Abstract
Repeat frequencies of optical remote sensing satellites have been increasing over the last 40 years, but there is still dependence on clear skies to acquire usable imagery. To increase the quality of data, composited mosaics of satellite imagery can be used. In this paper, we develop an automated method for clearing clouds and producing different types of composited mosaics suitable for use in cloud-affected countries, such as New Zealand. We improve the Tmask algorithm for cloud detection by using a parallax method to produce an initial cloud layer and by using an object-based cloud and shadow approach to remove false cloud detections. We develop several parametric scoring approaches for choosing best-pixel composites with minimal remaining cloud. The automated mosaicking approach produced Sentinel-2 mosaics of New Zealand for five successive summers, 2015/16 through 2019/20, with remaining cloud being less than 0.1%. Contributing satellite overpasses were typically of the order of 100. In comparison, manual methods for cloud clearing produced mosaics with 5% remaining cloud and from satellite overpasses typically of the order of 20. The improvements to cloud clearing enable the use of all possible Sentinel-2 imagery to produce automatic mosaics capable of regular land monitoring, at a reasonable cost.
Highlights
Optical remote-sensing satellites have been observing the Earth for over 40 years [1], providing a consistent, long-term, large-area data record [2]
The automated cloud clearing enables us to use all available imagery at a reasonable cost, which is important in a country like New Zealand that is often cloudy due to its mountainous terrain and maritime location
Panel (b) shows the result of the parallax method for cloud detection, with the clouds shown in magenta and the shadows in yellow
Summary
Optical remote-sensing satellites have been observing the Earth for over 40 years [1], providing a consistent, long-term, large-area data record [2]. These data have been used for many applications, including monitoring crop production [3], land cover and land-use change [4], deforestation [5], ecosystem health [6], and water quality [7]. An alternative to the best-pixel approach is the synthesis of new values, such as weighted averaging [14], mean value compositing [15], time-series fitted harmonic models [16], or time-series trajectory templates [17] These approaches can produce mosaics that are homogeneous in appearance, but they do not represent physical observations [3]
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