Dataset List

Vol.|Area

No.12 Vol.12,2025
No.11 Vol.12,2025
No.10 Vol.12,2025
No.09 Vol.12,2025
No.08 Vol.12,2025
No.07 Vol.12,2025
No.06 Vol.12,2025
No.05 Vol.12,2025
No.04 Vol.12,2025
No.03 Vol.12,2025
No.02 Vol.12,2025
No.01 Vol.12,2025
No.12 Vol.11,2024
No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Yearly Phenological Parameters during Vegetation Growing Season in Northern Hemisphere Based on Fengyun Satellites Images (2011-2019)


WANG Ning1,2Wu Ling2JIAO QuanJun*1HUANG WenJiang1,3ZHANG Bing1,3
1 State Key Laboratory of Remote Sensing and Digital Earth，Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100094，China2 School of Artificial Intelligence，China University of Geosciences，Beijing 100083，China3 University of Chinese Academy of Sciences，Beijing 100049，China

DOI:10.3974/geodb.2025.12.05.V1

Published:Dec. 2025

Visitors：31       Data Files Downloaded：0      
Data Downloaded： 无      Citations：

Key Words：

Northern Hemisphere,vegetation growing season,Fengyun satellite,dynamic threshold method

Abstract：

The vegetation growing season serves as a sensitive indicator of terrestrial ecosystem responses to climate change. Accurate monitoring of its key parameters is crucial for understanding vegetation-environment interactions, assessing carbon sink capacity, and evaluating the ecological impacts of global climate change. The yearly phenological parameters during vegetation growing season in northern hemisphere from Fengyun Satellites (2011-2019) was developed using a double-baseline dynamic threshold method, integrated with long-term NDVI time series data from Fengyun-3B satellite. The phenological parameters are the start date, end date, and length of the growing season. The dataset includes: (1) yearly vegetation growing season phenological parameters from 2011 to 2019; (2) multi-year average growing season phenological parameters. It has a spatial resolution of 0.05°. The dataset is archived in .tif data format, and consists of 30 data files with data size of 652 MB (compressed into one single file with 102 MB).

Foundation Item：

China Meteorological Administration (FY-APP); National Natural Science Foundation of China (42071330)

Data Citation：

WANG Ning, Wu Ling, JIAO QuanJun*, HUANG WenJiang, ZHANG Bing. Yearly Phenological Parameters during Vegetation Growing Season in Northern Hemisphere Based on Fengyun Satellites Images (2011-2019)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.12.05.V1.

References：


     [1]Wang, Y. P., Liu, Q. Y., Li, R., et al. Remote sensing of vegetation phenology in the northern hemisphere from multi-channel passive microwave measurements of Chinese FengYun-3D satellite [J]. Remote Sensing of Environment, 2025, 330: 114997.
     [2]Berra, E. F., Gaulton, R. Remote sensing of temperate and boreal forest phenology: a review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics [J]. Forest Ecology and Management, 2021, 480: 118663.
     [3]Xie, Z. Y., Zhu, W. Q. , He, B. K., et al. A background-free phenology index for improved monitoring of vegetation phenology [J]. Agricultural and Forest Meteorology, 2022, 315: 108826.
     [4]Hou, X. H., Niu, Z., Gao, S., et al. Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data [J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(1): 142-150.
     [5]Xu, W. F., Ma, H. Q. , Wu, D. H., et al. Assessment of the daily cloud-free MODIS snow-cover product for monitoring the snow-cover phenology over the Qinghai-Tibetan Plateau [J]. Remote Sensing, 2017, 9(6): 585.
     [6]Shao, Q., Huang, C., Xiao, Y. J., et al. Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency [J]. Ecological Informatics, 2025: 103216.
     [7]Zhang, M., Chen, L., Xu., N., et al. Influences of Earth incidence angle on FY-3/MWRI SST retrieval and evaluation of reprocessed SST [J]. Journal of Tropical Meteorology, 2024, 30(3): 230-240.
     [8]Zhang, P., Yu, H. B., Zhang, Q. F., et al. Applicability evaluation of FY-3B/3C and AMSR2 soil moisture products in Xilingol grassland [J]. Chinese Journal of Agrometeorology, 2023, 44(3): 238-251.
     [9]Zhou, Z., Zhu, L. L., Zhang, Y. H., et al. Downscaling machine learning snow depth inversion on the Qinghai-Xizang Plateau based on FY-3B passive microwave data [J]. Journal of Glaciology and Geocryology, 2024, 46(2): 539-554.
     [10]D’Odorico, P., Gonsamo, A., Gough, C. M., et al. The match and mismatch between photosynthesis and land surface phenology of deciduous forests [J]. Agricultural and Forest Meteorology, 2015, 214: 25-38.
     [11]Xian, D., Zhang, P., Gao, L., et al. Fengyun meteorological satellite products for earth system science applications [J]. Advances in Atmospheric Sciences, 2021, 38(8): 1267-1284.
     [12]Pearson, R. K., Neuvo, Y., Astola, J., et al. Generalized hampel filters [J]. EURASIP Journal on Advances in Signal Processing, 2016, 2016(1): 87.
     [13]Li, S., Xu, L., Jing, Y. H., et al. High-quality vegetation index product generation: a review of NDVI time series reconstruction techniques [J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 105: 102640.
     [14]White, M. A., Thornton, P. E., Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability [J]. Global Biogeochemical Cycles, 1997, 11(2): 217-234.
     [15]Garrity, S. R., Bohrer, G., Maurer, K. D., et al. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange [J]. Agricultural and Forest Meteorology, 2011, 151(12): 1741-1752.
     [16]Zhang, J., Zhao, J. J., Wang, Y. Q., et al. Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 169: 1-16.
     [17]Shao, Y. T., Wang, J. L. Vegetation Phenology Dataset in Mongolia [J]. Journal of Global Change Data & Discovery, 2022, 6(2): 241-248.
     [18]Zhou, L., Zhou, W., Chen, J. J., et al. Land surface phenology detections from multi-source remote sensing indices capturing canopy photosynthesis phenology across major land cover types in the Northern Hemisphere [J]. Ecological Indicators, 2022, 135: 108579.
     [19]Jiang, B. H., Chen, W., Chen, S. Y., et al. Comparison of the capability and performance of “photosynthesis” and “structure” indices in retrieving vegetation phenology in the Northern Hemisphere [J]. GIScience & Remote Sensing, 2025, 62(1): 2473127.

Data Product:

ID Data Name Data Size Operation
1 FY_GS_2011-2019.rar 104686.28KB
DownLoad
Co-Sponsors
Superintend