 499-506
  173. 173.
    Zhang X Y, Jayavelu S, Liu L L, Friedl M A, Henebry G M, Liu Y, Schaaf C B, Richardson A D and Gray J. 2018b. Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery. Agricultural and Forest Meteorology, 256-257: 137-149
  174. 174.
    Zhang X Y, Liu L L, Liu Y, Jayavelu S, Wang J M, Moon M, Henebry G M, Friedl M A and Schaaf C B. 2018a. Generation and evaluation of the VIIRS land surface phenology product. Remote Sensing of Environment, 216: 212-229
  175. 175.
    Zhang X Y, Wang J M, Henebry G M and Gao F. 2020. Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 37-51
  176. 176.
    Zhang Y, Joiner J, Alemohammad S H, Zhou S and Gentine P. 2018c. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences, 15(19): 5779-5800
  177. 177.
    Zhang Y G, Zhang Q, Liu L Y, Zhang Y J, Wang S Q, Ju W M, Zhou G S, Zhou L, Tang J W, Zhu X D, Wang F, Huang Y, Zhang Z Y, Qiu B, Zhang X K, Wang S H, Huang C P, Tang X G and Zhang J S. 2021. ChinaSpec: a network for long-term ground-based measurements of solar-induced fluorescence in China. Journal of Geophysical Research: Biogeosciences, 126(3): e2020JG006042
  178. 178.
    Zheng Z T and Zhu W Q. 2017. Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau. Remote Sensing, 9(12): 1288. [DOI ]
  179. 179.
    Zhou H H, Fu D J, Zhang L F, Wang W S, Cen Y and Wang J N. 2016. Modeling grassland phenology and analyzing relationship with corresponding meteorological factors based on digital camera. Remote Sensing Technology and Application, 31(5): 966-974
  180. 180.
    Zhou K M, Lin H, Song R F, Jiang Q, Chen Z M and Du K. 2020. Comparison of two fitting methods for NDVI of the deciduous period of Ginkgo leaves. Journal of Central South University of Forestry and Technology, 40(2): 95-101
  181. 181.
    Zhou L, Chi Y G, Liu X T, Dai X Q and Yang F T. 2020. Land surface phenology tracked by remotely sensed sun-induced chlorophyll fluorescence in subtropical evergreen coniferous forests. Acta Ecologica Sinica, 40(12): 4114-4125
  182. 182.
    Zhou Y K. 2018. Comparative study of vegetation phenology extraction methods based on digital images. Progress in Geography, 37(8): 1031-1044
  183. 183.
    Zhou Y K. 2019. Asymmetric behavior of vegetation seasonal growth and the climatic cause: evidence from long-term NDVI dataset in northeast China. Remote Sensing, 11(18): 2107
  184. 184.
    Zhou Y K. 2019. Depicting the asymmetries of vegetation phenology over Northeast China using remote sensing NDVI dataset. Remote Sensing Technology and Application, 34(2): 345-354
  185. 185.
    Zhou Y K and Liu J W. 2017. Vegetation phenological dataset of main ecological observation stations in China based on MODIS EVI. V1. [Science Data Bank]. (周玉科, 刘建文. 2017. 基于MODIS EVI的中国主要生态观测站植被物候数据集. V1.)
  186. 186.
    Zhu K Z and Wan M W. 1973. Phenology. Beijing: Science Publishing Press: 1-131
  187. 187.
    Zhu W Q, Mou M J, Wang L L and Jiang N. 2012. Evaluation of phenology extracting methods from vegetation index time series//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE: 1158-1161
  188. 188.
    Zhu X G, Zhang G L, Tholen D, Wang Y, Xin C P and Song Q F. 2011. The next generation models for crops and agro-ecosystems. Science China Information Sciences, 54(3): 589-597

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website