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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

résumé

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.

mots-clés

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

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