Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands
Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate chan...
Ausführliche Beschreibung
Autor*in: |
Shaohua Wang [verfasserIn] Gang Fu [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Frontiers in Environmental Science - Frontiers Media S.A., 2014, 11(2023) |
---|---|
Übergeordnetes Werk: |
volume:11 ; year:2023 |
Links: |
---|
DOI / URN: |
10.3389/fenvs.2023.1130448 |
---|
Katalog-ID: |
DOAJ080704530 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ080704530 | ||
003 | DE-627 | ||
005 | 20230502083924.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230310s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3389/fenvs.2023.1130448 |2 doi | |
035 | |a (DE-627)DOAJ080704530 | ||
035 | |a (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a GE1-350 | |
100 | 0 | |a Shaohua Wang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. | ||
650 | 4 | |a soil quality | |
650 | 4 | |a global change | |
650 | 4 | |a random forest | |
650 | 4 | |a alpine ecosystem | |
650 | 4 | |a alpine region | |
650 | 4 | |a ‘third pole’ | |
653 | 0 | |a Environmental sciences | |
700 | 0 | |a Gang Fu |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Frontiers in Environmental Science |d Frontiers Media S.A., 2014 |g 11(2023) |w (DE-627)771401604 |w (DE-600)2741535-1 |x 2296665X |7 nnns |
773 | 1 | 8 | |g volume:11 |g year:2023 |
856 | 4 | 0 | |u https://doi.org/10.3389/fenvs.2023.1130448 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 |z kostenfrei |
856 | 4 | 0 | |u https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2296-665X |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_11 | ||
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_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
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_2003 | ||
912 | |a GBV_ILN_2014 | ||
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_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 11 |j 2023 |
author_variant |
s w sw g f gf |
---|---|
matchkey_str |
article:2296665X:2023----::oelnsimitruiglmtdtadomlzdifrneeeainnebsdni |
hierarchy_sort_str |
2023 |
callnumber-subject-code |
GE |
publishDate |
2023 |
allfields |
10.3389/fenvs.2023.1130448 doi (DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 DE-627 ger DE-627 rakwb eng GE1-350 Shaohua Wang verfasserin aut Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences Gang Fu verfasserin aut In Frontiers in Environmental Science Frontiers Media S.A., 2014 11(2023) (DE-627)771401604 (DE-600)2741535-1 2296665X nnns volume:11 year:2023 https://doi.org/10.3389/fenvs.2023.1130448 kostenfrei https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 kostenfrei https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full kostenfrei https://doaj.org/toc/2296-665X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 |
spelling |
10.3389/fenvs.2023.1130448 doi (DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 DE-627 ger DE-627 rakwb eng GE1-350 Shaohua Wang verfasserin aut Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences Gang Fu verfasserin aut In Frontiers in Environmental Science Frontiers Media S.A., 2014 11(2023) (DE-627)771401604 (DE-600)2741535-1 2296665X nnns volume:11 year:2023 https://doi.org/10.3389/fenvs.2023.1130448 kostenfrei https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 kostenfrei https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full kostenfrei https://doaj.org/toc/2296-665X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 |
allfields_unstemmed |
10.3389/fenvs.2023.1130448 doi (DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 DE-627 ger DE-627 rakwb eng GE1-350 Shaohua Wang verfasserin aut Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences Gang Fu verfasserin aut In Frontiers in Environmental Science Frontiers Media S.A., 2014 11(2023) (DE-627)771401604 (DE-600)2741535-1 2296665X nnns volume:11 year:2023 https://doi.org/10.3389/fenvs.2023.1130448 kostenfrei https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 kostenfrei https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full kostenfrei https://doaj.org/toc/2296-665X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 |
allfieldsGer |
10.3389/fenvs.2023.1130448 doi (DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 DE-627 ger DE-627 rakwb eng GE1-350 Shaohua Wang verfasserin aut Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences Gang Fu verfasserin aut In Frontiers in Environmental Science Frontiers Media S.A., 2014 11(2023) (DE-627)771401604 (DE-600)2741535-1 2296665X nnns volume:11 year:2023 https://doi.org/10.3389/fenvs.2023.1130448 kostenfrei https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 kostenfrei https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full kostenfrei https://doaj.org/toc/2296-665X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 |
allfieldsSound |
10.3389/fenvs.2023.1130448 doi (DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 DE-627 ger DE-627 rakwb eng GE1-350 Shaohua Wang verfasserin aut Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences Gang Fu verfasserin aut In Frontiers in Environmental Science Frontiers Media S.A., 2014 11(2023) (DE-627)771401604 (DE-600)2741535-1 2296665X nnns volume:11 year:2023 https://doi.org/10.3389/fenvs.2023.1130448 kostenfrei https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 kostenfrei https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full kostenfrei https://doaj.org/toc/2296-665X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 |
language |
English |
source |
In Frontiers in Environmental Science 11(2023) volume:11 year:2023 |
sourceStr |
In Frontiers in Environmental Science 11(2023) volume:11 year:2023 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
soil quality global change random forest alpine ecosystem alpine region ‘third pole’ Environmental sciences |
isfreeaccess_bool |
true |
container_title |
Frontiers in Environmental Science |
authorswithroles_txt_mv |
Shaohua Wang @@aut@@ Gang Fu @@aut@@ |
publishDateDaySort_date |
2023-01-01T00:00:00Z |
hierarchy_top_id |
771401604 |
id |
DOAJ080704530 |
