Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Peng He [verfasserIn] Fan Yang [verfasserIn] Rutian Bi [verfasserIn] Lishuai Xu [verfasserIn] Jingshu Wang [verfasserIn] Xinqian Zheng [verfasserIn] Silalan Abudukade [verfasserIn] Wenbiao Wang [verfasserIn] Zhengnan Cui [verfasserIn] Qiao Tan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	yield estimation summer maize ESTARFM FSADF STNLFFM

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 13(2023), 2608, p 2608
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:2608, p 2608

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy13102608

Katalog-ID:	DOAJ093188781

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allfields	10.3390/agronomy13102608 doi (DE-627)DOAJ093188781 (DE-599)DOAJ6fdb582043ee443ea56106c9c5727f4c DE-627 ger DE-627 rakwb eng Peng He verfasserin aut Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. yield estimation summer maize ESTARFM FSADF STNLFFM Agriculture S Fan Yang verfasserin aut Rutian Bi verfasserin aut Lishuai Xu verfasserin aut Jingshu Wang verfasserin aut Xinqian Zheng verfasserin aut Silalan Abudukade verfasserin aut Wenbiao Wang verfasserin aut Zhengnan Cui verfasserin aut Qiao Tan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2608, p 2608 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2608, p 2608 https://doi.org/10.3390/agronomy13102608 kostenfrei https://doaj.org/article/6fdb582043ee443ea56106c9c5727f4c kostenfrei https://www.mdpi.com/2073-4395/13/10/2608 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 2608, p 2608
spelling	10.3390/agronomy13102608 doi (DE-627)DOAJ093188781 (DE-599)DOAJ6fdb582043ee443ea56106c9c5727f4c DE-627 ger DE-627 rakwb eng Peng He verfasserin aut Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. yield estimation summer maize ESTARFM FSADF STNLFFM Agriculture S Fan Yang verfasserin aut Rutian Bi verfasserin aut Lishuai Xu verfasserin aut Jingshu Wang verfasserin aut Xinqian Zheng verfasserin aut Silalan Abudukade verfasserin aut Wenbiao Wang verfasserin aut Zhengnan Cui verfasserin aut Qiao Tan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2608, p 2608 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2608, p 2608 https://doi.org/10.3390/agronomy13102608 kostenfrei https://doaj.org/article/6fdb582043ee443ea56106c9c5727f4c kostenfrei https://www.mdpi.com/2073-4395/13/10/2608 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 2608, p 2608
allfields_unstemmed	10.3390/agronomy13102608 doi (DE-627)DOAJ093188781 (DE-599)DOAJ6fdb582043ee443ea56106c9c5727f4c DE-627 ger DE-627 rakwb eng Peng He verfasserin aut Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. yield estimation summer maize ESTARFM FSADF STNLFFM Agriculture S Fan Yang verfasserin aut Rutian Bi verfasserin aut Lishuai Xu verfasserin aut Jingshu Wang verfasserin aut Xinqian Zheng verfasserin aut Silalan Abudukade verfasserin aut Wenbiao Wang verfasserin aut Zhengnan Cui verfasserin aut Qiao Tan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2608, p 2608 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2608, p 2608 https://doi.org/10.3390/agronomy13102608 kostenfrei https://doaj.org/article/6fdb582043ee443ea56106c9c5727f4c kostenfrei https://www.mdpi.com/2073-4395/13/10/2608 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 2608, p 2608
allfieldsGer	10.3390/agronomy13102608 doi (DE-627)DOAJ093188781 (DE-599)DOAJ6fdb582043ee443ea56106c9c5727f4c DE-627 ger DE-627 rakwb eng Peng He verfasserin aut Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. yield estimation summer maize ESTARFM FSADF STNLFFM Agriculture S Fan Yang verfasserin aut Rutian Bi verfasserin aut Lishuai Xu verfasserin aut Jingshu Wang verfasserin aut Xinqian Zheng verfasserin aut Silalan Abudukade verfasserin aut Wenbiao Wang verfasserin aut Zhengnan Cui verfasserin aut Qiao Tan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2608, p 2608 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2608, p 2608 https://doi.org/10.3390/agronomy13102608 kostenfrei https://doaj.org/article/6fdb582043ee443ea56106c9c5727f4c kostenfrei https://www.mdpi.com/2073-4395/13/10/2608 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 2608, p 2608
