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Algorithm development for drought mapping using imagery Sentinel-2

Drought mapping is needed in mitigation efforts. Several applications of rapid drought mapping techniques using remote sensing such as the normalized difference vegetation index (NDVI) have not provided accurate results. This study aims to quickly develop a drought mapping technique using the NDVI d...

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Main Authors:	Satriawan, Puthut Omar, Hidayah, Entin, Halik, Gusfan
Format:	Conference Proceeding
Language:	English
Subjects:	Algorithms Drought Dry season Extreme values Mapping Normalized difference vegetative index Remote sensing
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creator	Satriawan, Puthut Omar Hidayah, Entin Halik, Gusfan
description	Drought mapping is needed in mitigation efforts. Several applications of rapid drought mapping techniques using remote sensing such as the normalized difference vegetation index (NDVI) have not provided accurate results. This study aims to quickly develop a drought mapping technique using the NDVI development algorithm to produce an accurate drought map. The development of the new algorithm is based on evaluating the NDVI approach by combining water-sensitive bands. Sentinel-2 image data for three months in the dry season is used as an input model. The drought-level classification was treated to get the best model. The reliability of the model is measured by the overall accuracy value (OA) and kappa which compares the model drought map results from moderate to extreme levels with the results of drought identification in the field. Accuracy results obtained a KAPPA value of 0.83 and 80% OA. The results of this map can be used for drought mitigation.
doi_str_mv	10.1063/5.0206474
format	conference_proceeding
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Several applications of rapid drought mapping techniques using remote sensing such as the normalized difference vegetation index (NDVI) have not provided accurate results. This study aims to quickly develop a drought mapping technique using the NDVI development algorithm to produce an accurate drought map. The development of the new algorithm is based on evaluating the NDVI approach by combining water-sensitive bands. Sentinel-2 image data for three months in the dry season is used as an input model. The drought-level classification was treated to get the best model. The reliability of the model is measured by the overall accuracy value (OA) and kappa which compares the model drought map results from moderate to extreme levels with the results of drought identification in the field. Accuracy results obtained a KAPPA value of 0.83 and 80% OA. 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source	American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects	Algorithms Drought Dry season Extreme values Mapping Normalized difference vegetative index Remote sensing
title	Algorithm development for drought mapping using imagery Sentinel-2
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