A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images
Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Meng, Xiangtian [verfasserIn] Bao, Yilin [verfasserIn] Liu, Huanjun [verfasserIn] Zhang, Xinle [verfasserIn] Wang, Xiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Geoderma - Amsterdam [u.a.] : Elsevier Science, 1967, 425 |
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| Übergeordnetes Werk: |
volume:425 |
| DOI / URN: |
10.1016/j.geoderma.2022.116065 |
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ELV00832428X |
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| 245 | 1 | 0 | |a A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images |
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| 520 | |a Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. | ||
| 650 | 4 | |a Hyperspectral data | |
| 650 | 4 | |a Multi-temporal | |
| 650 | 4 | |a Data fusion | |
| 650 | 4 | |a Soil classification | |
| 650 | 4 | |a Soil class | |
| 650 | 4 | |a Mapping | |
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| 700 | 1 | |a Liu, Huanjun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xinle |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiang |e verfasserin |4 aut | |
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10.1016/j.geoderma.2022.116065 doi (DE-627)ELV00832428X (ELSEVIER)S0016-7061(22)00372-X DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Meng, Xiangtian verfasserin aut A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. Hyperspectral data Multi-temporal Data fusion Soil classification Soil class Mapping Bao, Yilin verfasserin aut Liu, Huanjun verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 425 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:425 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 425 |
| spelling |
10.1016/j.geoderma.2022.116065 doi (DE-627)ELV00832428X (ELSEVIER)S0016-7061(22)00372-X DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Meng, Xiangtian verfasserin aut A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. Hyperspectral data Multi-temporal Data fusion Soil classification Soil class Mapping Bao, Yilin verfasserin aut Liu, Huanjun verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 425 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:425 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 425 |
| allfields_unstemmed |
10.1016/j.geoderma.2022.116065 doi (DE-627)ELV00832428X (ELSEVIER)S0016-7061(22)00372-X DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Meng, Xiangtian verfasserin aut A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. Hyperspectral data Multi-temporal Data fusion Soil classification Soil class Mapping Bao, Yilin verfasserin aut Liu, Huanjun verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 425 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:425 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 425 |
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10.1016/j.geoderma.2022.116065 doi (DE-627)ELV00832428X (ELSEVIER)S0016-7061(22)00372-X DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Meng, Xiangtian verfasserin aut A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. Hyperspectral data Multi-temporal Data fusion Soil classification Soil class Mapping Bao, Yilin verfasserin aut Liu, Huanjun verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 425 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:425 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 425 |
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10.1016/j.geoderma.2022.116065 doi (DE-627)ELV00832428X (ELSEVIER)S0016-7061(22)00372-X DE-627 ger DE-627 rda eng 550 910 DE-600 38.60 bkl Meng, Xiangtian verfasserin aut A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. Hyperspectral data Multi-temporal Data fusion Soil classification Soil class Mapping Bao, Yilin verfasserin aut Liu, Huanjun verfasserin aut Zhang, Xinle verfasserin aut Wang, Xiang verfasserin aut Enthalten in Geoderma Amsterdam [u.a.] : Elsevier Science, 1967 425 Online-Ressource (DE-627)320414493 (DE-600)2001729-7 (DE-576)099603853 1872-6259 nnns volume:425 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 AR 425 |
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Meng, Xiangtian @@aut@@ Bao, Yilin @@aut@@ Liu, Huanjun @@aut@@ Zhang, Xinle @@aut@@ Wang, Xiang @@aut@@ |
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Meng, Xiangtian |
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Meng, Xiangtian ddc 550 bkl 38.60 misc Hyperspectral data misc Multi-temporal misc Data fusion misc Soil classification misc Soil class misc Mapping A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images |
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550 910 DE-600 38.60 bkl A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images Hyperspectral data Multi-temporal Data fusion Soil classification Soil class Mapping |
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ddc 550 bkl 38.60 misc Hyperspectral data misc Multi-temporal misc Data fusion misc Soil classification misc Soil class misc Mapping |
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Meng, Xiangtian Bao, Yilin Liu, Huanjun Zhang, Xinle Wang, Xiang |
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a new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images |
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A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images |
| abstract |
Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. |
| abstractGer |
Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. |
| abstract_unstemmed |
Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research. |
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A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images |
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| score |
7.401354 |