Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021

Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in th...
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Autor*in:	Yang, Haoxuan [verfasserIn] Wang, Qunming [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling

Übergeordnetes Werk:	Enthalten in: Journal of hydrology - Amsterdam [u.a.] : Elsevier, 1963, 621
Übergeordnetes Werk:	volume:621

DOI / URN:	10.1016/j.jhydrol.2023.129579

Katalog-ID:	ELV010020799

Internformat


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245	1	0	\|a Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021
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520			\|a Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields.
650		4	\|a Surface soil moisture (SSM)
650		4	\|a Soil Moisture Active Passive (SMAP)
650		4	\|a Data reconstruction
650		4	\|a Gap-filling
700	1		\|a Wang, Qunming \|e verfasserin \|4 aut
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allfields	10.1016/j.jhydrol.2023.129579 doi (DE-627)ELV010020799 (ELSEVIER)S0022-1694(23)00521-8 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Yang, Haoxuan verfasserin (orcid)0000-0001-7389-1447 aut Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields. Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling Wang, Qunming verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 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_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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
spelling	10.1016/j.jhydrol.2023.129579 doi (DE-627)ELV010020799 (ELSEVIER)S0022-1694(23)00521-8 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Yang, Haoxuan verfasserin (orcid)0000-0001-7389-1447 aut Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields. Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling Wang, Qunming verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 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_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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
allfields_unstemmed	10.1016/j.jhydrol.2023.129579 doi (DE-627)ELV010020799 (ELSEVIER)S0022-1694(23)00521-8 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Yang, Haoxuan verfasserin (orcid)0000-0001-7389-1447 aut Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields. Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling Wang, Qunming verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 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_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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
allfieldsGer	10.1016/j.jhydrol.2023.129579 doi (DE-627)ELV010020799 (ELSEVIER)S0022-1694(23)00521-8 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Yang, Haoxuan verfasserin (orcid)0000-0001-7389-1447 aut Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields. Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling Wang, Qunming verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 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_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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
allfieldsSound	10.1016/j.jhydrol.2023.129579 doi (DE-627)ELV010020799 (ELSEVIER)S0022-1694(23)00521-8 DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Yang, Haoxuan verfasserin (orcid)0000-0001-7389-1447 aut Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields. Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling Wang, Qunming verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 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_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_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
language	English
source	Enthalten in Journal of hydrology 621 volume:621
sourceStr	Enthalten in Journal of hydrology 621 volume:621
format_phy_str_mv	Article
bklname	Hydrologie: Allgemeines
institution	findex.gbv.de
topic_facet	Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling
dewey-raw	690
isfreeaccess_bool	false
container_title	Journal of hydrology
authorswithroles_txt_mv	Yang, Haoxuan @@aut@@ Wang, Qunming @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	268761817
dewey-sort	3690
id	ELV010020799
language_de	englisch
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author	Yang, Haoxuan
spellingShingle	Yang, Haoxuan ddc 690 bkl 38.85 misc Surface soil moisture (SSM) misc Soil Moisture Active Passive (SMAP) misc Data reconstruction misc Gap-filling Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021
authorStr	Yang, Haoxuan
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topic_title	690 VZ 38.85 bkl Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 Surface soil moisture (SSM) Soil Moisture Active Passive (SMAP) Data reconstruction Gap-filling
topic	ddc 690 bkl 38.85 misc Surface soil moisture (SSM) misc Soil Moisture Active Passive (SMAP) misc Data reconstruction misc Gap-filling
topic_unstemmed	ddc 690 bkl 38.85 misc Surface soil moisture (SSM) misc Soil Moisture Active Passive (SMAP) misc Data reconstruction misc Gap-filling
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title	Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021
ctrlnum	(DE-627)ELV010020799 (ELSEVIER)S0022-1694(23)00521-8
title_full	Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021
author_sort	Yang, Haoxuan
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author_browse	Yang, Haoxuan Wang, Qunming
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title_sort	reconstruction of a spatially seamless, daily smap (ssd_smap) surface soil moisture dataset from 2015 to 2021
title_auth	Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021
abstract	Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields.
abstractGer	Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields.
abstract_unstemmed	Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields.
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title_short	Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021
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doi_str	10.1016/j.jhydrol.2023.129579
up_date	2024-07-06T16:32:46.880Z
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Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021

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