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GF-1 leaf area index product across China based on three-dimensional stochastic radiation transfer model

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

  • Email：zhanghu@aircas.ac.cn
  • Introduction：张虎，研究方向为植被定量遥感。E-mail： zhanghu@aircas.ac.cn
ZHANG Hu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, 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。E-mail： lijing200531@aircas.ac.cn
LI Jing12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

LIU Qinhuo12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

ZHANG Zhaoxing1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

ZHU Xinran12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

LIU Chang12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

ZHAO Jing12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

DONG Yadong12,  
  • Affiliation：

    Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

XU Baodong3,  
  • Affiliation：

    Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

MENG Jihua4

résumé

Leaf Area Index (LAI), is a critical variable in models of climate, meteorology, hydrology, and biogeochemistry to characterize vegetation canopy structure. Remote sensing provides a practical approach to estimating dynamic LAI on a large scale and some global LAI products were generated in the past decades. However, these products are mainly focused on the low-medium resolution satellite data and there is no standardized high-resolution LAI product worldwide. The object of this work is to propose an LAI inversion algorithm for high-resolution satellite GF-1 Wide Field View (GF-1 WFV) images and generate the GF-1 LAI product across China.Three-dimensional stochastic radiative transfer (3D-SRT) model, which can take the 3D-canopy architecture into consideration, is a widely-used model in LAI inversion. Parameters of Single Scattering Albedo (SSA) and uncertainty in the 3D-SRT model are highly correlated with the band setting and band stability. To acquire the optimal values of these parameters, 94824 homogenous samples of six vegetation types across China are selected and the characteristics of the difference in their surface reflectance are analyzed. SSA and uncertainty are adjusted to the values when the GF-1 retrieved LAI and MODIS LAI share the most similarity across the homogeneous samples. Based on the 3D-SRT model and the adjusted key parameters, an look up table (LUT) was constructed for the LAI retrieval in this work.There are 359 ground-measured LAI data in Shihezi, Xinjiang, and Sidaoqiao, Inner Mongolia in the validation. The overall result shows compared with the inversion result before adjusting the parameters, the root mean square error (RMSE) of the optimized algorithm can be reduced from 1.209 to 0.804, the determination coefficient (R2) can be increased from 0.659 to 0.883, and the retrieval index (RI) can be increased from 25.3% to 73.8 %, suggesting the higher accuracy and stability of the algorithm and more suitable for GF-1 LAI retrieval. The accuracy and stability of the algorithm also improved for each vegetation type individually. Based on the algorithm, the GF-1 leaf area index product of 16 m/10 days resolution across China from 2018 to 2020 was generated. The temporal profiles extracted from the product can indicate reasonable phenological characteristics of different vegetation types.Based on the algorithm proposed in this study, the high-resolution (16 m /10 days) LAI products for 2018-2020 across China were generated based on domestic satellite GF-1 Wide Field View. It can provide accurate and effective data which supports vegetation change research, agricultural and forestry application, ecological environment monitoring, and government decision-making. However, due to the short revisit time of medium and high-resolution satellites and the cloud contamination, the miss rate of current products is still high. In the future, more works can focus on how to generate spatial and temporal continuous products.

mots-clés

remote sensing;leaf area index;GF-1;three dimensional radiative transfer model

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