High Resolution Distribution Dataset of Double-Season Paddy Rice in China

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting...
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Gespeichert in:

Autor*in:	Baihong Pan [verfasserIn] Yi Zheng [verfasserIn] Ruoque Shen [verfasserIn] Tao Ye [verfasserIn] Wenzhi Zhao [verfasserIn] Jie Dong [verfasserIn] Hanqing Ma [verfasserIn] Wenping Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	early rice late rice double-season rice time-weighted dynamic time warping synthetic aperture radar planting area

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 22, p 4609
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:22, p 4609

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13224609

Katalog-ID:	DOAJ046788204

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allfieldsSound	10.3390/rs13224609 doi (DE-627)DOAJ046788204 (DE-599)DOAJ8febfd005a4c4248a2b75dfab46a8216 DE-627 ger DE-627 rakwb eng Baihong Pan verfasserin aut High Resolution Distribution Dataset of Double-Season Paddy Rice in China 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield. early rice late rice double-season rice time-weighted dynamic time warping synthetic aperture radar planting area Science Q Yi Zheng verfasserin aut Ruoque Shen verfasserin aut Tao Ye verfasserin aut Wenzhi Zhao verfasserin aut Jie Dong verfasserin aut Hanqing Ma verfasserin aut Wenping Yuan verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 22, p 4609 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:22, p 4609 https://doi.org/10.3390/rs13224609 kostenfrei https://doaj.org/article/8febfd005a4c4248a2b75dfab46a8216 kostenfrei https://www.mdpi.com/2072-4292/13/22/4609 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 22, p 4609
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author	Baihong Pan
spellingShingle	Baihong Pan misc early rice misc late rice misc double-season rice misc time-weighted dynamic time warping misc synthetic aperture radar misc planting area misc Science misc Q High Resolution Distribution Dataset of Double-Season Paddy Rice in China
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topic_title	High Resolution Distribution Dataset of Double-Season Paddy Rice in China early rice late rice double-season rice time-weighted dynamic time warping synthetic aperture radar planting area
topic	misc early rice misc late rice misc double-season rice misc time-weighted dynamic time warping misc synthetic aperture radar misc planting area misc Science misc Q
topic_unstemmed	misc early rice misc late rice misc double-season rice misc time-weighted dynamic time warping misc synthetic aperture radar misc planting area misc Science misc Q
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title	High Resolution Distribution Dataset of Double-Season Paddy Rice in China
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title_full	High Resolution Distribution Dataset of Double-Season Paddy Rice in China
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title_sort	high resolution distribution dataset of double-season paddy rice in china
title_auth	High Resolution Distribution Dataset of Double-Season Paddy Rice in China
abstract	Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.
abstractGer	Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.
abstract_unstemmed	Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.
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title_short	High Resolution Distribution Dataset of Double-Season Paddy Rice in China
url	https://doi.org/10.3390/rs13224609 https://doaj.org/article/8febfd005a4c4248a2b75dfab46a8216 https://www.mdpi.com/2072-4292/13/22/4609 https://doaj.org/toc/2072-4292
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High Resolution Distribution Dataset of Double-Season Paddy Rice in China

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