Smoke detections and visibility estimation using Himawari_8 satellite data over Sumatera and Borneo Island Indonesia View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Heri Ismanto, Hartono Hartono, Muh Aris Marfai

ABSTRACT

Smoke as the one of weather hazard that contains large pollutant and affect the major live aspects: health, tourism, transportation and climate. Due to its regular appearance in Maritime Continent Indonesian area South East Asia, it is important to assess the satellite remote sensing Himawari_8 data to detect smoke and model the horizontal visibility as the smoke proxy. Using RGB (red, green, blue) combination, maximum likelihood and backward selection of multiple regression were used to detect and to develop the horizontal visibility model. RGB aerosol and RGB day natural color visually sees only the thick smoke with horizontal visibility observe below 1600 m. The best horizontal visibility model [with significant level 95% (probability < 0.05)] was develop from combination of band 3 (0.64 µm); band 7 (3.9 µm); and band 14 (11.2 µm) with root means square error value is about 404 m and correlation value is about 0.69. More... »

PAGES

205-216

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41324-018-0225-8

DOI

http://dx.doi.org/10.1007/s41324-018-0225-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110096688


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Gadjah Mada University", 
          "id": "https://www.grid.ac/institutes/grid.8570.a", 
          "name": [
            "Center for Aeronautical Meteorology, Agency of Meteorological, Climatological and Geophysical (BMKG), Kemayoran, 10720, Jakarta, Indonesia", 
            "Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, 55281, Yogyakarta, Indonesia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ismanto", 
        "givenName": "Heri", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Gadjah Mada University", 
          "id": "https://www.grid.ac/institutes/grid.8570.a", 
          "name": [
            "Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, 55281, Yogyakarta, Indonesia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hartono", 
        "givenName": "Hartono", 
        "id": "sg:person.012130420411.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012130420411.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Gadjah Mada University", 
          "id": "https://www.grid.ac/institutes/grid.8570.a", 
          "name": [
            "Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, 55281, Yogyakarta, Indonesia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marfai", 
        "givenName": "Muh Aris", 
        "id": "sg:person.016016031503.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016016031503.99"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1029/2010jd015148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002524028"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2003gl018323", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003407447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/acp-8-6739-2008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003648352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosenv.2008.09.067", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008638924"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s101130100021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011093371", 
          "https://doi.org/10.1007/s101130100021"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosenv.2013.08.050", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011847380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/acpd-15-22215-2015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013670307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2002jd002378", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016983263"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(1998)079<2457:sbiipf>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019455953"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/rs70404473", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020694248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/bams-88-10-1589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024723384"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosenv.2008.10.058", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030025273"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosenv.2013.01.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033126518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b0-12-227090-8/00049-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033225932"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431160110078467", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037139734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2008bams2354.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038737195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2151/jmsj.2016-009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039072090"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosres.2012.05.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039189152"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431160802226059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040846723"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/isprsarchives-xl-7-w3-219-2015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040970654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep06112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043321294", 
          "https://doi.org/10.1038/srep06112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1071/wf05012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043819598"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2002jd003324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044982622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-69397-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046456632", 
          "https://doi.org/10.1007/978-3-540-69397-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-69397-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046456632", 
          "https://doi.org/10.1007/978-3-540-69397-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2006.11.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047592751"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/rs8010011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050755337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11027-006-9045-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053167687", 
          "https://doi.org/10.1007/s11027-006-9045-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es405533d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055507419"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/36.951076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061162705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2010.2047863", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061611456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.22146/mgi.15624", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069330854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/rsete.2011.5964042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095140078"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04", 
    "datePublishedReg": "2019-04-01", 
    "description": "Smoke as the one of weather hazard that contains large pollutant and affect the major live aspects: health, tourism, transportation and climate. Due to its regular appearance in Maritime Continent Indonesian area South East Asia, it is important to assess the satellite remote sensing Himawari_8 data to detect smoke and model the horizontal visibility as the smoke proxy. Using RGB (red, green, blue) combination, maximum likelihood and backward selection of multiple regression were used to detect and to develop the horizontal visibility model. RGB aerosol and RGB day natural color visually sees only the thick smoke with horizontal visibility observe below 1600 m. The best horizontal visibility model [with significant level 95% (probability < 0.05)] was develop from combination of band 3 (0.64 \u00b5m); band 7 (3.9 \u00b5m); and band 14 (11.2 \u00b5m) with root means square error value is about 404 m and correlation value is about 0.69.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s41324-018-0225-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1153043", 
        "issn": [
          "2366-3286", 
          "2366-3294"
        ], 
        "name": "Spatial Information Research", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "name": "Smoke detections and visibility estimation using Himawari_8 satellite data over Sumatera and Borneo Island Indonesia", 
    "pagination": "205-216", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "16d21003fcf872fe8c20b33ad1a9f743c6f9d6e5591b5efd42cbb23b2a57b323"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s41324-018-0225-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110096688"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s41324-018-0225-8", 
      "https://app.dimensions.ai/details/publication/pub.1110096688"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:34", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000370_0000000370/records_46769_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs41324-018-0225-8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s41324-018-0225-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s41324-018-0225-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s41324-018-0225-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s41324-018-0225-8'


 

This table displays all metadata directly associated to this object as RDF triples.