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">DOAJ080704530</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502083924.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fenvs.2023.1130448</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ080704530</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17</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="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Shaohua Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">soil quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">global change</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">random forest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">alpine ecosystem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">alpine region</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">‘third pole’</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gang Fu</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">Frontiers in Environmental Science</subfield><subfield code="d">Frontiers Media S.A., 2014</subfield><subfield code="g">11(2023)</subfield><subfield code="w">(DE-627)771401604</subfield><subfield code="w">(DE-600)2741535-1</subfield><subfield code="x">2296665X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fenvs.2023.1130448</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2296-665X</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_11</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_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_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_2003</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_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_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_4367</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">11</subfield><subfield code="j">2023</subfield></datafield></record></collection>
|
callnumber-first |
G - Geography, Anthropology, Recreation |
author |
Shaohua Wang |
spellingShingle |
Shaohua Wang misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
authorStr |
Shaohua Wang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)771401604 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
GE1-350 |
illustrated |
Not Illustrated |
issn |
2296665X |
topic_title |
GE1-350 Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands soil quality global change random forest alpine ecosystem alpine region ‘third pole’ |
topic |
misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences |
topic_unstemmed |
misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences |
topic_browse |
misc GE1-350 misc soil quality misc global change misc random forest misc alpine ecosystem misc alpine region misc ‘third pole’ misc Environmental sciences |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Frontiers in Environmental Science |
hierarchy_parent_id |
771401604 |
hierarchy_top_title |
Frontiers in Environmental Science |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)771401604 (DE-600)2741535-1 |
title |
Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
ctrlnum |
(DE-627)DOAJ080704530 (DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17 |
title_full |
Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
author_sort |
Shaohua Wang |
journal |
Frontiers in Environmental Science |
journalStr |
Frontiers in Environmental Science |
callnumber-first-code |
G |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
author_browse |
Shaohua Wang Gang Fu |
container_volume |
11 |
class |
GE1-350 |
format_se |
Elektronische Aufsätze |
author-letter |
Shaohua Wang |
doi_str_mv |
10.3389/fenvs.2023.1130448 |
author2-role |
verfasserin |
title_sort |
modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
callnumber |
GE1-350 |
title_auth |
Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
abstract |
Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. |
abstractGer |
Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. |
abstract_unstemmed |
Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 |
title_short |
Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands |
url |
https://doi.org/10.3389/fenvs.2023.1130448 https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17 https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full https://doaj.org/toc/2296-665X |
remote_bool |
true |
author2 |
Gang Fu |
author2Str |
Gang Fu |
ppnlink |
771401604 |
callnumber-subject |
GE - Environmental Sciences |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3389/fenvs.2023.1130448 |
callnumber-a |
GE1-350 |
up_date |
2024-07-03T16:08:05.446Z |
_version_ |
1803574717134667776 |
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">DOAJ080704530</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502083924.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fenvs.2023.1130448</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ080704530</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2bbe5774c2394e5d86d88dfc8a0dac17</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="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Shaohua Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">soil quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">global change</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">random forest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">alpine ecosystem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">alpine region</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">‘third pole’</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gang Fu</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">Frontiers in Environmental Science</subfield><subfield code="d">Frontiers Media S.A., 2014</subfield><subfield code="g">11(2023)</subfield><subfield code="w">(DE-627)771401604</subfield><subfield code="w">(DE-600)2741535-1</subfield><subfield code="x">2296665X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fenvs.2023.1130448</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/2bbe5774c2394e5d86d88dfc8a0dac17</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fenvs.2023.1130448/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2296-665X</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_11</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_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_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_2003</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_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_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_4367</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">11</subfield><subfield code="j">2023</subfield></datafield></record></collection>
|
score |
7.399314 |