Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics
Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples fro...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Xiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Corticosterone response by - Veitch, Jasmine S.M. ELSEVIER, 2020, an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:216 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.still.2021.105241 |
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ELV056037171 |
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| 245 | 1 | 0 | |a Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics |
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| 520 | |a Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. | ||
| 520 | |a Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. | ||
| 650 | 7 | |a Spectral feature parameters |2 Elsevier | |
| 650 | 7 | |a Soil organic matter |2 Elsevier | |
| 650 | 7 | |a K-means clustering |2 Elsevier | |
| 650 | 7 | |a Spectral reflectance curves |2 Elsevier | |
| 700 | 1 | |a Li, Lin |4 oth | |
| 700 | 1 | |a Liu, Huanjun |4 oth | |
| 700 | 1 | |a Song, Kaishan |4 oth | |
| 700 | 1 | |a Wang, Liping |4 oth | |
| 700 | 1 | |a Meng, Xiangtian |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Veitch, Jasmine S.M. ELSEVIER |t Corticosterone response by |d 2020 |d an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment |g Amsterdam [u.a.] |w (DE-627)ELV005168384 |
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10.1016/j.still.2021.105241 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001693.pica (DE-627)ELV056037171 (ELSEVIER)S0167-1987(21)00314-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.89 bkl Wang, Xiang verfasserin aut Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Spectral feature parameters Elsevier Soil organic matter Elsevier K-means clustering Elsevier Spectral reflectance curves Elsevier Li, Lin oth Liu, Huanjun oth Song, Kaishan oth Wang, Liping oth Meng, Xiangtian oth Enthalten in Elsevier Science Veitch, Jasmine S.M. ELSEVIER Corticosterone response by 2020 an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment Amsterdam [u.a.] (DE-627)ELV005168384 volume:216 year:2022 pages:0 https://doi.org/10.1016/j.still.2021.105241 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.89 Endokrinologie VZ AR 216 2022 0 |
| spelling |
10.1016/j.still.2021.105241 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001693.pica (DE-627)ELV056037171 (ELSEVIER)S0167-1987(21)00314-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.89 bkl Wang, Xiang verfasserin aut Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Spectral feature parameters Elsevier Soil organic matter Elsevier K-means clustering Elsevier Spectral reflectance curves Elsevier Li, Lin oth Liu, Huanjun oth Song, Kaishan oth Wang, Liping oth Meng, Xiangtian oth Enthalten in Elsevier Science Veitch, Jasmine S.M. ELSEVIER Corticosterone response by 2020 an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment Amsterdam [u.a.] (DE-627)ELV005168384 volume:216 year:2022 pages:0 https://doi.org/10.1016/j.still.2021.105241 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.89 Endokrinologie VZ AR 216 2022 0 |
| allfields_unstemmed |
10.1016/j.still.2021.105241 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001693.pica (DE-627)ELV056037171 (ELSEVIER)S0167-1987(21)00314-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.89 bkl Wang, Xiang verfasserin aut Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Spectral feature parameters Elsevier Soil organic matter Elsevier K-means clustering Elsevier Spectral reflectance curves Elsevier Li, Lin oth Liu, Huanjun oth Song, Kaishan oth Wang, Liping oth Meng, Xiangtian oth Enthalten in Elsevier Science Veitch, Jasmine S.M. ELSEVIER Corticosterone response by 2020 an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment Amsterdam [u.a.] (DE-627)ELV005168384 volume:216 year:2022 pages:0 https://doi.org/10.1016/j.still.2021.105241 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.89 Endokrinologie VZ AR 216 2022 0 |
| allfieldsGer |
10.1016/j.still.2021.105241 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001693.pica (DE-627)ELV056037171 (ELSEVIER)S0167-1987(21)00314-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.89 bkl Wang, Xiang verfasserin aut Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Spectral feature parameters Elsevier Soil organic matter Elsevier K-means clustering Elsevier Spectral reflectance curves Elsevier Li, Lin oth Liu, Huanjun oth Song, Kaishan oth Wang, Liping oth Meng, Xiangtian oth Enthalten in Elsevier Science Veitch, Jasmine S.M. ELSEVIER Corticosterone response by 2020 an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment Amsterdam [u.a.] (DE-627)ELV005168384 volume:216 year:2022 pages:0 https://doi.org/10.1016/j.still.2021.105241 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.89 Endokrinologie VZ AR 216 2022 0 |
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10.1016/j.still.2021.105241 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001693.pica (DE-627)ELV056037171 (ELSEVIER)S0167-1987(21)00314-7 DE-627 ger DE-627 rakwb eng 610 VZ 44.89 bkl Wang, Xiang verfasserin aut Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. Spectral feature parameters Elsevier Soil organic matter Elsevier K-means clustering Elsevier Spectral reflectance curves Elsevier Li, Lin oth Liu, Huanjun oth Song, Kaishan oth Wang, Liping oth Meng, Xiangtian oth Enthalten in Elsevier Science Veitch, Jasmine S.M. ELSEVIER Corticosterone response by 2020 an international journal on research and development in soil tillage and field traffic, and their relationship with land use, crop production and the environment Amsterdam [u.a.] (DE-627)ELV005168384 volume:216 year:2022 pages:0 https://doi.org/10.1016/j.still.2021.105241 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.89 Endokrinologie VZ AR 216 2022 0 |
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Prediction of soil organic matter using VNIR spectral parameters extracted from shape characteristics |
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Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. |
| abstractGer |
Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. |
| abstract_unstemmed |
Whether spectral feature parameters (SFPs) can be effectively used to predict soil organic matter (SOM), and its spatial transferability was tested for different regions. In this study, a hybrid model was proposed based on SFPs and local regression method. The topsoil spectra of 221 soil samples from Sanjiang Plain and 187 soil samples from Nong’an county in Northeast China were re-sampled (at 0.01 µm intervals) and converted to the first-derivative curves and CR curves. Two SFPs were extracted on reflectance curves (RSFPs), which were defined as curve length (Lc) and area (Ac) between two absorption positions. Local random forest models with k-means clustering were built using the RSFPs and first-derivative spectra for evaluating the feasibility of RSFPs in these two study areas. After analyzing the results of Chinese soil samples, this method was also applied to Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database in order to evaluate the feasibility of RSFPs outside China. Our results revealed these: (1) high correlation between the RSFPs of soil samples with spectral characteristics of sandy soils and SOM; (2) the importance of Lc relative to Ac in SOM prediction is higher; and (3) SOM prediction using RSFPs is comparable to the first-derivative spectra, and the best result has an R2 of 0.76 and an RMSE of 7.43 g kg−1. Our results suggest that RSFPs can be used to predict SOM and have good spatial transferability after applying in two study areas. RSFPs with specific geometric meaning have high potential to predict other soil properties in Northeast China. Our results also provide a reference for SOM mapping using hyper-spectral satellites. |
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