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Received：08 November 2021，
Published：07 March 2023
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叶面积指数LAI（Leaf Area Index）是研究植被生态系统结构和功能的核心参数之一，遥感是获取大范围动态LAI的一个主要技术手段。目前国际上没有高分辨率的LAI标准化产品。本文基于三维随机辐射传输（3D-SRT）模型查找表算法研究了适用于国产高分辨率卫星高分一号宽幅相机（GF-1 WFV）的叶面积指数反演算法。模型中单次散射反照率和不确定性等参数与波段设置和波段稳定性相关。算法在全国范围内选取不同植被类型的均质样点，统计地表反射率的差异特征，调整全国6种植被类型各波段的单次散射反照率、不确定性等算法参数，进而构造适用于GF-1 WFV传感器的查找表以进行LAI的反演。研究中使用新疆维吾尔自治区石河子地区、内蒙古自治区四道桥包含农作物、森林等共359组实测地面数据开展LAI验证。验证结果表明，和调整参数前的反演结果相比，优化后的算法均方根误差RMSE可由算法优化前的1.209下降至0.804，决定系数
R
2
由0.659提高至0.883，反演成功率RI可由25.3%提高至73.8%，算法精度和稳定性较高，更适用于GF-1叶面积指数的反演。将其应用于GF-1卫星影像上，生产了2018年—2020年全国16 m空间分辨率10天合成的叶面积指数产品，产品能够反映出不同植被类型的物候特征，有利于大面积农业林业等遥感监测应用。
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 (
R
2
) 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.
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