Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters

Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared a...
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Autor*in:	Meng, Xiangtian [verfasserIn] Bao, Yilin [verfasserIn] Zhang, Xinle [verfasserIn] Wang, Xiang [verfasserIn] Liu, Huanjun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters

Übergeordnetes Werk:	Enthalten in: Geoderma - Amsterdam [u.a.] : Elsevier Science, 1967, 411
Übergeordnetes Werk:	volume:411

DOI / URN:	10.1016/j.geoderma.2022.115696

Katalog-ID:	ELV007300603

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245	1	0	\|a Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
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520			\|a Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model.
650		4	\|a Soil organic matter
650		4	\|a Stratification strategy
650		4	\|a Soil hierarchical
650		4	\|a Decision tree
650		4	\|a Density peak clustering
650		4	\|a Spectral characteristic parameters
700	1		\|a Bao, Yilin \|e verfasserin \|4 aut
700	1		\|a Zhang, Xinle \|e verfasserin \|4 aut
700	1		\|a Wang, Xiang \|e verfasserin \|4 aut
700	1		\|a Liu, Huanjun \|e verfasserin \|4 aut
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912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
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912			\|a GBV_ILN_370
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publishDate	2022
allfields	10.1016/j.geoderma.2022.115696 doi (DE-627)ELV007300603 (ELSEVIER)S0016-7061(22)00003-9 DE-627 ger DE-627 rda eng 550 910 VZ 38.60 bkl Meng, Xiangtian verfasserin aut Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model. Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters Bao, Yilin verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Liu, Huanjun verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 411 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:411 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften VZ AR 411
spelling	10.1016/j.geoderma.2022.115696 doi (DE-627)ELV007300603 (ELSEVIER)S0016-7061(22)00003-9 DE-627 ger DE-627 rda eng 550 910 VZ 38.60 bkl Meng, Xiangtian verfasserin aut Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model. Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters Bao, Yilin verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Liu, Huanjun verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 411 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:411 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften VZ AR 411
allfields_unstemmed	10.1016/j.geoderma.2022.115696 doi (DE-627)ELV007300603 (ELSEVIER)S0016-7061(22)00003-9 DE-627 ger DE-627 rda eng 550 910 VZ 38.60 bkl Meng, Xiangtian verfasserin aut Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model. Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters Bao, Yilin verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Liu, Huanjun verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 411 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:411 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften VZ AR 411
allfieldsGer	10.1016/j.geoderma.2022.115696 doi (DE-627)ELV007300603 (ELSEVIER)S0016-7061(22)00003-9 DE-627 ger DE-627 rda eng 550 910 VZ 38.60 bkl Meng, Xiangtian verfasserin aut Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model. Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters Bao, Yilin verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Liu, Huanjun verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 411 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:411 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften VZ AR 411
allfieldsSound	10.1016/j.geoderma.2022.115696 doi (DE-627)ELV007300603 (ELSEVIER)S0016-7061(22)00003-9 DE-627 ger DE-627 rda eng 550 910 VZ 38.60 bkl Meng, Xiangtian verfasserin aut Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model. Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters Bao, Yilin verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Liu, Huanjun verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 411 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:411 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.60 Bodenkunde: Allgemeines Geowissenschaften VZ AR 411
language	English
source	Enthalten in Geoderma 411 volume:411
sourceStr	Enthalten in Geoderma 411 volume:411
format_phy_str_mv	Article
bklname	Bodenkunde: Allgemeines
institution	findex.gbv.de
topic_facet	Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters
dewey-raw	550
isfreeaccess_bool	false
container_title	Geoderma
authorswithroles_txt_mv	Meng, Xiangtian @@aut@@ Bao, Yilin @@aut@@ Zhang, Xinle @@aut@@ Wang, Xiang @@aut@@ Liu, Huanjun @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320414493
dewey-sort	3550
id	ELV007300603
language_de	englisch
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author	Meng, Xiangtian
spellingShingle	Meng, Xiangtian ddc 550 bkl 38.60 misc Soil organic matter misc Stratification strategy misc Soil hierarchical misc Decision tree misc Density peak clustering misc Spectral characteristic parameters Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
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topic_title	550 910 VZ 38.60 bkl Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters Soil organic matter Stratification strategy Soil hierarchical Decision tree Density peak clustering Spectral characteristic parameters
topic	ddc 550 bkl 38.60 misc Soil organic matter misc Stratification strategy misc Soil hierarchical misc Decision tree misc Density peak clustering misc Spectral characteristic parameters
topic_unstemmed	ddc 550 bkl 38.60 misc Soil organic matter misc Stratification strategy misc Soil hierarchical misc Decision tree misc Density peak clustering misc Spectral characteristic parameters
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title	Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
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title_full	Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
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author_browse	Meng, Xiangtian Bao, Yilin Zhang, Xinle Wang, Xiang Liu, Huanjun
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author2-role	verfasserin
title_sort	prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
title_auth	Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
abstract	Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model.
abstractGer	Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model.
abstract_unstemmed	Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model.
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title_short	Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters
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author2	Bao, Yilin Zhang, Xinle Wang, Xiang Liu, Huanjun
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doi_str	10.1016/j.geoderma.2022.115696
up_date	2024-07-07T00:17:09.624Z
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We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 – 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg−1 and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg−1 and R2 increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Soil organic matter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stratification strategy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Soil hierarchical</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision tree</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Density peak clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spectral characteristic parameters</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield 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Prediction of soil organic matter using different soil classification hierarchical level stratification strategies and spectral characteristic parameters

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