A Global Analysis of the Spatial and Temporal Variability of Usable Landsat Observations at the Pixel Scale

• Published in: Frontiers in remote sensing 2022-06, Vol.3
• Main Authors: Zhang, Yingtong, Woodcock, Curtis E., Arévalo, Paulo, Olofsson, Pontus, Tang, Xiaojing, Stanimirova, Radost, Bullock, Eric, Tarrio, Katelyn R., Zhu, Zhe, Friedl, Mark A.
• Format: Article
• Language: English
• Subjects: data density; land change; land cover; landsat; pixel quality; time series
• ISSN: 2673-6187; 2673-6187
• DOI: 10.3389/frsen.2022.894618

The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data.