Spatiotemporal distribution of Landsat imagery of Europe using cloud cover-weighted metadata

Abstract

Landsat imagery is the most frequently used remotely sensed data in many fields related to the monitoring of the Earth's surface. As Landsat satellites have gathered data since 1972, lots of valuable information has been stored and can be derived from imagery over a long time interval. Of course, due to certain factors such as weather conditions and satellite-related technical issues, data collection cannot be consistent in time and space. Cloud coverage is the most obvious condition that determines the usability of a remotely sensed satellite images. For successful results, a rich data supply is essential. To explore the data supply of a certain study area, the Landsat metadata can be checked which is usually an involved process especially for a long time interval. Therefore, the visualisation of Landsat metadata can result in a faster work flow and successful study area selection. In this paper we present a cloud cover-weighted metadata map for the area of Europe.

An epilimnion and hypolimnion temperature model based on air temperature and lake characteristics

“…Since Landsat 5 and Landsat 7 have identical orbits with a delay of 8 days, satellite images for a given water body could be obtained every 8 days when both satellites were active (Landsat 5 was decommissioned in 2013). This optimal periodicity of measurement was reduced because of the presence of clouds (Tolnai et al, 2016), water vapour content beyond the limit of applicability of the algorithm used to estimate surface temperatures (Prats et al, 2018a) or because of technical and cost limitations in the number of images that could be acquired (Goward et al, 2006;Wulder et al, 2016). The number of satellite images per water body varied between 6 and 205, although the median number was 59 (less than 4 images per year in average).…”

“…However, satellite measurements have some limitations. The periodicity of satellite images is irregular because of clouds or because of operational limits of the number of images taken per day (Goward et al, 2006;Tolnai et al, 2016;Xiao et al, 2018). In addition, satellites retrieve skin temperatures rather than epilimnion or bulk surface temperatures (Schluessel et al, 1990;Donlon et al, 2002).…”

Responses of Lake Ice Phenology to Climate Change at Tibetan Plateau

“…Different remote sensing products have shown their relative merits in determining the lake ice phenology [12]. The limitations of most satellite products for lake ice phenology research largely exist in their temporal resolution and time span [13] [14] [15].…”

Global Revisit Interval Analysis of Landsat-8 -9 and Sentinel-2A -2B Data for Terrestrial Monitoring

“…Different imaging priorities are set according to latitude and location, i.e., Landsat-8 scenes in USA are set as a high priority, and no scenes are rejected for growing season monitoring of the Northern Hemisphere [ 8 ]. Cloud obscuration, when clouds preclude observations, is significant and complicated, as the global variability of clouds in space and time is considerable during Landsat overpasses [ 9 , 10 ].…”