北京市平原区2015年—2019年0.8 m地表反射率数据集

地表反射率产品作为最重要的定量遥感产品，是很多参量化遥感产品的基础数据源，可以被广泛应用于林业、农业、水资源、生态环境、城市环境等典型应用领域。对于米级高分辨率的遥感影像，国内外不提供反射率影像产品。目前主流的国产高分数据源大部分都是蓝、绿、红、近红4个波段的多光谱数据，缺少短波红外波段，难以满足陆地区域浓密植被算法或者清洁水体的短波红外信号近似看作零的假设条件，要完成精确大气校正、生产大范围的GF-2影像拼接的地表反射率产品，是一个挑战。为了更好的推广高分地表反射率的应用，本研究针对北京市平原区，运用全色和多光谱融合、几何精校正和相对辐射一致化算法，以Sentinel-2为参考影像，生成了一套几何精校正后的0.8 m地表反射率影像集。该数据集时间范围为2015年—2019年，每年一期，包含年度产品的覆盖情况及分布矢量，共计184景地表反射率影像，数据量总共为1.63 TB。

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

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.