{"_buckets": {"deposit": "49ff271f-e92e-4baa-aa02-2ba9960decd4"}, "_deposit": {"id": "23073", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "23073"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00023073"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2015-06-17", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "6", "bibliographicPageStart": "e0127514", "bibliographicVolumeNumber": "10", "bibliographic_titles": [{"bibliographic_title": "PLoS ONE"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(\u03bb) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(\u03bb) retrieval model (SAKM) and Jamet\u2019s neural network model (JNNM), and then develop a new neural network Kd(\u03bb) retrieval model (NNKM). Based on the comparison of Kd(\u03bb) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(\u03bb) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(\u03bb) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces \u003c30% uncertainty in deriving Kd(\u03bb) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(\u03bb) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(\u03bb) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://dx.doi.org/10.1371/journal.pone.0127514"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/25263"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "PLOS"}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u00a9 2015 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1932-6203", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Chen, Jun"}], "nameIdentifiers": [{"nameIdentifier": "68337", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Zhu, Yuanli"}], "nameIdentifiers": [{"nameIdentifier": "68338", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Wu, Yongsheng"}], "nameIdentifiers": [{"nameIdentifier": "68339", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Cui, Tingwei"}], "nameIdentifiers": [{"nameIdentifier": "68340", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ishizaka, Joji"}], "nameIdentifiers": [{"nameIdentifier": "68341", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ju, Yongtao"}], "nameIdentifiers": [{"nameIdentifier": "68342", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-22"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "journal_pone_0127514.pdf", "filesize": [{"value": "12.7 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 12700000.0, "url": {"label": "journal_pone_0127514.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/23073/files/journal_pone_0127514.pdf"}, "version_id": "ea21ecd9-f105-4b96-8156-d206c6808be1"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "A Neural Network Model for K(\u03bb) Retrieval and Application to Global Kpar Monitoring", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "A Neural Network Model for K(\u03bb) Retrieval and Application to Global Kpar Monitoring"}]}, "item_type_id": "10", "owner": "1", "path": ["879/880/881"], "permalink_uri": "http://hdl.handle.net/2237/25263", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2016-12-19"}, "publish_date": "2016-12-19", "publish_status": "0", "recid": "23073", "relation": {}, "relation_version_is_last": true, "title": ["A Neural Network Model for K(\u03bb) Retrieval and Application to Global Kpar Monitoring"], "weko_shared_id": null}