allfieldsSound	10.3390/agronomy13102608 doi (DE-627)DOAJ093188781 (DE-599)DOAJ6fdb582043ee443ea56106c9c5727f4c DE-627 ger DE-627 rakwb eng Peng He verfasserin aut Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. yield estimation summer maize ESTARFM FSADF STNLFFM Agriculture S Fan Yang verfasserin aut Rutian Bi verfasserin aut Lishuai Xu verfasserin aut Jingshu Wang verfasserin aut Xinqian Zheng verfasserin aut Silalan Abudukade verfasserin aut Wenbiao Wang verfasserin aut Zhengnan Cui verfasserin aut Qiao Tan verfasserin aut In Agronomy MDPI AG, 2012 13(2023), 2608, p 2608 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:13 year:2023 number:2608, p 2608 https://doi.org/10.3390/agronomy13102608 kostenfrei https://doaj.org/article/6fdb582043ee443ea56106c9c5727f4c kostenfrei https://www.mdpi.com/2073-4395/13/10/2608 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 2608, p 2608
language	English
source	In Agronomy 13(2023), 2608, p 2608 volume:13 year:2023 number:2608, p 2608
sourceStr	In Agronomy 13(2023), 2608, p 2608 volume:13 year:2023 number:2608, p 2608
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	yield estimation summer maize ESTARFM FSADF STNLFFM Agriculture S
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container_title	Agronomy
authorswithroles_txt_mv	Peng He @@aut@@ Fan Yang @@aut@@ Rutian Bi @@aut@@ Lishuai Xu @@aut@@ Jingshu Wang @@aut@@ Xinqian Zheng @@aut@@ Silalan Abudukade @@aut@@ Wenbiao Wang @@aut@@ Zhengnan Cui @@aut@@ Qiao Tan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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author	Peng He
spellingShingle	Peng He misc yield estimation misc summer maize misc ESTARFM misc FSADF misc STNLFFM misc Agriculture misc S Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau
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topic_title	Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau yield estimation summer maize ESTARFM FSADF STNLFFM
topic	misc yield estimation misc summer maize misc ESTARFM misc FSADF misc STNLFFM misc Agriculture misc S
topic_unstemmed	misc yield estimation misc summer maize misc ESTARFM misc FSADF misc STNLFFM misc Agriculture misc S
topic_browse	misc yield estimation misc summer maize misc ESTARFM misc FSADF misc STNLFFM misc Agriculture misc S
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title	Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau
ctrlnum	(DE-627)DOAJ093188781 (DE-599)DOAJ6fdb582043ee443ea56106c9c5727f4c
title_full	Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau
author_sort	Peng He
journal	Agronomy
journalStr	Agronomy
lang_code	eng
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author_browse	Peng He Fan Yang Rutian Bi Lishuai Xu Jingshu Wang Xinqian Zheng Silalan Abudukade Wenbiao Wang Zhengnan Cui Qiao Tan
container_volume	13
format_se	Elektronische Aufsätze
author-letter	Peng He
doi_str_mv	10.3390/agronomy13102608
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title_sort	adaptability evaluation of the spatiotemporal fusion model in the summer maize planting area of the southeast loess plateau
title_auth	Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau
abstract	Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.
abstractGer	Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.
abstract_unstemmed	Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.
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title_short	Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau
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author2	Fan Yang Rutian Bi Lishuai Xu Jingshu Wang Xinqian Zheng Silalan Abudukade Wenbiao Wang Zhengnan Cui Qiao Tan
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