Identification of paddy fields based on temporal phase using Spectral Angle Mapper (SAM) on Landsat-8 OLI

Mohammad Naufal Fathoni; Gelanggoro Kismo Anintika; Dariin Firda; Pronika Kricella; Prima Widayani; Sanjiwana Arjakusuma; Muhammad Kamal

doi:10.1117/12.2619435

22 December 2021 Identification of paddy fields based on temporal phase using Spectral Angle Mapper (SAM) on Landsat-8 OLI

Mohammad Naufal Fathoni, Gelanggoro Kismo Anintika, Dariin Firda, Pronika Kricella, Prima Widayani, Sanjiwana Arjakusuma, Muhammad Kamal

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Proceedings Volume 12082, Seventh Geoinformation Science Symposium 2021; 120820B (2021) https://doi.org/10.1117/12.2619435
Event: Seventh Geoinformation Science Symposium (GSS 2021), 2021, Yogyakarta, Indonesia

Abstract

Updating information on rice fields is very important to pay attention to environmental quality and food security. This is related to Indonesia's commitment to achieving Sustainable Development Goal number two in terms of agricultural data collection and analysis. Remote sensing can be used as an alternative method for identifying and mapping land cover, for instance paddy fields. Land cover in paddy fields varies greatly according to paddy growth phase, wherein these growth phase can be shown by different spectral reflectance values in remote sensing imagery. Mapping of paddy fields based on their spectral reflectance began to be widely carried out in Indonesia. Therefore, the aims of this study were to determine the spectral reflectance pattern of temporal paddy growth phase then form a map of the paddy fields based on those spectral libraries. This study used Spectral Angle Mapper (SAM) method to identify paddy fields on Landsat-8 OLI determined from spectral reflectance pattern of paddy-growth phase in some areas of Subang and Indramayu Regency in one growing season. The results succeeded in classifying paddy fields and non-paddy fields area. Classified paddy fields consisted of several land covers comprising the bare-land, inundation-land, vegetative, generative, and ripening. The accuracy test showed an overall accuracy of 70.07%. Misclassification in this study occurred due to the existence of thin cloud cover, besides there was a misclassification between built-up area and the bare land.

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Mohammad Naufal Fathoni, Gelanggoro Kismo Anintika, Dariin Firda, Pronika Kricella, Prima Widayani, Sanjiwana Arjakusuma, and Muhammad Kamal "Identification of paddy fields based on temporal phase using Spectral Angle Mapper (SAM) on Landsat-8 OLI", Proc. SPIE 12082, Seventh Geoinformation Science Symposium 2021, 120820B (22 December 2021); https://doi.org/10.1117/12.2619435

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KEYWORDS

Reflectivity

Earth observing sensors

Landsat

Remote sensing

Visualization

Agriculture

Vegetation

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