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Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover

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Title: Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover
Authors: Sonobe, Rei Browse this author →KAKEN DB
Yamaya, Yuki Browse this author
Tani, Hiroshi Browse this author →KAKEN DB
Wang, Xiufeng Browse this author →KAKEN DB
Kobayashi, Nobuyuki Browse this author
Mochizuki, Kan-ichiro Browse this author
Keywords: Crop
deep forest
Landsat 8
random forests
spectral indices
Issue Date: 3-Jul-2019
Publisher: Taylor & Francis
Journal Title: Geocarto International
Volume: 34
Issue: 8
Start Page: 839
End Page: 855
Publisher DOI: 10.1080/10106049.2018.1425739
Abstract: Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification.
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 3 Jul. 2019, available online:
Type: article (author version)
Appears in Collections:農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 山谷 祐貴

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