A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yanling Ding [verfasserIn] Hongyan Zhang [verfasserIn] Zhongqiang Wang [verfasserIn] Qiaoyun Xie [verfasserIn] Yeqiao Wang [verfasserIn] Lin Liu [verfasserIn] Christopher C. Hall [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 9, p 1470
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:9, p 1470

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12091470

Katalog-ID:	DOAJ086761072

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520			\|a Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.
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allfields	10.3390/rs12091470 doi (DE-627)DOAJ086761072 (DE-599)DOAJcdff43d481604bc7bee8e9b450c468ad DE-627 ger DE-627 rakwb eng Yanling Ding verfasserin aut A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature Science Q Hongyan Zhang verfasserin aut Zhongqiang Wang verfasserin aut Qiaoyun Xie verfasserin aut Yeqiao Wang verfasserin aut Lin Liu verfasserin aut Christopher C. Hall verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 9, p 1470 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:9, p 1470 https://doi.org/10.3390/rs12091470 kostenfrei https://doaj.org/article/cdff43d481604bc7bee8e9b450c468ad kostenfrei https://www.mdpi.com/2072-4292/12/9/1470 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 9, p 1470
spelling	10.3390/rs12091470 doi (DE-627)DOAJ086761072 (DE-599)DOAJcdff43d481604bc7bee8e9b450c468ad DE-627 ger DE-627 rakwb eng Yanling Ding verfasserin aut A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature Science Q Hongyan Zhang verfasserin aut Zhongqiang Wang verfasserin aut Qiaoyun Xie verfasserin aut Yeqiao Wang verfasserin aut Lin Liu verfasserin aut Christopher C. Hall verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 9, p 1470 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:9, p 1470 https://doi.org/10.3390/rs12091470 kostenfrei https://doaj.org/article/cdff43d481604bc7bee8e9b450c468ad kostenfrei https://www.mdpi.com/2072-4292/12/9/1470 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 9, p 1470
allfields_unstemmed	10.3390/rs12091470 doi (DE-627)DOAJ086761072 (DE-599)DOAJcdff43d481604bc7bee8e9b450c468ad DE-627 ger DE-627 rakwb eng Yanling Ding verfasserin aut A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature Science Q Hongyan Zhang verfasserin aut Zhongqiang Wang verfasserin aut Qiaoyun Xie verfasserin aut Yeqiao Wang verfasserin aut Lin Liu verfasserin aut Christopher C. Hall verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 9, p 1470 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:9, p 1470 https://doi.org/10.3390/rs12091470 kostenfrei https://doaj.org/article/cdff43d481604bc7bee8e9b450c468ad kostenfrei https://www.mdpi.com/2072-4292/12/9/1470 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 9, p 1470
allfieldsGer	10.3390/rs12091470 doi (DE-627)DOAJ086761072 (DE-599)DOAJcdff43d481604bc7bee8e9b450c468ad DE-627 ger DE-627 rakwb eng Yanling Ding verfasserin aut A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature Science Q Hongyan Zhang verfasserin aut Zhongqiang Wang verfasserin aut Qiaoyun Xie verfasserin aut Yeqiao Wang verfasserin aut Lin Liu verfasserin aut Christopher C. Hall verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 9, p 1470 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:9, p 1470 https://doi.org/10.3390/rs12091470 kostenfrei https://doaj.org/article/cdff43d481604bc7bee8e9b450c468ad kostenfrei https://www.mdpi.com/2072-4292/12/9/1470 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 9, p 1470
allfieldsSound	10.3390/rs12091470 doi (DE-627)DOAJ086761072 (DE-599)DOAJcdff43d481604bc7bee8e9b450c468ad DE-627 ger DE-627 rakwb eng Yanling Ding verfasserin aut A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature Science Q Hongyan Zhang verfasserin aut Zhongqiang Wang verfasserin aut Qiaoyun Xie verfasserin aut Yeqiao Wang verfasserin aut Lin Liu verfasserin aut Christopher C. Hall verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 9, p 1470 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:9, p 1470 https://doi.org/10.3390/rs12091470 kostenfrei https://doaj.org/article/cdff43d481604bc7bee8e9b450c468ad kostenfrei https://www.mdpi.com/2072-4292/12/9/1470 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 9, p 1470
language	English
source	In Remote Sensing 12(2020), 9, p 1470 volume:12 year:2020 number:9, p 1470
sourceStr	In Remote Sensing 12(2020), 9, p 1470 volume:12 year:2020 number:9, p 1470
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Yanling Ding @@aut@@ Hongyan Zhang @@aut@@ Zhongqiang Wang @@aut@@ Qiaoyun Xie @@aut@@ Yeqiao Wang @@aut@@ Lin Liu @@aut@@ Christopher C. Hall @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ086761072
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ086761072</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412234501.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs12091470</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086761072</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJcdff43d481604bc7bee8e9b450c468ad</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="100" ind1="0" ind2=" "><subfield code="a">Yanling Ding</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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">Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. 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author	Yanling Ding
spellingShingle	Yanling Ding misc crop residue cover misc crop residue indices misc empirical regression misc machine learning regression misc Sentinel-2 MSI misc textural feature misc Science misc Q A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods
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topic_title	A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods crop residue cover crop residue indices empirical regression machine learning regression Sentinel-2 MSI textural feature
topic	misc crop residue cover misc crop residue indices misc empirical regression misc machine learning regression misc Sentinel-2 MSI misc textural feature misc Science misc Q
topic_unstemmed	misc crop residue cover misc crop residue indices misc empirical regression misc machine learning regression misc Sentinel-2 MSI misc textural feature misc Science misc Q
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title	A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods
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title_full	A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods
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title_sort	comparison of estimating crop residue cover from sentinel-2 data using empirical regressions and machine learning methods
title_auth	A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods
abstract	Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.
abstractGer	Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.
abstract_unstemmed	Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an <i<R</i<<sup<2</sup<<sub<cv</sub< of 0.63 and RMSE<sub<cv</sub< of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.
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title_short	A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods
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The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.66 and RMSE<sub<cv</sub< = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.61 and RMSE<sub<cv</sub< = 6.415%. The estimation was improved by an SVR model with the same input predictors (<i<R</i<<sup<2</sup<<sub<cv</sub< = 0.67, RMSE<sub<cv</sub< = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with <i<R</i<<sup<2</sup<<sub<cv</sub< = 0.69 and RMSE<sub<cv</sub< = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. 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A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods

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