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Received：05 November 2021，
Published：07 March 2023
移动端阅览
植被覆盖度FVC（Fractional Vegetation Cover）是衡量地表植被状况的重要指标之一。卫星遥感是获取全球与区域动态FVC的主要技术手段，但现有FVC产品空间分辨率主要为中等（300 m）及中低分辨率，难以满足应用需求。已有高分辨率卫星较长的重访周期是制约高分辨率FVC产品难以生产的主要原因。国产高分一号（GF-1）宽幅相机（WFV）具有高时空分辨率特点，16 m空间分辨率和4天重访周期为高时空分辨率FVC产品生产提供数据支撑。本文对中国2018年—2020年16 m/10天GF-1 FVC产品从直接验证和间接验证两方面进行定量分析与评价，结果显示：（1）基于中国甘肃黑河站、吉林净月潭站、河北塞罕坝站等地面实测数据进行直接验证，GF-1 FVC产品精度（
R
2
=0.57，RMSE=0.12，BIAS=-0.03）优于GEOV3 FVC产品，能够降低森林类型高估现象；（2）中国陆地近88%的像元GF-1 FVC产品全年缺失率低于70%，在生长季内约占82.73%的像元缺失率低于73.68%，产品能够较好的体现植被季节变化特征。基于国产GF-1卫星的高时空分辨率FVC产品能够满足植被变化监测相关应用的需求。
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 (
R
2
= 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.
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