Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013–2016

Abstract

Currently, the historical archive images of Landsat family sensors are probably the most effective data products for tracking global longitudinal changes since the 1970s. However, the issue of the degree and extent of cloud coverage is always a challenge and varies distinctively worldwide. So far, acquisition probability (AP) analyses of cloud cover (CC) of Landsat observations have been conducted with different sensors at regional scale. To our knowledge, CC probability analysis for the newly-launched Landsat-8 Operational Land Imager (OLI) across China is not reported. In this paper, monthly, seasonal, and annual APs for Landsat OLI (44,228 in total) images over China acquired from April 2013 to October 2016 with various CC thresholds were analyzed. The results showed that: first, the cumulative average APs of all OLI data over China at the CC thresholds ≤30% was about 49.6% which illustrated the availability of OLI imagery across China. Second, the spatial patterns of 10%, 20%, and 30% CC thresholds of OLI observations, coincided well with the precipitation distributions separated by the respective 200 mm, 400 mm, and 800 mm isohyetal lines. Third, the APs of images with the 30% CC threshold are the highest in autumn and winter especially in October of 58.7%, while the corresponding lowest probability occurred in June of 41.0%. Finally, the spatial differences in APs of targeted images with ≤30% CC thresholds were quite significant. At regional scales, the arid and semi-arid areas, Inland River and Songliao River basins, and northwestern side of the Hu Huanyong population line had the larger probabilities of obtaining high-quality images. Our study suggested that OLI imagery satisfy the data requirements needed for land surface monitoring, although there existed obvious spatio-temporal differences in APs over China at the 30% CC threshold.

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

“…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).…”

The Spatial and Temporal Availability Differences of Cloud-Free Landsat Images Over Three Gorges Reservoir Area

“…The monthly acquisition probability curves are in the shape of a willow. At the CC threshold of 30%, the average monthly acquisition probability of the Landsat TM, ETM+, and OLI sensors is 0.259, 0.246, 0.225 respectively, less than the Landsat OLI acquisition probability of 49.6% in China at 30% CC threshold (Xiao et al 2018). For Landsat TM sensor, at 30% CC threshold, the acquisition probability is highest in August (0.340), followed closely by May, and lowest in January (0.160), followed by February and December.…”

“…To our knowledge, cloud cover analysis of all available Landsat data (TM, ETM+ and OLI in particular) over TGRA has not been reported to date. Although the availability analysis of Landsat 8 data in China has been carried out (Xiao et al 2018), but the study of country scale will weaken the statistical characteristics of region scale, and only the data of Landsat 8 were analyzed.…”

“…Laborde et al (2017) analyzed the Landsat 8 cloud-free observations in South East Asia. Xiao et al (2018) analyzed the spatio-temporal differences in cloud cover of Landsat-8 OLI across China. Sano et al (2007) studied the spatial and temporal probabilities of obtaining cloud-free data of Landsat TM, ETM+ over the Brazilian tropical savanna.…”

Are There Sufficient Landsat Observations for Retrospective and Continuous Monitoring of Land Cover Changes in China?

“…With increasing human activities in recent decades [1], anthropogenic land cover changes are more frequent and intensive [2], especially in nations with economies in transition. China is a case in point of a country undergoing rapid and dramatic changes in land cover since the adoption of the Reform and Opening-up policy in 1978 [3,4], which drove rapid development of the economy, industrialization, and urbanization [5]. Previous studies show that the extensive croplands of China have been either transformed into built-up areas due to urban expansion, or returned to forests or grasslands as a consequence of ecological restoration projects [5,6], causing a set of consequences on climate, biodiversity, and human well-being [7,8].…”