предыдущий|следующий

Validation and analysis the fractional vegetation cover product from GF-1 satellite data in China

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhaojing1@radi.ac.cn
  • Introduction：赵静，研究方向为植被参数定量遥感反演。E-mail： zhaojing1@radi.ac.cn
ZHAO Jing1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：lijing200531@aircas.ac.cn
  • Introduction：李静，研究方向为植被辐射传输模型、叶面积指数反演等。E-mail： lijing200531@aircas.ac.cn
LI Jing12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

MU Xihan3,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Zhaoxing1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

DONG Yadong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WU Shanlong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHONG Bo1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Qinhuo12

реферат

Fractional Vegetation Cover (FVC) is a critical parameter for monitoring vegetation growth status. Remote sensing effectively generates FVC at a large scale. However, the spatial resolution of the existing FVC products at the global scale is more than 300 m. The major limitation of the FVC produced from high-spatial-resolution satellite images is the lack of effective observations, mainly due to a small range of view and long periods of revisit time for satellite images with 30 m or higher spatial resolutions. The Chinese GaoFen No. 1 satellite (GF-1) wide-field view data with 16 m spatial resolution and 4-day revisit time provide an available data resource for FVC extraction. The objective of this study is to assess the quality of the 16 m/10-day FVC product based on GF-1 images from 2018 to 2020.The assessment of the GF-1 FVC product was accomplished through direct validation with ground measurements and indirect validation with the GEOV3 FVC product. Two FVC products were postprocessed with the same temporal (month) and spatial (300 m) resolution to compare GF-1 FVC with 16 m/10-day and GEOV3 FVC with 300 m/10-day. A total of 32 ground measurements (including 18 ground measurements for crops and 14 ground measurements for forest) throughout the growing season at the middle reach of Heihe River Basin, the Jingyuetan station, and the Saihanba plantation forestry farm were used to validate the FVC product in China. The indirect validation was evaluated by the spatial and temporal continuity with missing values and the product consistency with GEOV3 FVC.The percentage of the annual missing value lower than 70% accounted for 88% of the main land in China. During the growing season, the percentage of the annual missing value lower than 73.68% approached 82.73%. According to different inversion algorithms and input products, the percentage of the average missing value of forest types (>20%) was higher than that of nonforest types, such as crops and grassland (<10.6%). The GF-1 FVC agreed well with GEOV3 FVC for the nonforest type based on the homogeneous samples in China from January to December 2019. The direct validation results indicated that the accuracy for the FVC product achieved by the GF-1 FVC product is reasonable compared with the ground measurements (R2 = 0.57, root mean square error = 0.12, BIAS = -0.03) in China. Moreover, it is better than the accuracy achieved by the GEOV3 FVC product, particularly for forest type.In conclusion, the GF-1 FVC product of China with 16 m/10-day resolution reflects the seasonal characteristics of vegetation well. Moreover, the GF-1 FVC product with high spatial and temporal resolutions meets the requirements of vegetation monitor at the regional scale.

ключеви́че слова́

Fractional Vegetation Cover (FVC);direct validation;indirect validation;China;GF-1

