Landsat 4-5 TM: 1987-1997

The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.全球陆地调查（Global Land Survey, GLS）数据集是一套经过正射校正、云量最小化的陆地卫星型（Landsat-type）卫星影像集合，自20世纪70年代初以来，以十年为周期实现了全球陆地几乎全覆盖。该全球镶嵌影像分别以1975年、1990年、2000年、2005年和2010年为基准中心，所用数据源自四种传感器：增强型专题制图仪（Enhanced Thematic Mapper Plus, ETM+）、专题制图仪（Thematic Mapper, TM）、多光谱扫描仪（Multispectral Scanner, MSS）以及先进陆地成像仪（Advanced Land Imager, ALI）。 GLS数据集已被广泛应用于局地、区域及全球尺度下的土地覆盖与土地利用变化研究。本研究从空间覆盖范围、时间一致性、大地测量精度、辐射定标一致性、图像完整性、云污染程度以及残留间隙等多个维度对GLS数据集开展评估。总体而言，三款最新的GLS数据集质量优于GLS-1990与GLS-1975数据集，其中85%的影像云量低于10%，成像年份更紧密地聚集于基准年份周围，相较于GLS-2000数据集具备更优的共配准效果，且辐射绝对定标性能更佳。 或许该数据集在科学应用中面临的最显著阻碍是图像物候期的差异（即成像年积日的差异）。本文为终端用户提供了GLS数据集针对特定应用场景的质量评估，并在可行范围内给出了弥补其缺陷的建议。