Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93502
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Wang, J | en_US |
dc.creator | Yang, D | en_US |
dc.creator | Chen, S | en_US |
dc.creator | Zhu, X | en_US |
dc.creator | Wu, S | en_US |
dc.creator | Bogonovich, M | en_US |
dc.creator | Guo, Z | en_US |
dc.creator | Zhu, Z | en_US |
dc.creator | Wu, J | en_US |
dc.date.accessioned | 2022-07-08T01:02:49Z | - |
dc.date.available | 2022-07-08T01:02:49Z | - |
dc.identifier.issn | 0034-4257 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93502 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2021 Elsevier Inc. All rights reserved. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Wang, J., Yang, D., Chen, S., Zhu, X., Wu, S., Bogonovich, M., . . . Wu, J. (2021). Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images. Remote Sensing of Environment, 264, 112604 is available at https://dx.doi.org/10.1016/j.rse.2021.112604. | en_US |
dc.subject | Cloud and cloud shadow detection | en_US |
dc.subject | Ecological and environmental monitoring | en_US |
dc.subject | Land cover types | en_US |
dc.subject | Pixel quality control | en_US |
dc.subject | PlanetScope | en_US |
dc.subject | Tropical areas | en_US |
dc.title | Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 264 | en_US |
dc.identifier.doi | 10.1016/j.rse.2021.112604 | en_US |
dcterms.abstract | PlanetScope satellite data with a 3-m resolution and near-daily global coverage have been increasingly used for land surface monitoring, ranging from land cover change detection to vegetative biophysics characterization and ecological assessments. Similar to other satellite data, effective screening of clouds and cloud shadows in PlanetScope images is a prerequisite for these applications, yet remains challenging as PlanetScope has 1) fewer spectral bands than other satellites hindering the use of traditional methods, and 2) inconsistent radiometric calibration across satellite sensors making the cloud/shadow detection using fixed thresholds unrealistic. To address these challenges, we developed a SpatioTemporal Integration approach for Automatic Cloud and Shadow Screening (‘STI-ACSS’), including two steps: (1) generating initial masks of clouds/shadows by integrating both spatial (i.e. cloud/shadow indices of an individual PlanetScope image) and temporal (i.e. reflectance outliers in PlanetScope image time series) information with an adaptive threshold approach; (2) a two-step fine-tuning on these initial masks to derive final masks by integrating morphological processing with an object-based cloud and cloud shadow matching. We tested STI-ACSS at six tropical sites representative of different land cover types (e.g. forest, urban, cropland, savannah, and shrubland). For each site, we evaluated the performance of STI-ACSS with reference to the manual masks of clouds/shadows, and compared it with four state-of-the-art methods, namely Function of mask (Fmask), Automatic Time-Series Analysis (ATSA), Iterative Haze Optimized Transformation (IHOT) and the default PlanetScope quality control layer. Our results show that, across all sites, STI-ACSS 1) has the highest average overall accuracy (98.03%), 2) generates an average producer accuracy of 95.53% for clouds and 89.48% for cloud shadows, and 3) is robust across sites and seasons. These results suggest the effectiveness of using STI-ACSS for cloud/shadow detection for PlanetScope satellites in the tropics, with potential to be extended to other satellite sensors with limited spectral bands. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing of environment, Oct. 2021, v. 264, 112604 | en_US |
dcterms.isPartOf | Remote sensing of environment | en_US |
dcterms.issued | 2021-10 | - |
dc.identifier.scopus | 2-s2.0-85110418280 | - |
dc.identifier.eissn | 1879-0704 | en_US |
dc.identifier.artn | 112604 | en_US |
dc.description.validate | 202207 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | LSGI-0005, a1565 | - |
dc.identifier.SubFormID | 45446 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; the Division of Ecology and Biodiversity PDF research award; the United States Department of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory (From submitter Zhu, Xiaolin [LSGI], submission no.45446, funding source is RGC. Checked with publisher pdf, no RGC) | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 56135333 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Wang_Automatic_Cloud_Shadow.pdf | Pre-Published version | 7.43 MB | Adobe PDF | View/Open |
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