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Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI) : evaluation in Central Asia

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Abstract
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.
Keywords
MONITORING METEOROLOGICAL DROUGHT, REMOTE-SENSING DATA, AGRICULTURAL, DROUGHT, VEGETATION ACTIVITY, SOIL-MOISTURE, PRECIPITATION PRODUCTS, EVAPORATIVE DEMAND, CLIMATE-CHANGE, AGRO-DROUGHTS, CHINA, Drought, Assessment, OSDCI, SPEI, Central Asia

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MLA
Guo, Hao, et al. “Determining Variable Weights for an Optimal Scaled Drought Condition Index (OSDCI) : Evaluation in Central Asia.” REMOTE SENSING OF ENVIRONMENT, vol. 231, 2019.
APA
Guo, H., Bao, A., Liu, T., Ndayisaba, F., Jiang, L., Zheng, G., … De Maeyer, P. (2019). Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI) : evaluation in Central Asia. REMOTE SENSING OF ENVIRONMENT, 231.
Chicago author-date
Guo, Hao, Anming Bao, Tie Liu, Felix Ndayisaba, Liangliang Jiang, Guoxiong Zheng, Tao Chen, and Philippe De Maeyer. 2019. “Determining Variable Weights for an Optimal Scaled Drought Condition Index (OSDCI) : Evaluation in Central Asia.” REMOTE SENSING OF ENVIRONMENT 231.
Chicago author-date (all authors)
Guo, Hao, Anming Bao, Tie Liu, Felix Ndayisaba, Liangliang Jiang, Guoxiong Zheng, Tao Chen, and Philippe De Maeyer. 2019. “Determining Variable Weights for an Optimal Scaled Drought Condition Index (OSDCI) : Evaluation in Central Asia.” REMOTE SENSING OF ENVIRONMENT 231.
Vancouver
1.
Guo H, Bao A, Liu T, Ndayisaba F, Jiang L, Zheng G, et al. Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI) : evaluation in Central Asia. REMOTE SENSING OF ENVIRONMENT. 2019;231.
IEEE
[1]
H. Guo et al., “Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI) : evaluation in Central Asia,” REMOTE SENSING OF ENVIRONMENT, vol. 231, 2019.
@article{8650007,
  abstract     = {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.},
  articleno    = {111220},
  author       = {Guo, Hao and Bao, Anming and Liu, Tie and Ndayisaba, Felix and Jiang, Liangliang and Zheng, Guoxiong and Chen, Tao and De Maeyer, Philippe},
  issn         = {0034-4257},
  journal      = {REMOTE SENSING OF ENVIRONMENT},
  keywords     = {MONITORING METEOROLOGICAL DROUGHT,REMOTE-SENSING DATA,AGRICULTURAL,DROUGHT,VEGETATION ACTIVITY,SOIL-MOISTURE,PRECIPITATION PRODUCTS,EVAPORATIVE DEMAND,CLIMATE-CHANGE,AGRO-DROUGHTS,CHINA,Drought,Assessment,OSDCI,SPEI,Central Asia},
  language     = {eng},
  pages        = {17},
  title        = {Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI) : evaluation in Central Asia},
  url          = {http://dx.doi.org/10.1016/j.rse.2019.111220},
  volume       = {231},
  year         = {2019},
}

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