Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin
Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of...
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Gespeichert in:
| Autor*in: |
Varpe, S. R. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Aerosol science and engineering - [Singapore] : Springer Singapore, 2017, 6(2022), 2 vom: 26. März, Seite 197-211 |
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| Übergeordnetes Werk: |
volume:6 ; year:2022 ; number:2 ; day:26 ; month:03 ; pages:197-211 |
| Links: |
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| DOI / URN: |
10.1007/s41810-022-00135-8 |
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SPR047238070 |
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| 100 | 1 | |a Varpe, S. R. |e verfasserin |0 (orcid)0000-0003-0791-3550 |4 aut | |
| 245 | 1 | 0 | |a Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin |
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| 520 | |a Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. | ||
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| 700 | 1 | |a Kolhe, A. R. |4 aut | |
| 700 | 1 | |a Singh, P. |4 aut | |
| 700 | 1 | |a Mahajan, C. M. |4 aut | |
| 700 | 1 | |a Kutal, G. C. |4 aut | |
| 700 | 1 | |a Patil, R. S. |4 aut | |
| 700 | 1 | |a Prasad, P. |4 aut | |
| 700 | 1 | |a Aher, G. R. |4 aut | |
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10.1007/s41810-022-00135-8 doi (DE-627)SPR047238070 (SPR)s41810-022-00135-8-e DE-627 ger DE-627 rakwb eng Varpe, S. R. verfasserin (orcid)0000-0003-0791-3550 aut Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. AOD (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 AERONET (dpeaa)DE-He213 DB (dpeaa)DE-He213 DT (dpeaa)DE-He213 DTB combined (dpeaa)DE-He213 Central IGP (dpeaa)DE-He213 Kolhe, A. R. aut Singh, P. aut Mahajan, C. M. aut Kutal, G. C. aut Patil, R. S. aut Prasad, P. aut Aher, G. R. aut Enthalten in Aerosol science and engineering [Singapore] : Springer Singapore, 2017 6(2022), 2 vom: 26. März, Seite 197-211 (DE-627)883913747 (DE-600)2890764-4 2510-3768 nnns volume:6 year:2022 number:2 day:26 month:03 pages:197-211 https://dx.doi.org/10.1007/s41810-022-00135-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2022 2 26 03 197-211 |
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10.1007/s41810-022-00135-8 doi (DE-627)SPR047238070 (SPR)s41810-022-00135-8-e DE-627 ger DE-627 rakwb eng Varpe, S. R. verfasserin (orcid)0000-0003-0791-3550 aut Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. AOD (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 AERONET (dpeaa)DE-He213 DB (dpeaa)DE-He213 DT (dpeaa)DE-He213 DTB combined (dpeaa)DE-He213 Central IGP (dpeaa)DE-He213 Kolhe, A. R. aut Singh, P. aut Mahajan, C. M. aut Kutal, G. C. aut Patil, R. S. aut Prasad, P. aut Aher, G. R. aut Enthalten in Aerosol science and engineering [Singapore] : Springer Singapore, 2017 6(2022), 2 vom: 26. März, Seite 197-211 (DE-627)883913747 (DE-600)2890764-4 2510-3768 nnns volume:6 year:2022 number:2 day:26 month:03 pages:197-211 https://dx.doi.org/10.1007/s41810-022-00135-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2022 2 26 03 197-211 |
| allfields_unstemmed |
10.1007/s41810-022-00135-8 doi (DE-627)SPR047238070 (SPR)s41810-022-00135-8-e DE-627 ger DE-627 rakwb eng Varpe, S. R. verfasserin (orcid)0000-0003-0791-3550 aut Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. AOD (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 AERONET (dpeaa)DE-He213 DB (dpeaa)DE-He213 DT (dpeaa)DE-He213 DTB combined (dpeaa)DE-He213 Central IGP (dpeaa)DE-He213 Kolhe, A. R. aut Singh, P. aut Mahajan, C. M. aut Kutal, G. C. aut Patil, R. S. aut Prasad, P. aut Aher, G. R. aut Enthalten in Aerosol science and engineering [Singapore] : Springer Singapore, 2017 6(2022), 2 vom: 26. März, Seite 197-211 (DE-627)883913747 (DE-600)2890764-4 2510-3768 nnns volume:6 year:2022 number:2 day:26 month:03 pages:197-211 https://dx.doi.org/10.1007/s41810-022-00135-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2022 2 26 03 197-211 |
| allfieldsGer |
10.1007/s41810-022-00135-8 doi (DE-627)SPR047238070 (SPR)s41810-022-00135-8-e DE-627 ger DE-627 rakwb eng Varpe, S. R. verfasserin (orcid)0000-0003-0791-3550 aut Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. AOD (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 AERONET (dpeaa)DE-He213 DB (dpeaa)DE-He213 DT (dpeaa)DE-He213 DTB combined (dpeaa)DE-He213 Central IGP (dpeaa)DE-He213 Kolhe, A. R. aut Singh, P. aut Mahajan, C. M. aut Kutal, G. C. aut Patil, R. S. aut Prasad, P. aut Aher, G. R. aut Enthalten in Aerosol science and engineering [Singapore] : Springer Singapore, 2017 6(2022), 2 vom: 26. März, Seite 197-211 (DE-627)883913747 (DE-600)2890764-4 2510-3768 nnns volume:6 year:2022 number:2 day:26 month:03 pages:197-211 https://dx.doi.org/10.1007/s41810-022-00135-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2022 2 26 03 197-211 |
| allfieldsSound |
10.1007/s41810-022-00135-8 doi (DE-627)SPR047238070 (SPR)s41810-022-00135-8-e DE-627 ger DE-627 rakwb eng Varpe, S. R. verfasserin (orcid)0000-0003-0791-3550 aut Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. AOD (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 AERONET (dpeaa)DE-He213 DB (dpeaa)DE-He213 DT (dpeaa)DE-He213 DTB combined (dpeaa)DE-He213 Central IGP (dpeaa)DE-He213 Kolhe, A. R. aut Singh, P. aut Mahajan, C. M. aut Kutal, G. C. aut Patil, R. S. aut Prasad, P. aut Aher, G. R. aut Enthalten in Aerosol science and engineering [Singapore] : Springer Singapore, 2017 6(2022), 2 vom: 26. März, Seite 197-211 (DE-627)883913747 (DE-600)2890764-4 2510-3768 nnns volume:6 year:2022 number:2 day:26 month:03 pages:197-211 https://dx.doi.org/10.1007/s41810-022-00135-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2022 2 26 03 197-211 |
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R.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0791-3550</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. 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Varpe, S. R. |
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Varpe, S. R. misc AOD misc MODIS misc AERONET misc DB misc DT misc DTB combined misc Central IGP Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin |
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Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin AOD (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 AERONET (dpeaa)DE-He213 DB (dpeaa)DE-He213 DT (dpeaa)DE-He213 DTB combined (dpeaa)DE-He213 Central IGP (dpeaa)DE-He213 |
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Varpe, S. R. Kolhe, A. R. Singh, P. Mahajan, C. M. Kutal, G. C. Patil, R. S. Prasad, P. Aher, G. R. |
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annual and inter-annual variability coupled with comparison of modis-aeronet retrieved aerosol optical depth over a rural site in the central indo-gangetic basin |
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Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin |
| abstract |
Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 |
| abstractGer |
Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 |
| abstract_unstemmed |
Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant. © The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022 |
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Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR047238070</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507202832.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220610s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41810-022-00135-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR047238070</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41810-022-00135-8-e</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="1" ind2=" "><subfield code="a">Varpe, S. R.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0791-3550</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The long-term (2000–2015) MODIS EOS-Terra/Aqua multi-algorithm (DT, DB and combined DTB) retrieved $ AOD_{550 nm} $ and AERONET measured $ AOD_{550 nm} $ data at Gandhi College (24.87° N, 84.19° E; 60 m amsl), a rural site in the central IGB, have been employed to assess the performance of MODIS AOD products against AERONET AOD and to examine their annual and inter-annual variability. For both MODIS Terra/Aqua sensors, the linear regression data statistics reveals that the values of slopes for MODIS Terra-EOS sensor lie in the range 1.04 ± 0.03 [DB (QA = 2,3)] to 1.11 ± 0.02 [DT (QA = (2,3)] which are slightly higher than 1. Also, similar observation is noticed for MODIS Aqua-EOS sensor for which slopes of linear regression fit span over 0.99 ± 0.03 [DB (QA = 2,3)] – 1.16 ± 0.03 [DT (QA = 3)]. The intercept, however, approach zero values for both MODIS Terra/AQUA–EOS sensor at DT (QA = 3), DT (QA = 2,3), DB (QA = 2,3) and DTB combined retrieval algorithms. The evaluation/performance analysis, therefore, exhibits the observed near-perfect match of MODIS Terra/Aqua-EOS sensors derived $ AOD_{550nm} $ from all algorithms with AERONET measured $ AOD_{550 nm} $ as a result of magnitudes of the slope and intercept of the linear regression fit to the scatter diagrams of MODIS $ AOD_{550} $ nm against AERONET $ AOD_{550 nm} $. Results, thus indicate that at Gandhi College, the DT, and DTB combined retrieval algorithms satisfactorily estimate AOD products which can then be used to build aerosol climatology over Gandhi College. The MODIS and AERONET derived $ AOD_{550 nm} $ values over the Gandhi College indicate a distinct annual pattern with maximum $ AOD_{550 nm} $ during the winter season and minimum $ AOD_{550 nm} $ during monsoon season and winter-summer transition period. Analysis revealed that the aerosol loading starts building up over the study region from March to June of the pre-monsoon season mainly due to the high convective activity and long-range mineral dust transport from western arid regions. An increase in AOD values during the post-monsoon and winter season is primarily due to the influx of aerosols from biomass burning processes and stable atmospheric conditions, the MODIS Terra retrieved $ AOD_{550 nm} $ using DT and combined DTB algorithm showed higher values in the month of July of monsoon season as compared to MODIS Aqua retrieved $ AOD_{550 nm} $. The inter-annual AOD trend analysis reveals that for MODIS Terra satellite there exits an increasing AOD trend during post-monsoon and winter seasons for DT (0.0233 $ year^{–1} $), DB (0.0239 $ year^{–1} $) and DTB (0.0246 $ year^{–1} $), retrieval algorithms. For MODIS Aqua satellite also, there exists an increasing $ AOD_{550 nm} $ trend with differing but higher AOD $ year^{–1} $ trend value. On the contrary, for monsoon and pre-monsoon seasons, for MODIS Terra and Aqua satellite, the AOD trends are found to be statistically insignificant.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">AOD</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MODIS</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">AERONET</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DB</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DT</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DTB combined</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Central IGP</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kolhe, A. R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, P.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mahajan, C. M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kutal, G. C.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Patil, R. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Prasad, P.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Aher, G. R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Aerosol science and engineering</subfield><subfield code="d">[Singapore] : Springer Singapore, 2017</subfield><subfield code="g">6(2022), 2 vom: 26. März, Seite 197-211</subfield><subfield code="w">(DE-627)883913747</subfield><subfield code="w">(DE-600)2890764-4</subfield><subfield code="x">2510-3768</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2</subfield><subfield code="g">day:26</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:197-211</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s41810-022-00135-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</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_SPRINGER</subfield></datafield><datafield tag="912" 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