NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia
To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined pro...
Ausführliche Beschreibung
Autor*in: |
Nengcheng Chen [verfasserIn] Yuqi He [verfasserIn] Xiang Zhang [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2017 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 9(2017), 1, p 51 |
---|---|
Übergeordnetes Werk: |
volume:9 ; year:2017 ; number:1, p 51 |
Links: |
---|
DOI / URN: |
10.3390/rs9010051 |
---|
Katalog-ID: |
DOAJ07581479X |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ07581479X | ||
003 | DE-627 | ||
005 | 20230503085049.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230228s2017 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/rs9010051 |2 doi | |
035 | |a (DE-627)DOAJ07581479X | ||
035 | |a (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 0 | |a Nengcheng Chen |e verfasserin |4 aut | |
245 | 1 | 0 | |a NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia |
264 | 1 | |c 2017 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. | ||
650 | 4 | |a disaggregation | |
650 | 4 | |a soil moisture | |
650 | 4 | |a NIR-red triangle space | |
650 | 4 | |a normalized soil moisture index (NSMI) | |
650 | 4 | |a soil moisture active passive (SMAP) | |
653 | 0 | |a Science | |
653 | 0 | |a Q | |
700 | 0 | |a Yuqi He |e verfasserin |4 aut | |
700 | 0 | |a Xiang Zhang |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Remote Sensing |d MDPI AG, 2009 |g 9(2017), 1, p 51 |w (DE-627)608937916 |w (DE-600)2513863-7 |x 20724292 |7 nnns |
773 | 1 | 8 | |g volume:9 |g year:2017 |g number:1, p 51 |
856 | 4 | 0 | |u https://doi.org/10.3390/rs9010051 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e |z kostenfrei |
856 | 4 | 0 | |u http://www.mdpi.com/2072-4292/9/1/51 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2072-4292 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2108 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2119 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4392 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 9 |j 2017 |e 1, p 51 |
author_variant |
n c nc y h yh x z xz |
---|---|
matchkey_str |
article:20724292:2017----::irdpcrbsdiageainfmpolosueo5meouinaeosae |
hierarchy_sort_str |
2017 |
publishDate |
2017 |
allfields |
10.3390/rs9010051 doi (DE-627)DOAJ07581479X (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e DE-627 ger DE-627 rakwb eng Nengcheng Chen verfasserin aut NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) Science Q Yuqi He verfasserin aut Xiang Zhang verfasserin aut In Remote Sensing MDPI AG, 2009 9(2017), 1, p 51 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:9 year:2017 number:1, p 51 https://doi.org/10.3390/rs9010051 kostenfrei https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e kostenfrei http://www.mdpi.com/2072-4292/9/1/51 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2017 1, p 51 |
spelling |
10.3390/rs9010051 doi (DE-627)DOAJ07581479X (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e DE-627 ger DE-627 rakwb eng Nengcheng Chen verfasserin aut NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) Science Q Yuqi He verfasserin aut Xiang Zhang verfasserin aut In Remote Sensing MDPI AG, 2009 9(2017), 1, p 51 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:9 year:2017 number:1, p 51 https://doi.org/10.3390/rs9010051 kostenfrei https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e kostenfrei http://www.mdpi.com/2072-4292/9/1/51 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2017 1, p 51 |
allfields_unstemmed |
10.3390/rs9010051 doi (DE-627)DOAJ07581479X (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e DE-627 ger DE-627 rakwb eng Nengcheng Chen verfasserin aut NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) Science Q Yuqi He verfasserin aut Xiang Zhang verfasserin aut In Remote Sensing MDPI AG, 2009 9(2017), 1, p 51 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:9 year:2017 number:1, p 51 https://doi.org/10.3390/rs9010051 kostenfrei https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e kostenfrei http://www.mdpi.com/2072-4292/9/1/51 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2017 1, p 51 |
allfieldsGer |
10.3390/rs9010051 doi (DE-627)DOAJ07581479X (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e DE-627 ger DE-627 rakwb eng Nengcheng Chen verfasserin aut NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) Science Q Yuqi He verfasserin aut Xiang Zhang verfasserin aut In Remote Sensing MDPI AG, 2009 9(2017), 1, p 51 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:9 year:2017 number:1, p 51 https://doi.org/10.3390/rs9010051 kostenfrei https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e kostenfrei http://www.mdpi.com/2072-4292/9/1/51 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2017 1, p 51 |
allfieldsSound |
10.3390/rs9010051 doi (DE-627)DOAJ07581479X (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e DE-627 ger DE-627 rakwb eng Nengcheng Chen verfasserin aut NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) Science Q Yuqi He verfasserin aut Xiang Zhang verfasserin aut In Remote Sensing MDPI AG, 2009 9(2017), 1, p 51 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:9 year:2017 number:1, p 51 https://doi.org/10.3390/rs9010051 kostenfrei https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e kostenfrei http://www.mdpi.com/2072-4292/9/1/51 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2017 1, p 51 |
language |
English |
source |
In Remote Sensing 9(2017), 1, p 51 volume:9 year:2017 number:1, p 51 |
sourceStr |
In Remote Sensing 9(2017), 1, p 51 volume:9 year:2017 number:1, p 51 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) Science Q |
isfreeaccess_bool |
true |
container_title |
Remote Sensing |
authorswithroles_txt_mv |