References

  1. 1.
    Bacour C, Baret F, Béal D, Weiss M and Pavageau K. 2006. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: principles and validation. Remote Sensing of Environment, 105(4): 313-325
  2. 2.
    Baret F, Morissette J T, Fernandes R A, Champeaux J L, Myneni R B, Chen J, Plummer S, Weiss M, Bacour C, Garrigues S and Nickeson J E. 2006. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1794-1803
  3. 3.
    Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L and Leroy M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: principles of the algorithm. Remote Sensing of Environment, 110(3): 275-286
  4. 4.
    Baret F, Weiss M, Lacaze R, Camacho F, Makhmara H, Pacholcyzk P and Smets B. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production. Remote Sensing of Environment, 137: 299-309
  5. 5.
    Beck H E, Zimmermann N E, McVicar T R, Vergopolan N, Berg A and Wood E F. 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5: 180214
  6. 6.
    Bian J H, Li A N, Zhang Z J, Zhao W, Lei G B, Yin G F, Jin H A, Tan J B and Huang C Q. 2017. Monitoring fractional green vegetation cover dynamics over a seasonally inundated alpine wetland using dense time series HJ-1A/B constellation images and an adaptive endmember selection LSMM model. Remote Sensing of Environment, 197: 98-114
  7. 7.
    Camacho F, Cernicharo J, Lacaze R, Baret F and Weiss M. 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 Sensing of Environment, 137: 310-329
  8. 8.
    Fuster B, Sánchez-Zapero J, Camacho F, García-Santos V, Verger A, Lacaze R, Weiss M, Baret F and Smets B. 2020. Quality assessment of PROBA-V LAI, fAPAR and fCOVER collection 300 m products of copernicus global land service. Remote Sensing, 12(6): 1017
  9. 9.
    Gitelson A A, Kaufman Y J, Stark R and Rundquist D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1): 76-87
  10. 10.
    Jia K, Liang S L, Wei X Q, Yao Y J, Yang L Q, Zhang X T and Liu D Y. 2018. Validation of Global LAnd Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region. Remote Sensing Letters, 9(9): 847-856
  11. 11.
    Jia K, Yang L Q, Liang S L, Xiao Z Q, Zhao X, Yao Y J, Zhang X T, Jiang B and Liu D Y. 2019. Long-term global land surface satellite (GLASS) fractional vegetation cover product derived from MODIS and AVHRR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 508-518
  12. 12.
    Jiapaer G, Chen X and Bao A M. 2011. A comparison of methods for estimating fractional vegetation cover in arid regions. Agricultural and Forest Meteorology, 151(12): 1698-1710
  13. 13.
    Li S Z, Li J, Yu W T, Zhang Z X, Wu S L, Zhong B and Liu Q H. 2022. A dataset of 16 m/10-day normalized difference vegetation index of MuSyQ GF-series (2018—2020, China, version 01). China Scientific Data, 7(1): 231-240
  14. 14.
    Liu Y K, Mu X H, Wang H X and Yan G J. 2012. A novel method for extracting green fractional vegetation cover from digital images. Journal of Vegetation Science, 23(3): 406-418
  15. 15.
    Ma P P, Li J, Liu Q H, He B B and Zhao J. 2019. Multisensor synergistic quantitative leaf area index product of China. Journal of Remote Sensing, 23(6): 1232-1252
  16. 16.
    Mu X H, Huang S, Ren H Z, Yan G J, Song W J and Ruan G Y. 2015. Validating GEOV1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 439-446
  17. 17.
    Mu X H, Liu Q H, Ruan G Y, Zhao J, Zhong B, Wu S L and Peng J J. 2017. A 1 km/5 day fractional vegetation cover dataset over China-ASEAN (2013). Journal of Global Change Data and Discovery, 1(1): 45-51
  18. 18.
    Mu X H, Zhao T, Ruan G Y, Song J L, Wang J D, Yan G J, Mcvicar T R, Yan K, Gao Z, Liu Y K and Wang Y Y. 2021. 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. Journal of Meteorological Research, 35(1): 128-147
  19. 19.
    Wang T, Zhou D W, Shen X J, Fan G H and Zhang H. 2020. Koppen’s climate classification map for China. Journal of the Meteorological Sciences, 40(6): 752-760
  20. 20.
    Weiss M, Baret F, Garrigues S and Lacaze R. 2007. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sensing of Environment, 110(3): 317-331
  21. 21.
    Yang L Q, Jia K, Liang S L, Wei X Q, Yao Y J and Zhang X T. 2017. A robust algorithm for estimating surface fractional vegetation cover from landsat data. Remote Sensing, 9(8): 857
  22. 22.
    Zhang H, Li J, Zhang Z X, Wu S L, Zhong B and Liu Q H. 2022. A dataset of 16m/10-day leaf area indices of MuSyQ GF-series (2018—2020, China, Version 01). China Scientific Data, 7(1): 211-220
  23. 23.
    Zhao J, Li J, Liu Q H, Xu B D, Yu W T, Lin S R and Hu Z. 2020. Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory. International Journal of Applied Earth Observation and Geoinformation, 90: 102112
  24. 24.
    Zhao J, Li J, Zhang Z X, Wu S L, Zhong B and Liu Q H. 2022. A dataset of 16 m/10-day fractional vegetation cover of MuSyQ GF-series (2018—2020, China, Version 01). China Scientific Data, 7(1): 221-230

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