{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:32:01Z","timestamp":1778167921244,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SmartSat CRC","award":["P3-07s"],"award-info":[{"award-number":["P3-07s"]}]},{"name":"Australian Government\u2019s CRC Program","award":["P3-07s"],"award-info":[{"award-number":["P3-07s"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Smoke plumes are the first things seen from space when wildfires occur. Thus, fire smoke detection is important for early fire detection. Deep Learning (DL) models have been used to detect fire smoke in satellite imagery for fire detection. However, previous DL-based research only considered lower spatial resolution sensors (e.g., Moderate-Resolution Imaging Spectroradiometer (MODIS)) and only used the visible (i.e., red, green, blue (RGB)) bands. To contribute towards solutions for early fire smoke detection, we constructed a six-band imagery dataset from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) with a 30-metre spatial resolution. The dataset consists of 1836 images in three classes, namely \u201cSmoke\u201d, \u201cClear\u201d, and \u201cOther_aerosol\u201d. To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named \u201cVariant Input Bands for Smoke Detection (VIB_SD)\u201d, which achieved competitive accuracy with the state-of-the-art model SAFA, with less than 2% of its number of parameters. We further investigated the impact of using additional Infra-Red (IR) bands on the accuracy of fire smoke detection with VIB_SD by training it with five different band combinations. The results demonstrated that adding the Near-Infra-Red (NIR) band improved prediction accuracy compared with only using the visible bands. Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. The case study showed that the model trained with multispectral bands could effectively detect fire smoke mixed with cloud over small geographic extents.<\/jats:p>","DOI":"10.3390\/rs14133047","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"3047","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6037-2947","authenticated-orcid":false,"given":"Liang","family":"Zhao","sequence":"first","affiliation":[{"name":"UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jixue","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3604-4625","authenticated-orcid":false,"given":"Stefan","family":"Peters","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Oliver","sequence":"additional","affiliation":[{"name":"Geoscience Australia, 101 Jerrabomberra Ave., Symonston, ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norman","family":"Mueller","sequence":"additional","affiliation":[{"name":"Geoscience Australia, 101 Jerrabomberra Ave., Symonston, ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tedim, F., Leone, V., Amraoui, M., Bouillon, C., Coughlan, M.R., Delogu, G.M., Fernandes, P.M., Ferreira, C., McCaffrey, S., and McGee, T.K. 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