Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020
Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging S...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ji, Lin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 30(2022), 12 vom: 06. Dez., Seite 33040-33060 |
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| Übergeordnetes Werk: |
volume:30 ; year:2022 ; number:12 ; day:06 ; month:12 ; pages:33040-33060 |
| Links: |
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| DOI / URN: |
10.1007/s11356-022-24502-7 |
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| Katalog-ID: |
SPR049691767 |
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| 520 | |a Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. | ||
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10.1007/s11356-022-24502-7 doi (DE-627)SPR049691767 (SPR)s11356-022-24502-7-e DE-627 ger DE-627 rakwb eng Ji, Lin verfasserin aut Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. Spring maize (dpeaa)DE-He213 Spatio-temporal variations (dpeaa)DE-He213 Drought (dpeaa)DE-He213 Northeast China (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Wu, Yongfeng aut Ma, Juncheng aut Song, Chenxi aut Zhu, Zhicheng aut Zhao, Aiping aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 12 vom: 06. Dez., Seite 33040-33060 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:12 day:06 month:12 pages:33040-33060 https://dx.doi.org/10.1007/s11356-022-24502-7 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_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2360 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 30 2022 12 06 12 33040-33060 |
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10.1007/s11356-022-24502-7 doi (DE-627)SPR049691767 (SPR)s11356-022-24502-7-e DE-627 ger DE-627 rakwb eng Ji, Lin verfasserin aut Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. Spring maize (dpeaa)DE-He213 Spatio-temporal variations (dpeaa)DE-He213 Drought (dpeaa)DE-He213 Northeast China (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Wu, Yongfeng aut Ma, Juncheng aut Song, Chenxi aut Zhu, Zhicheng aut Zhao, Aiping aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 12 vom: 06. Dez., Seite 33040-33060 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:12 day:06 month:12 pages:33040-33060 https://dx.doi.org/10.1007/s11356-022-24502-7 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_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2360 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 30 2022 12 06 12 33040-33060 |
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10.1007/s11356-022-24502-7 doi (DE-627)SPR049691767 (SPR)s11356-022-24502-7-e DE-627 ger DE-627 rakwb eng Ji, Lin verfasserin aut Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. Spring maize (dpeaa)DE-He213 Spatio-temporal variations (dpeaa)DE-He213 Drought (dpeaa)DE-He213 Northeast China (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Wu, Yongfeng aut Ma, Juncheng aut Song, Chenxi aut Zhu, Zhicheng aut Zhao, Aiping aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 12 vom: 06. Dez., Seite 33040-33060 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:12 day:06 month:12 pages:33040-33060 https://dx.doi.org/10.1007/s11356-022-24502-7 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_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2360 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 30 2022 12 06 12 33040-33060 |
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10.1007/s11356-022-24502-7 doi (DE-627)SPR049691767 (SPR)s11356-022-24502-7-e DE-627 ger DE-627 rakwb eng Ji, Lin verfasserin aut Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. Spring maize (dpeaa)DE-He213 Spatio-temporal variations (dpeaa)DE-He213 Drought (dpeaa)DE-He213 Northeast China (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Wu, Yongfeng aut Ma, Juncheng aut Song, Chenxi aut Zhu, Zhicheng aut Zhao, Aiping aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 12 vom: 06. Dez., Seite 33040-33060 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:12 day:06 month:12 pages:33040-33060 https://dx.doi.org/10.1007/s11356-022-24502-7 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_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2360 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 30 2022 12 06 12 33040-33060 |
| allfieldsSound |
10.1007/s11356-022-24502-7 doi (DE-627)SPR049691767 (SPR)s11356-022-24502-7-e DE-627 ger DE-627 rakwb eng Ji, Lin verfasserin aut Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. Spring maize (dpeaa)DE-He213 Spatio-temporal variations (dpeaa)DE-He213 Drought (dpeaa)DE-He213 Northeast China (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Wu, Yongfeng aut Ma, Juncheng aut Song, Chenxi aut Zhu, Zhicheng aut Zhao, Aiping aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 12 vom: 06. Dez., Seite 33040-33060 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:12 day:06 month:12 pages:33040-33060 https://dx.doi.org/10.1007/s11356-022-24502-7 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_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 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_2360 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 30 2022 12 06 12 33040-33060 |
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Enthalten in Environmental science and pollution research 30(2022), 12 vom: 06. Dez., Seite 33040-33060 volume:30 year:2022 number:12 day:06 month:12 pages:33040-33060 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. 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Ji, Lin |
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Ji, Lin misc Spring maize misc Spatio-temporal variations misc Drought misc Northeast China misc TVDI misc MODIS Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 |
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Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 Spring maize (dpeaa)DE-He213 Spatio-temporal variations (dpeaa)DE-He213 Drought (dpeaa)DE-He213 Northeast China (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 |
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Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 |
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Environmental science and pollution research |
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spatio-temporal variations and drought of spring maize in northeast china between 2002 and 2020 |
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Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 |
| abstract |
Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the “Liandaowan” region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002–2014), exhibited changes and reductions in response to the change (2015–2017), and exhibited optimization and recovery after this change (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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12 |
| title_short |
Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020 |
| url |
https://dx.doi.org/10.1007/s11356-022-24502-7 |
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| author2 |
Wu, Yongfeng Ma, Juncheng Song, Chenxi Zhu, Zhicheng Zhao, Aiping |
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Wu, Yongfeng Ma, Juncheng Song, Chenxi Zhu, Zhicheng Zhao, Aiping |
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2024-07-04T01:53:41.276Z |
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| score |
7.4001493 |