File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images

TitleAutomatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images
Authors
KeywordsPlanetScope
Pixel quality control
Cloud and cloud shadow detection
Land cover types
Tropical areas
Issue Date2021
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse
Citation
Remote Sensing of Environment, 2021, v. 264, p. article no. 112604 How to Cite?
AbstractPlanetScope 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.
Persistent Identifierhttp://hdl.handle.net/10722/304836
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, J-
dc.contributor.authorYang, D-
dc.contributor.authorChen, S-
dc.contributor.authorZhu, X-
dc.contributor.authorWu, S-
dc.contributor.authorBogonovich, M-
dc.contributor.authorGUO, Z-
dc.contributor.authorZhu, Z-
dc.contributor.authorWu, J-
dc.date.accessioned2021-10-05T02:35:54Z-
dc.date.available2021-10-05T02:35:54Z-
dc.date.issued2021-
dc.identifier.citationRemote Sensing of Environment, 2021, v. 264, p. article no. 112604-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/304836-
dc.description.abstractPlanetScope 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.-
dc.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectPlanetScope-
dc.subjectPixel quality control-
dc.subjectCloud and cloud shadow detection-
dc.subjectLand cover types-
dc.subjectTropical areas-
dc.titleAutomatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images-
dc.typeArticle-
dc.identifier.emailWang, J: lucyjing@hku.hk-
dc.identifier.emailWu, S: shengwu@hku.hk-
dc.identifier.emailWu, J: jinwu@hku.hk-
dc.identifier.authorityWu, J=rp02509-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2021.112604-
dc.identifier.scopuseid_2-s2.0-85110418280-
dc.identifier.hkuros325916-
dc.identifier.volume264-
dc.identifier.spagearticle no. 112604-
dc.identifier.epagearticle no. 112604-
dc.identifier.isiWOS:000688250300001-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats