Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images

Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fus...
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Autor*in:	Tang, Jia [verfasserIn] Zeng, Jingyu Zhang, Qing Zhang, Rongrong Leng, Song Zeng, Yue Shui, Wei Xu, Zhanghua Wang, Qianfeng

Format:	Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area

Anmerkung:	© ISB 2020

Übergeordnetes Werk:	Enthalten in: International journal of biometeorology - Springer Berlin Heidelberg, 1961, 64(2020), 8 vom: 08. Apr., Seite 1273-1283
Übergeordnetes Werk:	volume:64 ; year:2020 ; number:8 ; day:08 ; month:04 ; pages:1273-1283

Links:	Volltext

DOI / URN:	10.1007/s00484-020-01904-1

Katalog-ID:	OLC2118488270

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520			\|a Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.
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allfields	10.1007/s00484-020-01904-1 doi (DE-627)OLC2118488270 (DE-He213)s00484-020-01904-1-p DE-627 ger DE-627 rakwb eng 570 550 VZ 570 VZ 12 ssgn BIODIV DE-30 fid Tang, Jia verfasserin aut Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISB 2020 Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area Zeng, Jingyu aut Zhang, Qing aut Zhang, Rongrong aut Leng, Song aut Zeng, Yue aut Shui, Wei aut Xu, Zhanghua aut Wang, Qianfeng aut Enthalten in International journal of biometeorology Springer Berlin Heidelberg, 1961 64(2020), 8 vom: 08. Apr., Seite 1273-1283 (DE-627)12985106X (DE-600)280324-0 (DE-576)015150259 0020-7128 nnns volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283 https://doi.org/10.1007/s00484-020-01904-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_2018 GBV_ILN_4277 AR 64 2020 8 08 04 1273-1283
spelling	10.1007/s00484-020-01904-1 doi (DE-627)OLC2118488270 (DE-He213)s00484-020-01904-1-p DE-627 ger DE-627 rakwb eng 570 550 VZ 570 VZ 12 ssgn BIODIV DE-30 fid Tang, Jia verfasserin aut Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISB 2020 Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area Zeng, Jingyu aut Zhang, Qing aut Zhang, Rongrong aut Leng, Song aut Zeng, Yue aut Shui, Wei aut Xu, Zhanghua aut Wang, Qianfeng aut Enthalten in International journal of biometeorology Springer Berlin Heidelberg, 1961 64(2020), 8 vom: 08. Apr., Seite 1273-1283 (DE-627)12985106X (DE-600)280324-0 (DE-576)015150259 0020-7128 nnns volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283 https://doi.org/10.1007/s00484-020-01904-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_2018 GBV_ILN_4277 AR 64 2020 8 08 04 1273-1283
allfields_unstemmed	10.1007/s00484-020-01904-1 doi (DE-627)OLC2118488270 (DE-He213)s00484-020-01904-1-p DE-627 ger DE-627 rakwb eng 570 550 VZ 570 VZ 12 ssgn BIODIV DE-30 fid Tang, Jia verfasserin aut Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISB 2020 Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area Zeng, Jingyu aut Zhang, Qing aut Zhang, Rongrong aut Leng, Song aut Zeng, Yue aut Shui, Wei aut Xu, Zhanghua aut Wang, Qianfeng aut Enthalten in International journal of biometeorology Springer Berlin Heidelberg, 1961 64(2020), 8 vom: 08. Apr., Seite 1273-1283 (DE-627)12985106X (DE-600)280324-0 (DE-576)015150259 0020-7128 nnns volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283 https://doi.org/10.1007/s00484-020-01904-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_2018 GBV_ILN_4277 AR 64 2020 8 08 04 1273-1283
allfieldsGer	10.1007/s00484-020-01904-1 doi (DE-627)OLC2118488270 (DE-He213)s00484-020-01904-1-p DE-627 ger DE-627 rakwb eng 570 550 VZ 570 VZ 12 ssgn BIODIV DE-30 fid Tang, Jia verfasserin aut Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISB 2020 Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area Zeng, Jingyu aut Zhang, Qing aut Zhang, Rongrong aut Leng, Song aut Zeng, Yue aut Shui, Wei aut Xu, Zhanghua aut Wang, Qianfeng aut Enthalten in International journal of biometeorology Springer Berlin Heidelberg, 1961 64(2020), 8 vom: 08. Apr., Seite 1273-1283 (DE-627)12985106X (DE-600)280324-0 (DE-576)015150259 0020-7128 nnns volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283 https://doi.org/10.1007/s00484-020-01904-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_2018 GBV_ILN_4277 AR 64 2020 8 08 04 1273-1283
allfieldsSound	10.1007/s00484-020-01904-1 doi (DE-627)OLC2118488270 (DE-He213)s00484-020-01904-1-p DE-627 ger DE-627 rakwb eng 570 550 VZ 570 VZ 12 ssgn BIODIV DE-30 fid Tang, Jia verfasserin aut Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISB 2020 Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area Zeng, Jingyu aut Zhang, Qing aut Zhang, Rongrong aut Leng, Song aut Zeng, Yue aut Shui, Wei aut Xu, Zhanghua aut Wang, Qianfeng aut Enthalten in International journal of biometeorology Springer Berlin Heidelberg, 1961 64(2020), 8 vom: 08. Apr., Seite 1273-1283 (DE-627)12985106X (DE-600)280324-0 (DE-576)015150259 0020-7128 nnns volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283 https://doi.org/10.1007/s00484-020-01904-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_2018 GBV_ILN_4277 AR 64 2020 8 08 04 1273-1283
language	English
source	Enthalten in International journal of biometeorology 64(2020), 8 vom: 08. Apr., Seite 1273-1283 volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283
sourceStr	Enthalten in International journal of biometeorology 64(2020), 8 vom: 08. Apr., Seite 1273-1283 volume:64 year:2020 number:8 day:08 month:04 pages:1273-1283
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area
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container_title	International journal of biometeorology
authorswithroles_txt_mv	Tang, Jia @@aut@@ Zeng, Jingyu @@aut@@ Zhang, Qing @@aut@@ Zhang, Rongrong @@aut@@ Leng, Song @@aut@@ Zeng, Yue @@aut@@ Shui, Wei @@aut@@ Xu, Zhanghua @@aut@@ Wang, Qianfeng @@aut@@
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author	Tang, Jia
spellingShingle	Tang, Jia ddc 570 ssgn 12 fid BIODIV misc ESTARFM misc Softmax misc Self-adapting algorithm misc Cropland phenology misc Xiong’an New Area Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images
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topic_title	570 550 VZ 570 VZ 12 ssgn BIODIV DE-30 fid Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images ESTARFM Softmax Self-adapting algorithm Cropland phenology Xiong’an New Area
topic	ddc 570 ssgn 12 fid BIODIV misc ESTARFM misc Softmax misc Self-adapting algorithm misc Cropland phenology misc Xiong’an New Area
topic_unstemmed	ddc 570 ssgn 12 fid BIODIV misc ESTARFM misc Softmax misc Self-adapting algorithm misc Cropland phenology misc Xiong’an New Area
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title	Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images
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title_full	Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images
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title_sort	self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused ndvi images
title_auth	Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images
abstract	Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. © ISB 2020
abstractGer	Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. © ISB 2020
abstract_unstemmed	Abstract Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale. © ISB 2020
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title_short	Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images
url	https://doi.org/10.1007/s00484-020-01904-1
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author2	Zeng, Jingyu Zhang, Qing Zhang, Rongrong Leng, Song Zeng, Yue Shui, Wei Xu, Zhanghua Wang, Qianfeng
author2Str	Zeng, Jingyu Zhang, Qing Zhang, Rongrong Leng, Song Zeng, Yue Shui, Wei Xu, Zhanghua Wang, Qianfeng
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up_date	2024-07-03T19:41:27.271Z
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