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Annual 0.8 m surface reflectance data set of Beijing plain area from 2015 to 2019

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：shenqian@aircas.ac.cn
  • Introduction：申茜，1981年生，女，副研究员，研究方向为水环境遥感、高光谱遥感。E-mail： shenqian@aircas.ac.cn
SHEN Qian1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YAO Yue1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Liwei1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    China Remote Sensing Satellite Ground Station, Beijing 100094, China

LONG Tengfei12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    China Remote Sensing Satellite Ground Station, Beijing 100094, China

CHEN Fu12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049 ,China

ZHANG Bing13

résumé

As the most important quantitative remote sensing product, surface reflectance products are the basic data source for many parametric remote sensing products and can be widely used in typical applications, such as forestry, agriculture, water resources, ecological environment, and urban environment. However, for meter-level high-resolution remote sensing images, reflectance products are unavailable at home and abroad. Most of the current domestic satellite products use high-resolution multispectral data in the four bands of blue, green, red, and near infrared as the main data source, and accurate atmospheric correction is difficult to achieve mainly due to the lack of short-wave infrared data. A set of spatiotemporal continuous meter-level high-resolution image reflectance products should also be provided for researchers to use. In this study, GF-2 Level-1A standardized products were used as input. The processing steps included panchromatic and multispectral fusion, geometric precision correction and mosaic, and atmospheric correction. A set of annual surface reflectance images with a spatial resolution of 0.8 m covering Beijing plain area from 2015 to 2019 was generated.First, a geometric deviation of subpixel level exists among the four multispectral bands of GF-2 Level-1A products. The Pixel Knife software was used to register each band of GF-2 images accurately, then the images were fused with panchromatic images. Second, with Sentinel-2 as reference images (10 m resolution) and supplementary SRTM DEM data (30 m resolution), we used a regional network adjustment method to achieve geometric precision correction. The geometric accuracy is within 20 m. This method ensures the absolute geometric positioning accuracy and the relative geometric accuracy among images. Third, we used a relative radiation uniformity method to complete atmospheric correction. Regarding Sentinel-2 reflectance images as reference images (10 m resolution), we automatically searched the pseudo invariant points between the reference images and the images to be corrected and built a regression equation band by band.A total of 184 scenes of surface reflectance images with a total data volume of 1.63 TB are acquired. The data set is issued annually, including the coverage and distribution vector of the annual products. In the current data set, for mountain-shaded and relatively clean water bodies, such as Miyun Reservoir water bodies, the retrieval reflectance is often zero or negative. Therefore, this data set is suitable for underlying surface applications, except mountainous areas and relatively clean water bodies. It can prevent the shading phenomenon after the fusion of panchromatic and multispectral images and ensure that the multiscene geometric precision correction images have good geometric consistency at the joints. The validation results show that the atmospheric correction effect of water, road, and vegetation canopy is good.On the premise that the reference image is ready, the processing method proposed in this article can process high-resolution images and output surface reflectance products rapidly, massively, and automatically. Currently, 70% success rate is achieved for atmospheric correction, which should be further improved.

mots-clés

0.8 m surface reflectance data;GF-2;high resolution image;atmospheric correction;plain area of Beijing

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