Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed...

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Autores principales:	Rongkun Zhao, Yuechen Li, Jin Chen, Mingguo Ma, Lei Fan, Wei Lu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	paddy rice MNSPI remove cloud FSDAF phenology-based algorithm time series Science Q
Acceso en línea:	https://doaj.org/article/950f23ed0e3348f092429eb3388dc0dd
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spelling	oai:doaj.org-article:950f23ed0e3348f092429eb3388dc0dd2021-11-11T18:55:37ZMapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm10.3390/rs132144002072-4292https://doaj.org/article/950f23ed0e3348f092429eb3388dc0dd2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4400https://doaj.org/toc/2072-4292The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.Rongkun ZhaoYuechen LiJin ChenMingguo MaLei FanWei LuMDPI AGarticlepaddy riceMNSPI remove cloudFSDAFphenology-based algorithmtime seriesScienceQENRemote Sensing, Vol 13, Iss 4400, p 4400 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	paddy rice MNSPI remove cloud FSDAF phenology-based algorithm time series Science Q
spellingShingle	paddy rice MNSPI remove cloud FSDAF phenology-based algorithm time series Science Q Rongkun Zhao Yuechen Li Jin Chen Mingguo Ma Lei Fan Wei Lu Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
description	The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.
format	article
author	Rongkun Zhao Yuechen Li Jin Chen Mingguo Ma Lei Fan Wei Lu
author_facet	Rongkun Zhao Yuechen Li Jin Chen Mingguo Ma Lei Fan Wei Lu
author_sort	Rongkun Zhao
title	Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
title_short	Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
title_full	Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
title_fullStr	Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
title_full_unstemmed	Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
title_sort	mapping a paddy rice area in a cloudy and rainy region using spatiotemporal data fusion and a phenology-based algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/950f23ed0e3348f092429eb3388dc0dd
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Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

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