An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data
The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using ped...
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| Autor*in: |
J Huang [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soil use and management - Oxford : Blackwell Publ., 1985, 33(2017), 3, Seite 397 |
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| Übergeordnetes Werk: |
volume:33 ; year:2017 ; number:3 ; pages:397 |
| Links: |
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| DOI / URN: |
10.1111/sum.12347 |
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| 245 | 1 | 3 | |a An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data |
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| 520 | |a The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. | ||
| 650 | 4 | |a Maximum likelihood estimates | |
| 650 | 4 | |a Fertility | |
| 650 | 4 | |a Structural stability | |
| 650 | 4 | |a Electrical resistivity | |
| 650 | 4 | |a Remote sensing | |
| 650 | 4 | |a Electromagnetic induction | |
| 650 | 4 | |a Models | |
| 650 | 4 | |a Soil | |
| 650 | 4 | |a Budgeting | |
| 650 | 4 | |a Computer simulation | |
| 650 | 4 | |a Soil mapping | |
| 650 | 4 | |a Electrical conductivity | |
| 650 | 4 | |a Cation exchange | |
| 650 | 4 | |a Measurement methods | |
| 650 | 4 | |a Digital mapping | |
| 650 | 4 | |a Prediction models | |
| 650 | 4 | |a Cation exchanging | |
| 650 | 4 | |a Error analysis | |
| 650 | 4 | |a Mapping | |
| 650 | 4 | |a Soil improvement | |
| 650 | 4 | |a Mathematical models | |
| 650 | 4 | |a Geostatistics | |
| 650 | 4 | |a Conductivity | |
| 650 | 4 | |a Simulation | |
| 650 | 4 | |a Spectrometry | |
| 650 | 4 | |a Preprocessing | |
| 650 | 4 | |a Gamma rays | |
| 650 | 4 | |a Digital cartography | |
| 650 | 4 | |a Budgets | |
| 650 | 4 | |a Spatial discrimination | |
| 700 | 0 | |a T F A Bishop |4 oth | |
| 700 | 0 | |a J Triantafilis |4 oth | |
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10.1111/sum.12347 doi PQ20171228 (DE-627)OLC1998019349 (DE-599)GBVOLC1998019349 (PRQ)p571-a9ad2c5e909a64719415a568b84d64d7863129d63dc87b246409d25e28059e140 (KEY)0172628520170000033000300397errorbudgetfordigitalsoilmappingofcationexchangeca DE-627 ger DE-627 rakwb eng 630 640 DE-600 BIODIV fid 38.60 bkl 48.32 bkl J Huang verfasserin aut An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. Maximum likelihood estimates Fertility Structural stability Electrical resistivity Remote sensing Electromagnetic induction Models Soil Budgeting Computer simulation Soil mapping Electrical conductivity Cation exchange Measurement methods Digital mapping Prediction models Cation exchanging Error analysis Mapping Soil improvement Mathematical models Geostatistics Conductivity Simulation Spectrometry Preprocessing Gamma rays Digital cartography Budgets Spatial discrimination T F A Bishop oth J Triantafilis oth Enthalten in Soil use and management Oxford : Blackwell Publ., 1985 33(2017), 3, Seite 397 (DE-627)168400413 (DE-600)742151-5 (DE-576)252416953 0266-0032 nnns volume:33 year:2017 number:3 pages:397 http://dx.doi.org/10.1111/sum.12347 Volltext https://search.proquest.com/docview/1935970081 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_22 GBV_ILN_2026 GBV_ILN_2346 38.60 AVZ 48.32 AVZ AR 33 2017 3 397 |
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10.1111/sum.12347 doi PQ20171228 (DE-627)OLC1998019349 (DE-599)GBVOLC1998019349 (PRQ)p571-a9ad2c5e909a64719415a568b84d64d7863129d63dc87b246409d25e28059e140 (KEY)0172628520170000033000300397errorbudgetfordigitalsoilmappingofcationexchangeca DE-627 ger DE-627 rakwb eng 630 640 DE-600 BIODIV fid 38.60 bkl 48.32 bkl J Huang verfasserin aut An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. Maximum likelihood estimates Fertility Structural stability Electrical resistivity Remote sensing Electromagnetic induction Models Soil Budgeting Computer simulation Soil mapping Electrical conductivity Cation exchange Measurement methods Digital mapping Prediction models Cation exchanging Error analysis Mapping Soil improvement Mathematical models Geostatistics Conductivity Simulation Spectrometry Preprocessing Gamma rays Digital cartography Budgets Spatial discrimination T F A Bishop oth J Triantafilis oth Enthalten in Soil use and management Oxford : Blackwell Publ., 1985 33(2017), 3, Seite 397 (DE-627)168400413 (DE-600)742151-5 (DE-576)252416953 0266-0032 nnns volume:33 year:2017 number:3 pages:397 http://dx.doi.org/10.1111/sum.12347 Volltext https://search.proquest.com/docview/1935970081 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_22 GBV_ILN_2026 GBV_ILN_2346 38.60 AVZ 48.32 AVZ AR 33 2017 3 397 |
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10.1111/sum.12347 doi PQ20171228 (DE-627)OLC1998019349 (DE-599)GBVOLC1998019349 (PRQ)p571-a9ad2c5e909a64719415a568b84d64d7863129d63dc87b246409d25e28059e140 (KEY)0172628520170000033000300397errorbudgetfordigitalsoilmappingofcationexchangeca DE-627 ger DE-627 rakwb eng 630 640 DE-600 BIODIV fid 38.60 bkl 48.32 bkl J Huang verfasserin aut An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. Maximum likelihood estimates Fertility Structural stability Electrical resistivity Remote sensing Electromagnetic induction Models Soil Budgeting Computer simulation Soil mapping Electrical conductivity Cation exchange Measurement methods Digital mapping Prediction models Cation exchanging Error analysis Mapping Soil improvement Mathematical models Geostatistics Conductivity Simulation Spectrometry Preprocessing Gamma rays Digital cartography Budgets Spatial discrimination T F A Bishop oth J Triantafilis oth Enthalten in Soil use and management Oxford : Blackwell Publ., 1985 33(2017), 3, Seite 397 (DE-627)168400413 (DE-600)742151-5 (DE-576)252416953 0266-0032 nnns volume:33 year:2017 number:3 pages:397 http://dx.doi.org/10.1111/sum.12347 Volltext https://search.proquest.com/docview/1935970081 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_22 GBV_ILN_2026 GBV_ILN_2346 38.60 AVZ 48.32 AVZ AR 33 2017 3 397 |
