The relationship between the Normalized Difference Vegetation Index and drought indices in the South Central United States

Bushra, Nazla; Rohli, Robert V.; Lam, Nina Siu‐Ngan; Zou, Lei; Mostafiz, Rubayet Bin; Mihunov, Volodymyr V.

doi:10.1007/s11069-019-03569-5

Cited by 31 publications

(15 citation statements)

References 55 publications

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“…We should keep in mind that NDWI is an indicator of leaf water content in vegetation, and not an indicator of drought. A possibility to have a better view on droughts is combining NDWI with NDVI in a Normalized Difference Drought Index (NDDI) (Bushra et al, 2019;Gulácsi & Kovács, 2015;Lee et al, 2016) and validate it with in situ measurements and help to define thresholds and train models. However, this indicator does not have a global drought threshold and, moreover, is not recorded within the IDMP (Jayawardhana & Chathurange, 2020).…”

Section: Ndwi Spatiotemporal Patterns Across Switzerlandmentioning

confidence: 99%

Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

et al. 2021

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Section: Ndwi Spatiotemporal Patterns Across Switzerlandmentioning

confidence: 99%

Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

et al. 2021

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“…The SPEI taken at multiple time scales from the monthly averages of temperature and precipitation clearly reflect the regional dry-wet evolution and the availability of water resources. Furthermore, the SPEI serves as an indicator of drought by taking into account the multi-scale characteristics of the standardized precipitation index (SPI) and the implications of evaporation on the Palmer drought severity index(PDSI), enabling a better evaluation of the recent drought crises due to global warming, particularly those in semi-arid and arid areas [18,19].…”

Section: Introductionmentioning

confidence: 99%

Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Luo

Mao

Wen

et al. 2020

Land

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Drought and vegetation dynamics in the northern Xinjiang Uygur Autonomous Region of China (NXC), the centre of Asia with arid climate, were assessed using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI). Analyses were performed through the use of Sen's method and Spearman's correlation to investigate variations in the NDVI and the impacts of drought on vegetation from 1998 to 2015. The severity of droughts in the NXC was assessed by the SPEI, which was revealed to increase over the last 60 years at a rate of 0.017 per decade. This indicates that an alleviating tendency of drought intensity occurred in the NXC. Specifically, the spatial pattern of drought intensity increased gradually from the north-western to south-eastern regions. The average yearly NDVI was 0.28 and increased slightly by 0.001 yr −1 (r = 0.94, p = 3.64) between 1998 and 2015. Additionally, the NDVI showed an obviously spatial heterogeneity, with greater values in the west and small values in the east. Significantly, positive correlations between SPEI and NDVI were observed, while drought exerted a five-year lag effect on vegetation.

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“…We will effectively evaluate how the environmental effects of drought events evolved [45]. Furthermore, the multiple drought indices or composite drought index will be applied in the future study [46,47].…”

Section: Strength Of Time-varying Spatial Downscaling and Future Workmentioning

confidence: 99%

Time Varying Spatial Downscaling of Satellite-Based Drought Index

et al. 2021

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Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.

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The relationship between the Normalized Difference Vegetation Index and drought indices in the South Central United States

Cited by 31 publications

References 55 publications

Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Time Varying Spatial Downscaling of Satellite-Based Drought Index

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