175 TRIPLES      21 PREDICATES      59 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s41324-018-0225-8 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N29e80e98f02c436298aee0002bde2e7c
4 schema:citation sg:pub.10.1007/978-3-540-69397-0
5 sg:pub.10.1007/s101130100021
6 sg:pub.10.1007/s11027-006-9045-6
7 sg:pub.10.1038/srep06112
8 https://doi.org/10.1016/b0-12-227090-8/00049-x
9 https://doi.org/10.1016/j.atmosenv.2008.09.067
10 https://doi.org/10.1016/j.atmosenv.2008.10.058
11 https://doi.org/10.1016/j.atmosenv.2013.01.052
12 https://doi.org/10.1016/j.atmosenv.2013.08.050
13 https://doi.org/10.1016/j.atmosres.2012.05.009
14 https://doi.org/10.1016/j.rse.2006.11.023
15 https://doi.org/10.1021/es405533d
16 https://doi.org/10.1029/2002jd002378
17 https://doi.org/10.1029/2002jd003324
18 https://doi.org/10.1029/2003gl018323
19 https://doi.org/10.1029/2010jd015148
20 https://doi.org/10.1071/wf05012
21 https://doi.org/10.1080/01431160110078467
22 https://doi.org/10.1080/01431160802226059
23 https://doi.org/10.1109/36.951076
24 https://doi.org/10.1109/rsete.2011.5964042
25 https://doi.org/10.1109/tgrs.2010.2047863
26 https://doi.org/10.1175/1520-0477(1998)079<2457:sbiipf>2.0.co;2
27 https://doi.org/10.1175/2008bams2354.1
28 https://doi.org/10.1175/bams-88-10-1589
29 https://doi.org/10.2151/jmsj.2016-009
30 https://doi.org/10.22146/mgi.15624
31 https://doi.org/10.3390/rs70404473
32 https://doi.org/10.3390/rs8010011
33 https://doi.org/10.5194/acp-8-6739-2008
34 https://doi.org/10.5194/acpd-15-22215-2015
35 https://doi.org/10.5194/isprsarchives-xl-7-w3-219-2015
36 schema:datePublished 2019-04
37 schema:datePublishedReg 2019-04-01
38 schema:description Smoke as the one of weather hazard that contains large pollutant and affect the major live aspects: health, tourism, transportation and climate. Due to its regular appearance in Maritime Continent Indonesian area South East Asia, it is important to assess the satellite remote sensing Himawari_8 data to detect smoke and model the horizontal visibility as the smoke proxy. Using RGB (red, green, blue) combination, maximum likelihood and backward selection of multiple regression were used to detect and to develop the horizontal visibility model. RGB aerosol and RGB day natural color visually sees only the thick smoke with horizontal visibility observe below 1600 m. The best horizontal visibility model [with significant level 95% (probability < 0.05)] was develop from combination of band 3 (0.64 µm); band 7 (3.9 µm); and band 14 (11.2 µm) with root means square error value is about 404 m and correlation value is about 0.69.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree false
42 schema:isPartOf N4230ab02ffe748bf9cf46b320322dfcb
43 Nc2c6abea841f412ca7875fc88d9972d1
44 sg:journal.1153043
45 schema:name Smoke detections and visibility estimation using Himawari_8 satellite data over Sumatera and Borneo Island Indonesia
46 schema:pagination 205-216
47 schema:productId N03fd7515d6a146098491dfa6920dced0
48 N258d97d5b38f414c839cfd141ee1d8a6
49 N79111c6c3e7b43dab61c381a2324388b
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110096688
51 https://doi.org/10.1007/s41324-018-0225-8
52 schema:sdDatePublished 2019-04-11T13:34
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher N7bf8daf8542c46afa3ee353dc498a8cb
55 schema:url https://link.springer.com/10.1007%2Fs41324-018-0225-8
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N03fd7515d6a146098491dfa6920dced0 schema:name dimensions_id
60 schema:value pub.1110096688
61 rdf:type schema:PropertyValue
62 N258d97d5b38f414c839cfd141ee1d8a6 schema:name doi
63 schema:value 10.1007/s41324-018-0225-8
64 rdf:type schema:PropertyValue
65 N29e80e98f02c436298aee0002bde2e7c rdf:first Ne74d5649199447b5a94c9a0925e46e04
66 rdf:rest N63cf135e618445d5af6a608879eab081
67 N4230ab02ffe748bf9cf46b320322dfcb schema:issueNumber 2
68 rdf:type schema:PublicationIssue
69 N63cf135e618445d5af6a608879eab081 rdf:first sg:person.012130420411.76
70 rdf:rest N92b14955730a4f2f8932c752294242ca
71 N79111c6c3e7b43dab61c381a2324388b schema:name readcube_id
72 schema:value 16d21003fcf872fe8c20b33ad1a9f743c6f9d6e5591b5efd42cbb23b2a57b323
73 rdf:type schema:PropertyValue
74 N7bf8daf8542c46afa3ee353dc498a8cb schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 N92b14955730a4f2f8932c752294242ca rdf:first sg:person.016016031503.99
77 rdf:rest rdf:nil
78 Nc2c6abea841f412ca7875fc88d9972d1 schema:volumeNumber 27
79 rdf:type schema:PublicationVolume
80 Ne74d5649199447b5a94c9a0925e46e04 schema:affiliation https://www.grid.ac/institutes/grid.8570.a
81 schema:familyName Ismanto
82 schema:givenName Heri
83 rdf:type schema:Person
84 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
85 schema:name Information and Computing Sciences
86 rdf:type schema:DefinedTerm
87 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
88 schema:name Artificial Intelligence and Image Processing
89 rdf:type schema:DefinedTerm
90 sg:journal.1153043 schema:issn 2366-3286
91 2366-3294
92 schema:name Spatial Information Research
93 rdf:type schema:Periodical
94 sg:person.012130420411.76 schema:affiliation https://www.grid.ac/institutes/grid.8570.a
95 schema:familyName Hartono
96 schema:givenName Hartono