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10.1111/sum.12347 doi PQ20171228 (DE-627)OLC1998019349 (DE-599)GBVOLC1998019349 (PRQ)p571-a9ad2c5e909a64719415a568b84d64d7863129d63dc87b246409d25e28059e140 (KEY)0172628520170000033000300397errorbudgetfordigitalsoilmappingofcationexchangeca DE-627 ger DE-627 rakwb eng 630 640 DE-600 BIODIV fid 38.60 bkl 48.32 bkl J Huang verfasserin aut An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. Maximum likelihood estimates Fertility Structural stability Electrical resistivity Remote sensing Electromagnetic induction Models Soil Budgeting Computer simulation Soil mapping Electrical conductivity Cation exchange Measurement methods Digital mapping Prediction models Cation exchanging Error analysis Mapping Soil improvement Mathematical models Geostatistics Conductivity Simulation Spectrometry Preprocessing Gamma rays Digital cartography Budgets Spatial discrimination T F A Bishop oth J Triantafilis oth Enthalten in Soil use and management Oxford : Blackwell Publ., 1985 33(2017), 3, Seite 397 (DE-627)168400413 (DE-600)742151-5 (DE-576)252416953 0266-0032 nnns volume:33 year:2017 number:3 pages:397 http://dx.doi.org/10.1111/sum.12347 Volltext https://search.proquest.com/docview/1935970081 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_22 GBV_ILN_2026 GBV_ILN_2346 38.60 AVZ 48.32 AVZ AR 33 2017 3 397 |
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10.1111/sum.12347 doi PQ20171228 (DE-627)OLC1998019349 (DE-599)GBVOLC1998019349 (PRQ)p571-a9ad2c5e909a64719415a568b84d64d7863129d63dc87b246409d25e28059e140 (KEY)0172628520170000033000300397errorbudgetfordigitalsoilmappingofcationexchangeca DE-627 ger DE-627 rakwb eng 630 640 DE-600 BIODIV fid 38.60 bkl 48.32 bkl J Huang verfasserin aut An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. Maximum likelihood estimates Fertility Structural stability Electrical resistivity Remote sensing Electromagnetic induction Models Soil Budgeting Computer simulation Soil mapping Electrical conductivity Cation exchange Measurement methods Digital mapping Prediction models Cation exchanging Error analysis Mapping Soil improvement Mathematical models Geostatistics Conductivity Simulation Spectrometry Preprocessing Gamma rays Digital cartography Budgets Spatial discrimination T F A Bishop oth J Triantafilis oth Enthalten in Soil use and management Oxford : Blackwell Publ., 1985 33(2017), 3, Seite 397 (DE-627)168400413 (DE-600)742151-5 (DE-576)252416953 0266-0032 nnns volume:33 year:2017 number:3 pages:397 http://dx.doi.org/10.1111/sum.12347 Volltext https://search.proquest.com/docview/1935970081 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_22 GBV_ILN_2026 GBV_ILN_2346 38.60 AVZ 48.32 AVZ AR 33 2017 3 397 |
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Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. 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J Huang ddc 630 fid BIODIV bkl 38.60 bkl 48.32 misc Maximum likelihood estimates misc Fertility misc Structural stability misc Electrical resistivity misc Remote sensing misc Electromagnetic induction misc Models misc Soil misc Budgeting misc Computer simulation misc Soil mapping misc Electrical conductivity misc Cation exchange misc Measurement methods misc Digital mapping misc Prediction models misc Cation exchanging misc Error analysis misc Mapping misc Soil improvement misc Mathematical models misc Geostatistics misc Conductivity misc Simulation misc Spectrometry misc Preprocessing misc Gamma rays misc Digital cartography misc Budgets misc Spatial discrimination An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data |
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630 640 DE-600 BIODIV fid 38.60 bkl 48.32 bkl An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data Maximum likelihood estimates Fertility Structural stability Electrical resistivity Remote sensing Electromagnetic induction Models Soil Budgeting Computer simulation Soil mapping Electrical conductivity Cation exchange Measurement methods Digital mapping Prediction models Cation exchanging Error analysis Mapping Soil improvement Mathematical models Geostatistics Conductivity Simulation Spectrometry Preprocessing Gamma rays Digital cartography Budgets Spatial discrimination |
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error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data |
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An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data |
| abstract |
The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. |
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The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. |
| abstract_unstemmed |
The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray ([gamma]-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the [gamma]-ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the [gamma]-ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries. |
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An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed [gamma]-ray spectrometer data |
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