Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

Chen, Hao; Zhang, Jiahua; Yao, Fengmei

doi:10.1016/j.jag.2014.09.011

Cited by 144 publications

(78 citation statements)

References 37 publications

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“…The inversion algorithm itself is a key factor that affects the drought-monitoring performance of the indices. For example, there was a large difference between the VUA-based TCI and MODIS-based TCI [2,35]. SMCI did not function better in areas with a high density of vegetation cover and high temperature than VIUPD-derived VCI.…”

Section: Facters Which Affect the Performance Of Viupd-derived Vcimentioning

confidence: 87%

“…Previous studies revealed that there is a lag response of vegetation indices to moisture conditions and the lagged response of vegetation indices likely occurs because vegetation growth is controlled by soil moisture and therefore changes in vegetation growth are buffered by soil moisture [2,3]. 1-and 2-month SPI are more suitable for evaluating meteorological remote sensing-based drought indices such as PCI and VUA-based SMCI; PDSI, 3-, 6-, 9-, 12-, and 24-month SPI are more suitable for evaluating agricultural remote sensing-based drought indices [29,35]. …”

Section: Monthly Temporal Correlationsmentioning

confidence: 99%

“…The TCI, PCI and SMCI are widely used remote-sensing-based single drought indices [2,35]. To assess the ability of VIUPD-derived VCI to monitor drought, the spatial patterns of VIUPD-derived VCI and the widely used remote-sensing-based single drought indices were compared for April to October 2002-2011 ( Figure 6 and Tables 3-5).…”

Section: Comparison Of the Spatial Variability Of Viupd-derived Vci Amentioning

confidence: 99%

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Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

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Section: Facters Which Affect the Performance Of Viupd-derived Vcimentioning

confidence: 87%

Section: Monthly Temporal Correlationsmentioning

confidence: 99%

Section: Comparison Of the Spatial Variability Of Viupd-derived Vci Amentioning

confidence: 99%

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Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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“…It is reported that these severe droughts have caused evident change in regional conditions of water and heat, reducing the supply of drinking water to the local inhabitants, disturbing the function of the vegetation service, and even leading to reduction of biodiversity and degradation of vegetation [35,36]. Therefore, to characterize the moisture condition of Southwest China is of great importance for risk management of water resources and agricultural production, and also benefits to provide an early warning for the protection of ecological environment.…”

Section: Study Areamentioning

confidence: 99%

Use of the Standardized Precipitation Evapotranspiration Index (SPEI) to Characterize the Drying Trend in Southwest China from 1982–2012

Quan

et al. 2015

Remote Sensing

123

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Abstract:In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982-2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, and the spatio-temporal extent of drought were evaluated, respectively. The results explicitly showed a drying trend of Southwest China. The mean SPEI values at five time scales all decreased significantly. Some moderate and severe droughts were captured after 2005 and the droughts were even getting aggravated. By examining the spatio-temporal extent, the aggravating condition of drought was further revealed. To investigate the performance of SPEI, correlation analysis was conducted between SPEI and two remotely sensed drought indices: Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI). The comparison was also conducted with the Standardized Precipitation Index (SPI). The results showed that for both SMCI and VCI, the SPI and SPEI had approximate correlations with them. The SPEI could better monitor the soil moisture than the SPI in months with significant increase of temperature. The correlations between the VCI and SPI/SPEI were lower; nevertheless, the SPEI was slightly superior to the SPI.

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“…Recent studies have linked rainfall deficits, soil thermal stress and vegetation growth and mortality to drought [2,3,[5][6][7]9,11,12,15,20,25,[27][28][29][30][31][32]36,[50][51][52], highlighting the need for operational drought monitoring systems that can show drought intensity, location and extent [16]. In response, this study devised an operational procedure and algorithm, a run-off model, from TMPA rainfall and MODIS evapotranspiration products for spatiotemporal drought assessment (Figures 3 and 4).…”

Section: Discussionmentioning

confidence: 99%

Satellite-Based Run-Off Model for Monitoring Drought in Peninsular Malaysia

et al. 2016

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Traditional in situ observation interpolation techniques that provide rainfall data from rain gauges have limitations because they are discrete point-based data records, which may not be sufficient to assess droughts from a spatiotemporal perspective. Considering this limitation, this study has developed a run-off model-a fully satellite-based method for monitoring drought in Peninsular Malaysia. The formulation of the run-off deficit uses a water balance equation based on satellite-based rainfall and evapotranspiration data extracted respectively from calibrated TRMM multi-satellites precipitation analysis data (TMPA) and moderate resolution imaging spectroradiometer data (MODIS). The run-off deficit was calculated based on per pixel spatial scale and allowed to produce the continuous and regular run-off maps. The run-off model was tested and evaluated in a one drought year (2005) within a span of three years (2003)(2004)(2005) over the Kelantan (3448 km 2 ) and Hulu Perak (3672 km 2 ) catchments of Peninsular Malaysia. The validation results show that (1) monthly TMPA rainfall and MODIS evapotranspiration data significantly improved after calibration; (2) satellite-based run-off data is not only strongly correlated with actual steam flow, but also with spatiotemporal variation of run-off in drought-affected forest catchments. The most severely drought-affected forest catchments that experienced the run-off deficits were Hulu Perak, Ulu Gading, Gunung Stong and Relai over Kelantan. The real time run-off change analysis shows that drought started in January and reached its peak in July of 2005. It was therefore demonstrated that this fully satellite-based run-off deficit model is as good as a conventional drought-monitoring indicator, and can provide not only drought distribution information, but it also can reflect the drought-induced impacts on stream flow, forest catchment and land-use.

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Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

Cited by 144 publications

References 37 publications

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Use of the Standardized Precipitation Evapotranspiration Index (SPEI) to Characterize the Drying Trend in Southwest China from 1982–2012

Satellite-Based Run-Off Model for Monitoring Drought in Peninsular Malaysia

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