A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiom...
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Autor*in:	Mengmeng Sun [verfasserIn] Xiang Zhao [verfasserIn] Jiacheng Zhao [verfasserIn] Naijing Liu [verfasserIn] Siqing Zhao [verfasserIn] Yinkun Guo [verfasserIn] Wenxi Shi [verfasserIn] Longping Si [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 17(2024), Seite 7068-7088
Übergeordnetes Werk:	volume:17 ; year:2024 ; pages:7068-7088

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2024.3373884

Katalog-ID:	DOAJ095794743

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700	0		\|a Longping Si \|e verfasserin \|4 aut
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spelling	10.1109/JSTARS.2024.3373884 doi (DE-627)DOAJ095794743 (DE-599)DOAJ76c7efdc30b34f13852b42f009d4f5b3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Mengmeng Sun verfasserin aut A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth. Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block Ocean engineering Geophysics. Cosmic physics Xiang Zhao verfasserin aut Jiacheng Zhao verfasserin aut Naijing Liu verfasserin aut Siqing Zhao verfasserin aut Yinkun Guo verfasserin aut Wenxi Shi verfasserin aut Longping Si verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 17(2024), Seite 7068-7088 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:17 year:2024 pages:7068-7088 https://doi.org/10.1109/JSTARS.2024.3373884 kostenfrei https://doaj.org/article/76c7efdc30b34f13852b42f009d4f5b3 kostenfrei https://ieeexplore.ieee.org/document/10461008/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 7068-7088
allfields_unstemmed	10.1109/JSTARS.2024.3373884 doi (DE-627)DOAJ095794743 (DE-599)DOAJ76c7efdc30b34f13852b42f009d4f5b3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Mengmeng Sun verfasserin aut A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth. Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block Ocean engineering Geophysics. Cosmic physics Xiang Zhao verfasserin aut Jiacheng Zhao verfasserin aut Naijing Liu verfasserin aut Siqing Zhao verfasserin aut Yinkun Guo verfasserin aut Wenxi Shi verfasserin aut Longping Si verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 17(2024), Seite 7068-7088 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:17 year:2024 pages:7068-7088 https://doi.org/10.1109/JSTARS.2024.3373884 kostenfrei https://doaj.org/article/76c7efdc30b34f13852b42f009d4f5b3 kostenfrei https://ieeexplore.ieee.org/document/10461008/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 7068-7088
allfieldsGer	10.1109/JSTARS.2024.3373884 doi (DE-627)DOAJ095794743 (DE-599)DOAJ76c7efdc30b34f13852b42f009d4f5b3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Mengmeng Sun verfasserin aut A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth. Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block Ocean engineering Geophysics. Cosmic physics Xiang Zhao verfasserin aut Jiacheng Zhao verfasserin aut Naijing Liu verfasserin aut Siqing Zhao verfasserin aut Yinkun Guo verfasserin aut Wenxi Shi verfasserin aut Longping Si verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 17(2024), Seite 7068-7088 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:17 year:2024 pages:7068-7088 https://doi.org/10.1109/JSTARS.2024.3373884 kostenfrei https://doaj.org/article/76c7efdc30b34f13852b42f009d4f5b3 kostenfrei https://ieeexplore.ieee.org/document/10461008/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 7068-7088
allfieldsSound	10.1109/JSTARS.2024.3373884 doi (DE-627)DOAJ095794743 (DE-599)DOAJ76c7efdc30b34f13852b42f009d4f5b3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Mengmeng Sun verfasserin aut A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth. Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block Ocean engineering Geophysics. Cosmic physics Xiang Zhao verfasserin aut Jiacheng Zhao verfasserin aut Naijing Liu verfasserin aut Siqing Zhao verfasserin aut Yinkun Guo verfasserin aut Wenxi Shi verfasserin aut Longping Si verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 17(2024), Seite 7068-7088 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:17 year:2024 pages:7068-7088 https://doi.org/10.1109/JSTARS.2024.3373884 kostenfrei https://doaj.org/article/76c7efdc30b34f13852b42f009d4f5b3 kostenfrei https://ieeexplore.ieee.org/document/10461008/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 7068-7088
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17(2024), Seite 7068-7088 volume:17 year:2024 pages:7068-7088
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17(2024), Seite 7068-7088 volume:17 year:2024 pages:7068-7088
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Mengmeng Sun @@aut@@ Xiang Zhao @@aut@@ Jiacheng Zhao @@aut@@ Naijing Liu @@aut@@ Siqing Zhao @@aut@@ Yinkun Guo @@aut@@ Wenxi Shi @@aut@@ Longping Si @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	581732634
id	DOAJ095794743
language_de	englisch
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callnumber-first	T - Technology
author	Mengmeng Sun
spellingShingle	Mengmeng Sun misc TC1501-1800 misc QC801-809 misc Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) misc diverse features misc multiscale structure misc residual block misc Ocean engineering misc Geophysics. Cosmic physics A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network
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topic_title	TC1501-1800 QC801-809 A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) diverse features multiscale structure residual block
topic	misc TC1501-1800 misc QC801-809 misc Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) misc diverse features misc multiscale structure misc residual block misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) misc diverse features misc multiscale structure misc residual block misc Ocean engineering misc Geophysics. Cosmic physics
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title	A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network
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title_full	A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network
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author_browse	Mengmeng Sun Xiang Zhao Jiacheng Zhao Naijing Liu Siqing Zhao Yinkun Guo Wenxi Shi Longping Si
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title_sort	new spatial downscaling method for long-term avhrr ndvi by multiscale residual convolutional neural network
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title_auth	A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network
abstract	Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth.
abstractGer	Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth.
abstract_unstemmed	Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth.
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title_short	A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network
url	https://doi.org/10.1109/JSTARS.2024.3373884 https://doaj.org/article/76c7efdc30b34f13852b42f009d4f5b3 https://ieeexplore.ieee.org/document/10461008/ https://doaj.org/toc/2151-1535
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up_date	2024-07-03T16:39:42.194Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ095794743</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413122744.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/JSTARS.2024.3373884</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ095794743</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ76c7efdc30b34f13852b42f009d4f5b3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC1501-1800</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC801-809</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Mengmeng Sun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. 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The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. 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A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

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