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012130420411.76
98 rdf:type schema:Person
99 sg:person.016016031503.99 schema:affiliation https://www.grid.ac/institutes/grid.8570.a
100 schema:familyName Marfai
101 schema:givenName Muh Aris
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016016031503.99
103 rdf:type schema:Person
104 sg:pub.10.1007/978-3-540-69397-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046456632
105 https://doi.org/10.1007/978-3-540-69397-0
106 rdf:type schema:CreativeWork
107 sg:pub.10.1007/s101130100021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011093371
108 https://doi.org/10.1007/s101130100021
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/s11027-006-9045-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053167687
111 https://doi.org/10.1007/s11027-006-9045-6
112 rdf:type schema:CreativeWork
113 sg:pub.10.1038/srep06112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043321294
114 https://doi.org/10.1038/srep06112
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/b0-12-227090-8/00049-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1033225932
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.atmosenv.2008.09.067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008638924
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.atmosenv.2008.10.058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030025273
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.atmosenv.2013.01.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033126518
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.atmosenv.2013.08.050 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011847380
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.atmosres.2012.05.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039189152
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.rse.2006.11.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047592751
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1021/es405533d schema:sameAs https://app.dimensions.ai/details/publication/pub.1055507419
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1029/2002jd002378 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016983263
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1029/2002jd003324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044982622
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1029/2003gl018323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003407447
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1029/2010jd015148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002524028
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1071/wf05012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043819598
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1080/01431160110078467 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037139734
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1080/01431160802226059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040846723
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1109/36.951076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061162705
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1109/rsete.2011.5964042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095140078
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1109/tgrs.2010.2047863 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061611456
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1175/1520-0477(1998)079<2457:sbiipf>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019455953
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1175/2008bams2354.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038737195
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1175/bams-88-10-1589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024723384
157 rdf:type schema:CreativeWork
158 https://doi.org/10.2151/jmsj.2016-009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039072090
159 rdf:type schema:CreativeWork
160 https://doi.org/10.22146/mgi.15624 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069330854
161 rdf:type schema:CreativeWork
162 https://doi.org/10.3390/rs70404473 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020694248
163 rdf:type schema:CreativeWork
164 https://doi.org/10.3390/rs8010011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050755337
165 rdf:type schema:CreativeWork
166 https://doi.org/10.5194/acp-8-6739-2008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003648352
167 rdf:type schema:CreativeWork
168 https://doi.org/10.5194/acpd-15-22215-2015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013670307
169 rdf:type schema:CreativeWork
170 https://doi.org/10.5194/isprsarchives-xl-7-w3-219-2015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040970654
171 rdf:type schema:CreativeWork
172 https://www.grid.ac/institutes/grid.8570.a schema:alternateName Gadjah Mada University
173 schema:name Center for Aeronautical Meteorology, Agency of Meteorological, Climatological and Geophysical (BMKG), Kemayoran, 10720, Jakarta, Indonesia
174 Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, 55281, Yogyakarta, Indonesia
175 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...