An evaluation of satellite-based drought indices on a regional scale

Sur, Chanyang; Hur, Jiwon; Kim, Kyoungjun; Choi, Woo-Jung; Choi, Minha

doi:10.1080/01431161.2015.1101653

Cited by 33 publications

(12 citation statements)

References 37 publications

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“…In the 20th century, agricultural drought monitoring relied on often patchy and site-based meteorological indices [13], which principally included rainfall and evaporative demand. Developments in remote sensing science in the latter part of the 20th century enabled significant advances in drought monitoring based on inferring vegetation condition and other hydrological variables using multispectral or radar satellite data [20]. Previous approaches were significantly limited by the lack in continuous spatio-temporal meteorological observations [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Previous approaches were significantly limited by the lack in continuous spatio-temporal meteorological observations [21,22]. The main benefit of a remote sensing approach for drought monitoring is that it provides a cost effective method to assess key variables related to drought over larger temporal and spatial scales than was previously possible [13,20,23,24].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2

West

Quinn

Horswell

et al. 2018

Water

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The aim of this study was to determine the extent to which Sentinel-2 Normalised Difference Vegetation Index (NDVI) reflects soil moisture conditions, and whether this product offers an improvement over Landsat-8. Based on drought exposure, cloud-free imagery availability, and measured soil moisture, five sites in the Southwestern United States were selected. These sites, normally dry to arid, were in various states of drought. A secondary focus was therefore the performance of the NDVI under extreme conditions. Following supervised classification, the NDVI values for one-kilometre radius areas were calculated. Sentinel-2 NDVI variants using Spectral Bands 8 (10 m spatial resolution), 5, 6, 7, and 8A (20 m spatial resolution) were calculated. Landsat-8 NDVI was calculated at 30 m spatial resolution. Pearson correlation analysis was undertaken for NDVI against moisture at various depths. To assess the difference in correlation strength, a principal component analysis was performed on the combination of all bands and the combination of the new red-edge bands. Performance of the red-edge NDVI against the standard near infrared (NIR) was then evaluated using a Steiger comparison. No significant correlations between Landsat-8 NDVI and soil moisture were found. Significant correlations at depths of less than 30 cm were present between Sentinel-2 NDVI and soil moisture at three sites. The remaining two sites were characterised by low vegetation cover, suggesting a cover threshold of approximately 30-40% is required for a correlation to be present. At all sites of significant positive moisture to NDVI correlation, the linear combination of the red-edge bands produced stronger correlations than the poorer spectral but higher spatial resolution band. NDVI calculated using the higher spectral resolution bands may therefore be of greater use in this context than the higher spatial resolution option. Results suggest potential for the application of Sentinel-2 NDVI in soil moisture monitoring, even in extreme environments. To the best of our knowledge, this paper represents the first study of this kind using Sentinel-2.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2

West

Quinn

Horswell

et al. 2018

Water

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“…Korean Peninsula is located on the northeast brink of Asia at 33-43°N (latitude) and 124-132°E (longitude) ( Figure 1). Even though Korean Peninsula has been previously investigated by Sur et al [8], this study presents the result of the additional analysis performed in the north Korean region. Korean Peninsula covers an area of 219,020 km 2 , located in the Asian monsoon region, having a mean annual precipitation approximately 1,100 mm (North Korea, 919.7 mm; South Korea, 1,307.7 mm).…”

Section: Korean Peninsulamentioning

confidence: 99%

“…Moreover, VHI and SWDI are mainly estimated based on other variables related to vegetative greening conditions, so they cannot accurately reveal the instant of meteorological phenomena that can improve drought severity. For tenacity of this issue, Sur et al [8] assessed a progressive drought index named "Energy-Based Water Deficit Index" (EWDI), which concurrently considered the circulation of energy, water, and carbon across the soil surface and atmosphere to reflect the complex conditions of droughts related to atmosphere and vegetation. ey estimated this index across Korean Peninsula using MODIS-based datasets and exposed that the EWDI performed well and showed favorable association with the ESI (correlation coefficient within 0.73 and 0.76 based on their specific study area) as well as the conventional drought indices such as PDSI (correlation coefficient within 0.57 and 0.67) and SPI (correlation coefficient within 0.61 and 0.64).…”

Section: Introductionmentioning

confidence: 99%

Hydrological Drought Assessment of Energy-Based Water Deficit Index (EWDI) at Different Geographical Regions

Sur

Kim

Lee

et al. 2019

Advances in Meteorology

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This study applied the remote sensing-based drought index, namely, the Energy-Based Water Deficit Index (EWDI), across Mongolia, Australia, and Korean Peninsula for the period between 2000 and 2010. The EWDI is estimated based on the hydrometeorological variables such as evapotranspiration, soil moisture, solar radiation, and vegetation activity which are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imageries. The estimated EWDI was compared with the Evaporative Stress Index (ESI), the Vegetation Condition Index (VCI), and the Standardized Precipitation Index (SPI). The correlation coefficients between the drought indices are as follows: 0.73–0.76 (EWDI vs ESI), 0.64–0.71 (EWDI vs VCI), 0.54–0.64 (EWDI vs SPI-3), 0.69–0.71 (ESI vs VCI), 0.55–0.62 (ESI vs SPI-3), and 0.53–0.57 (VCI vs SPI-3). The drought prediction accuracy of each index according to error matrix analysis is as follows: 83.33–94.17% (EWDI), 70.00–91.67% (ESI), 47.50–85.00% (VCI), and 61.67–88.33% (SPI-3). Based on the results, the EWDI and ESI were found to be more accurate in capturing moderate drought conditions than the SPI at different geographical regions.

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“…Specifically, various meteorological, agricultural and hydrological drought indicators used in drought monitoring are based on ground-based meteorological datasets including precipitation, temperature, soil moisture data and groundwater levels, storage in the saturated zone or streamflow datasets. However, a major limitation in the application of several ground based drought indices is the use of point based dataset which restricts the spatio-temporal extrapolation of drought estimations to large areas due to several scientific reasons (Sur et al, 2015). Thus, remotely sensed observations have been extensively used to monitor drought-related variables from a climatological viewpoint and to assess drought impacts from an ecosystem perspective (Aghakouchak et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Drought at Different Climate Regions in Mongolia

2016

2nd International Conference on Disaster Management and Civil Engineering (ICDMCE-2016) April 12-13, 2016 Kyoto (Japan)

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An evaluation of satellite-based drought indices on a regional scale

Cited by 33 publications

References 37 publications

Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2

Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2

Hydrological Drought Assessment of Energy-Based Water Deficit Index (EWDI) at Different Geographical Regions

Assessment of Drought at Different Climate Regions in Mongolia

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