Nengcheng Chen @@aut@@ Yuqi He @@aut@@ Xiang Zhang @@aut@@ |
publishDateDaySort_date |
2017-01-01T00:00:00Z |
hierarchy_top_id |
608937916 |
id |
DOAJ07581479X |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ07581479X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503085049.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs9010051</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ07581479X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nengcheng Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">disaggregation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">soil moisture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">NIR-red triangle space</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">normalized soil moisture index (NSMI)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">soil moisture active passive (SMAP)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yuqi He</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiang Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">9(2017), 1, p 51</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1, p 51</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs9010051</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2072-4292/9/1/51</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2017</subfield><subfield code="e">1, p 51</subfield></datafield></record></collection>
|
author |
Nengcheng Chen |
spellingShingle |
Nengcheng Chen misc disaggregation misc soil moisture misc NIR-red triangle space misc normalized soil moisture index (NSMI) misc soil moisture active passive (SMAP) misc Science misc Q NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia |
authorStr |
Nengcheng Chen |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)608937916 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
DOAJ |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
20724292 |
topic_title |
NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia disaggregation soil moisture NIR-red triangle space normalized soil moisture index (NSMI) soil moisture active passive (SMAP) |
topic |
misc disaggregation misc soil moisture misc NIR-red triangle space misc normalized soil moisture index (NSMI) misc soil moisture active passive (SMAP) misc Science misc Q |
topic_unstemmed |
misc disaggregation misc soil moisture misc NIR-red triangle space misc normalized soil moisture index (NSMI) misc soil moisture active passive (SMAP) misc Science misc Q |
topic_browse |
misc disaggregation misc soil moisture misc NIR-red triangle space misc normalized soil moisture index (NSMI) misc soil moisture active passive (SMAP) misc Science misc Q |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Remote Sensing |
hierarchy_parent_id |
608937916 |
hierarchy_top_title |
Remote Sensing |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)608937916 (DE-600)2513863-7 |
title |
NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia |
ctrlnum |
(DE-627)DOAJ07581479X (DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e |
title_full |
NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia |
author_sort |
Nengcheng Chen |
journal |
Remote Sensing |
journalStr |
Remote Sensing |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2017 |
contenttype_str_mv |
txt |
author_browse |
Nengcheng Chen Yuqi He Xiang Zhang |
container_volume |
9 |
format_se |
Elektronische Aufsätze |
author-letter |
Nengcheng Chen |
doi_str_mv |
10.3390/rs9010051 |
author2-role |
verfasserin |
title_sort |
nir-red spectra-based disaggregation of smap soil moisture to 250 m resolution based on smapex-4/5 in southeastern australia |
title_auth |
NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia |
abstract |
To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. |
abstractGer |
To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. |
abstract_unstemmed |
To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 |
container_issue |
1, p 51 |
title_short |
NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia |
url |
https://doi.org/10.3390/rs9010051 https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e http://www.mdpi.com/2072-4292/9/1/51 https://doaj.org/toc/2072-4292 |
remote_bool |
true |
author2 |
Yuqi He Xiang Zhang |
author2Str |
Yuqi He Xiang Zhang |
ppnlink |
608937916 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/rs9010051 |
up_date |
2024-07-03T16:58:46.676Z |
_version_ |
1803577906094407680 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ07581479X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503085049.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs9010051</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ07581479X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5dc4bf4b82e9406bb64d9706c2a51e1e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nengcheng Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">disaggregation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">soil moisture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">NIR-red triangle space</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">normalized soil moisture index (NSMI)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">soil moisture active passive (SMAP)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yuqi He</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiang Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">9(2017), 1, p 51</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1, p 51</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs9010051</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5dc4bf4b82e9406bb64d9706c2a51e1e</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2072-4292/9/1/51</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2017</subfield><subfield code="e">1, p 51</subfield></datafield></record></collection>
|
score |
7.402011 |