Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive
Pixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting data to inc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/10/10/1570 |
| _version_ | 1819002022818480128 |
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| author | Stefan Ernst Leo Lymburner Josh Sixsmith |
| author_facet | Stefan Ernst Leo Lymburner Josh Sixsmith |
| author_sort | Stefan Ernst |
| collection | DOAJ |
| description | Pixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting data to include in automated analysis, as imperfect or erroneous observations are undesirable for most applications. There is, however, a certain rate of commission error in cloud detection, and saturation may not affect all spectral bands at a time, which can lead to suitable observations being excluded. This can have a substantial impact on the amount of data available for analysis. To understand how different surface types can affect cloud commission and saturation, we analyzed cloud and per-band saturation PQ flags for 31 years of Landsat data within Digital Earth Australia. Areas showing substantial reduction in observation density compared to their surroundings were investigated to characterize how specific surface types impact on the temporal density of observations deemed desirable. Using Fmask 3.2 by way of example, our approach demonstrates a method that can be applied to summarize the characteristics of cloud-screening algorithms and sensor saturation. Results indicate that cloud commission and sensor saturation rates show specific characteristics depending on the targets under observation. This potentially leads to an imbalance in data availability driven by surface type in a given study area. Based on our findings, the level of detail in PQ flags delivered with ARD is pivotal in maximizing the potential of EO data. |
| first_indexed | 2024-12-20T22:58:30Z |
| format | Article |
| id | doaj.art-238d1120459b413bad8dd09e6c51d2a2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T22:58:30Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-238d1120459b413bad8dd09e6c51d2a22022-12-21T19:24:03ZengMDPI AGRemote Sensing2072-42922018-10-011010157010.3390/rs10101570rs10101570Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat ArchiveStefan Ernst0Leo Lymburner1Josh Sixsmith2Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, GermanyGeoscience Australia, GPO Box 378, Canberra ACT 2601, AustraliaGeoscience Australia, GPO Box 378, Canberra ACT 2601, AustraliaPixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting data to include in automated analysis, as imperfect or erroneous observations are undesirable for most applications. There is, however, a certain rate of commission error in cloud detection, and saturation may not affect all spectral bands at a time, which can lead to suitable observations being excluded. This can have a substantial impact on the amount of data available for analysis. To understand how different surface types can affect cloud commission and saturation, we analyzed cloud and per-band saturation PQ flags for 31 years of Landsat data within Digital Earth Australia. Areas showing substantial reduction in observation density compared to their surroundings were investigated to characterize how specific surface types impact on the temporal density of observations deemed desirable. Using Fmask 3.2 by way of example, our approach demonstrates a method that can be applied to summarize the characteristics of cloud-screening algorithms and sensor saturation. Results indicate that cloud commission and sensor saturation rates show specific characteristics depending on the targets under observation. This potentially leads to an imbalance in data availability driven by surface type in a given study area. Based on our findings, the level of detail in PQ flags delivered with ARD is pivotal in maximizing the potential of EO data.http://www.mdpi.com/2072-4292/10/10/1570pixel qualityAnalysis Ready Datacloud maskingsaturationFmaskLandsat |
| spellingShingle | Stefan Ernst Leo Lymburner Josh Sixsmith Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive Remote Sensing pixel quality Analysis Ready Data cloud masking saturation Fmask Landsat |
| title | Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive |
| title_full | Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive |
| title_fullStr | Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive |
| title_full_unstemmed | Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive |
| title_short | Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive |
| title_sort | implications of pixel quality flags on the observation density of a continental landsat archive |
| topic | pixel quality Analysis Ready Data cloud masking saturation Fmask Landsat |
| url | http://www.mdpi.com/2072-4292/10/10/1570 |
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