Climate Change Data Portal
DOI | 10.1016/j.rse.2019.111220 |
Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia | |
Guo, Hao1,2,3,4,7; Bao, Anming1,4,7; Liu, Tie1,4,7; Ndayisaba, Felix5,6; Jiang, Liangliang1,2,3,4,7; Zheng, Guoxiong1,2; Chen, Tao1,2; De Maeyer, Philippe3,4,7 | |
发表日期 | 2019 |
ISSN | 0034-4257 |
EISSN | 1879-0704 |
卷号 | 231 |
英文摘要 | The Scaled Drought Condition Index (SDCI) is an agricultural drought index derived from multiple remote sensing datasets including precipitation from the Tropical Rainfall Measurement Mission (TRMM) and temperature and vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS). The SDCI can be used for large-scale drought monitoring at a high spatial resolution in dry and humid regions. However, it is not applicable to certain areas for numerous reasons, including the discontinued service and limited coverage of the TRMM, uncertainties associated with the coarse resolution of precipitation input, the fixed lag time between precipitation deficit and vegetation responses, experimental weights and an arbitrary classification scheme. Therefore, this study aims to propose an optimal SDCI (OSDCI) using an alternative Global Satellite Mapping of Precipitation (GSMaP) as precipitation input, adding a varied lag time between precipitation and vegetation response, determining the optimal weights of variables and revising the severity classification. In Central Asia, OSDCI is calibrated using the 3-month Standardized Precipitation Evapotranspiration Index (SPEI) during the 2001-2008 growing seasons as drought reference. The SPEI from 2009 to 2016, soil moisture anomalies (SMAs) derived from both the Global Land Data Assimilation System (GLDAS) and the Climate Prediction Center (CPC), and the annual crop production of wheat and barley are used for cross-validation. For comparison, three original SDCIs are also included. The results show that the OSDCI displays better performance than the three experimental SDCIs with higher correlation coefficients relative to SPEI and SMA from the CPC for different cover types, and performance is comparable to that of SMA from the GLDAS and annual crop production. Additionally, the OSDCI based on the revised classification scheme (OSDCI_rev) shows significant superiority over the OSDCI based on the original classification system (OSDCI org) with lower drought frequency and area error. Furthermore, the OSDCI_rev can capture both the temporal evolution and the spatial patterns of droughts exemplified by the SPEI and SMAs. Overall, the results of this study show the potential use of the OSDCI with a revised classification scheme as a remote sensing drought index for agricultural drought monitoring. |
WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
来源期刊 | REMOTE SENSING OF ENVIRONMENT
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文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/102879 |
作者单位 | 1.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100039, Peoples R China; 3.Univ Ghent, Dept Geog, B-9000 Ghent, Belgium; 4.Sino Belgian Joint Lab Geoinformat, Urumqi 830011, Peoples R China; 5.Univ Lay Adventists Kigali UNILAK, Fac Environm Sci, PO 6392, Kigali, Rwanda; 6.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China; 7.Sino Belgian Joint Lab Geoinformat, B-9000 Ghent, Belgium |
推荐引用方式 GB/T 7714 | Guo, Hao,Bao, Anming,Liu, Tie,et al. Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia[J],2019,231. |
APA | Guo, Hao.,Bao, Anming.,Liu, Tie.,Ndayisaba, Felix.,Jiang, Liangliang.,...&De Maeyer, Philippe.(2019).Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia.REMOTE SENSING OF ENVIRONMENT,231. |
MLA | Guo, Hao,et al."Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia".REMOTE SENSING OF ENVIRONMENT 231(2019). |
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