Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiaoyang Zhao [verfasserIn] Haoming Xia [verfasserIn] Baoying Liu [verfasserIn] Wenzhe Jiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	drought spatiotemporal characteristics compare wetting

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 7, p 1570
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:7, p 1570

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14071570

Katalog-ID:	DOAJ02478608X

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allfields	10.3390/rs14071570 doi (DE-627)DOAJ02478608X (DE-599)DOAJ8c18d0f72c9a44e1a7fe758ae105e170 DE-627 ger DE-627 rakwb eng Xiaoyang Zhao verfasserin aut Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. drought spatiotemporal characteristics compare wetting Science Q Haoming Xia verfasserin aut Baoying Liu verfasserin aut Wenzhe Jiao verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 7, p 1570 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:7, p 1570 https://doi.org/10.3390/rs14071570 kostenfrei https://doaj.org/article/8c18d0f72c9a44e1a7fe758ae105e170 kostenfrei https://www.mdpi.com/2072-4292/14/7/1570 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 7, p 1570
spelling	10.3390/rs14071570 doi (DE-627)DOAJ02478608X (DE-599)DOAJ8c18d0f72c9a44e1a7fe758ae105e170 DE-627 ger DE-627 rakwb eng Xiaoyang Zhao verfasserin aut Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. drought spatiotemporal characteristics compare wetting Science Q Haoming Xia verfasserin aut Baoying Liu verfasserin aut Wenzhe Jiao verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 7, p 1570 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:7, p 1570 https://doi.org/10.3390/rs14071570 kostenfrei https://doaj.org/article/8c18d0f72c9a44e1a7fe758ae105e170 kostenfrei https://www.mdpi.com/2072-4292/14/7/1570 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 7, p 1570
allfields_unstemmed	10.3390/rs14071570 doi (DE-627)DOAJ02478608X (DE-599)DOAJ8c18d0f72c9a44e1a7fe758ae105e170 DE-627 ger DE-627 rakwb eng Xiaoyang Zhao verfasserin aut Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. drought spatiotemporal characteristics compare wetting Science Q Haoming Xia verfasserin aut Baoying Liu verfasserin aut Wenzhe Jiao verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 7, p 1570 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:7, p 1570 https://doi.org/10.3390/rs14071570 kostenfrei https://doaj.org/article/8c18d0f72c9a44e1a7fe758ae105e170 kostenfrei https://www.mdpi.com/2072-4292/14/7/1570 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 7, p 1570
allfieldsGer	10.3390/rs14071570 doi (DE-627)DOAJ02478608X (DE-599)DOAJ8c18d0f72c9a44e1a7fe758ae105e170 DE-627 ger DE-627 rakwb eng Xiaoyang Zhao verfasserin aut Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. drought spatiotemporal characteristics compare wetting Science Q Haoming Xia verfasserin aut Baoying Liu verfasserin aut Wenzhe Jiao verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 7, p 1570 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:7, p 1570 https://doi.org/10.3390/rs14071570 kostenfrei https://doaj.org/article/8c18d0f72c9a44e1a7fe758ae105e170 kostenfrei https://www.mdpi.com/2072-4292/14/7/1570 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 7, p 1570
allfieldsSound	10.3390/rs14071570 doi (DE-627)DOAJ02478608X (DE-599)DOAJ8c18d0f72c9a44e1a7fe758ae105e170 DE-627 ger DE-627 rakwb eng Xiaoyang Zhao verfasserin aut Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. drought spatiotemporal characteristics compare wetting Science Q Haoming Xia verfasserin aut Baoying Liu verfasserin aut Wenzhe Jiao verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 7, p 1570 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:7, p 1570 https://doi.org/10.3390/rs14071570 kostenfrei https://doaj.org/article/8c18d0f72c9a44e1a7fe758ae105e170 kostenfrei https://www.mdpi.com/2072-4292/14/7/1570 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 7, p 1570
language	English
source	In Remote Sensing 14(2022), 7, p 1570 volume:14 year:2022 number:7, p 1570
sourceStr	In Remote Sensing 14(2022), 7, p 1570 volume:14 year:2022 number:7, p 1570
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	drought spatiotemporal characteristics compare wetting Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Xiaoyang Zhao @@aut@@ Haoming Xia @@aut@@ Baoying Liu @@aut@@ Wenzhe Jiao @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ02478608X
language_de	englisch
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The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. 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author	Xiaoyang Zhao
spellingShingle	Xiaoyang Zhao misc drought misc spatiotemporal characteristics misc compare misc wetting misc Science misc Q Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine
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topic_title	Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine drought spatiotemporal characteristics compare wetting
topic	misc drought misc spatiotemporal characteristics misc compare misc wetting misc Science misc Q
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title	Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine
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title_full	Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine
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title_sort	spatiotemporal comparison of drought in shaanxi–gansu–ningxia from 2003 to 2020 using various drought indices in google earth engine
title_auth	Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine
abstract	As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.
abstractGer	As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.
abstract_unstemmed	As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R<sup<2</sup< = 0.9606, <i<p</i< < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R<sup<2</sup< = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (<i<p</i< < 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and <i<p</i< values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and <i<p</i< values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.
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title_short	Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine
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Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

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