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An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions

Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal...

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Autores principales: Xie, Dengfeng, Zhang, Jinshui, Zhu, Xiufang, Pan, Yaozhong, Liu, Hongli, Yuan, Zhoumiqi, Yun, Ya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801583/
https://www.ncbi.nlm.nih.gov/pubmed/26861334
http://dx.doi.org/10.3390/s16020207
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author Xie, Dengfeng
Zhang, Jinshui
Zhu, Xiufang
Pan, Yaozhong
Liu, Hongli
Yuan, Zhoumiqi
Yun, Ya
author_facet Xie, Dengfeng
Zhang, Jinshui
Zhu, Xiufang
Pan, Yaozhong
Liu, Hongli
Yuan, Zhoumiqi
Yun, Ya
author_sort Xie, Dengfeng
collection PubMed
description Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.
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spelling pubmed-48015832016-03-25 An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions Xie, Dengfeng Zhang, Jinshui Zhu, Xiufang Pan, Yaozhong Liu, Hongli Yuan, Zhoumiqi Yun, Ya Sensors (Basel) Article Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation. MDPI 2016-02-05 /pmc/articles/PMC4801583/ /pubmed/26861334 http://dx.doi.org/10.3390/s16020207 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xie, Dengfeng
Zhang, Jinshui
Zhu, Xiufang
Pan, Yaozhong
Liu, Hongli
Yuan, Zhoumiqi
Yun, Ya
An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions
title An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions
title_full An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions
title_fullStr An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions
title_full_unstemmed An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions
title_short An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions
title_sort improved starfm with help of an unmixing-based method to generate high spatial and temporal resolution remote sensing data in complex heterogeneous regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801583/
https://www.ncbi.nlm.nih.gov/pubmed/26861334
http://dx.doi.org/10.3390/s16020207
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