高分一号卫星高时空分辨率植被指数产品验证与分析
Verification and analysis of high spatial-temporal resolution vegetation index product based on GF-1 satellite data
- 2023年27卷第3期 页码：665-676
纸质出版日期： 2023-03-07
DOI： 10.11834/jrs.20231710
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摘要
归一化差值植被指数NDVI（Normalized Difference Vegetation Index）是使用率最高的植被指数，但现有的NDVI产品时间分辨率跟空间分辨率不足，限制了其在一定区域范围的精细化动态监测应用。高分一号（GF-1）宽幅卫星WFV（Wide Field View）具有4 d重访周期、16 m空间分辨率，在长时间序列动态监测中具有巨大潜力。本文对基于GF-1宽幅相机数据生产的全国2018年—2020年16 m 10 d的MuSyQ NDVI产品与基于Google Earth Engine（GEE）的Landsat 7、Landsat 8和Sentinel-2数据生产的Landsat NDVI、Sentinel-2 NDVI产品进行了一致性与差异性分析。结果显示MuSyQ NDVI产品空间分布更加连续，没有其他两个产品显示的条带特征；MuSyQ NDVI产品的有效数据比例更高，尤其是分布在北方及青藏高原的农作物和草地类型，具有更好的空间连续性。时间尺度上，由于GF-1/WFV相比于其他两种产品具有更高频次的观测，MuSyQ NDVI时间序列曲线更加平滑且连续，跳跃现象不明显，且能表现出更细节的植被生长特征及物候特征。在空间和时间尺度上，GF-1/NDVI产品提供的高时空分辨率的NDVI产品较已有产品更优，为后续植被动态研究中NDVI产品的选择提供了有用的信息，对于较大空间范围内的长时间序列精细化监测更具有优势。
Abstract
The rapid development of remote sensing technology has promoted the generation of different vegetation index products. Normalized Difference Vegetation Index (NDVI) is the vegetation index with the highest utilization rate. However
the existing NDVI products have insufficient time resolution and spatial resolution
thereby limiting the fine dynamic monitoring application in a certain region. The Wide-Field View (WFV) of GF-1 satellite data has a 4-day revisit period and 16 m spatial resolution
indicating its great potential in long-time series dynamic monitoring. The objective of this study is to establish a method for generating a 16 m/10-day NDVI product based on GF-1 images from 2018 to 2020.
In this study
the GF-1 NDVI products of 16 m and 10 days from 2018 to 2020 are produced based on GF-1 WFV. Moreover
Landsat NDVI and sentinel-2 NDVI products are produced based on landsat7
landsat8
and sentinel-2 data in the Google Earth engine database. The quantitative analysis and evaluation of time
spatial consistency
spatial continuity
and product comparison are performed from the space and time scale.
In January and August
the spatial distribution of MuSyQ NDVI
Landsat NDVI
and sentinel-2 NDVI products in China is reasonable and consistent. MuSyQ NDVI’s lack of space in January is lower than that of two other products. The frequency distribution histogram of MuSyQ NDVI
Landsat NDVI
and sentinel-2 NDVI differs. The difference among the three products is concentrated in the range of ±0.2
the peak value is at 0
and the frequency is close to 70%. These findings indicate that MuSyQ NDVI has good spatial consistency with the two other NDVI products. In Northeast China
Northwest China
and Qinghai Tibet Plateau
MuSyQ NDV has a lower loss rate and better spatial continuity than the two other products. Moreover
the spatial continuity of products is high. On the whole
the effective value ratio of the MuSyQ NDVI product is better than that of the two other products; in particular
the effective value ratios of the MuSyQ NDVI product in farmland and grassland areas are 28.6 (70%) and 30.27 (70%)
respectively
which are higher than those of the two other NDVI products. In the forest area
the effective value ratio of MuSyQ NDVI is also slightly better than that of the two other products. The three NDVI products have good consistency and phenological characteristics in the time series of farmland and grassland. In the deciduous broad-leaved forest area
the three products have similar seasonal variation laws. They fluctuate greatly in the time series curve in the evergreen broad-leaved forest and evergreen coniferous forest area. The consistency of MuSyQ NDVI
Landsat NDVI
and sentinel-2 NDVI in nonforest sites is higher than that in forest sites.
In terms of spatial and temporal scales
the high spatial-temporal resolution NDVI products provided by GF-1/NDVI products are better than the existing products. They also provide useful information for selecting NDVI products in subsequent vegetation dynamic research. Moreover
they have advantages for long-time series fine monitoring in an extensive spatial range.
关键词
高分一号（GF-1）植被指数高分辨率时空特征交叉验证
Keywords
GF-1vegetation indexhigh resolutionspatiotemporal characteristicscross validation
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中国科学院空天信息创新研究院
北京师范大学 遥感科学国家重点实验室
北京师范大学 全球变化与地球系统科学研究院
自然资源部第一海洋研究所
山东科技大学 计算机科学与工程学院
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