DEM correction to the TVDI method on drought monitoring in karst areas

Yan, Huang; Zhou, Guoqing; Yang, Fengfeng; Xing, Lu

doi:10.1080/01431161.2018.1500732

Cited by 28 publications

(13 citation statements)

References 35 publications

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“…In addition, Moran et al [36] defined the vegetation index/temperature trapezoid (VITT) based on the relationship between temperature and vegetation. This trapezoidal model has also been used to calculate the TVDI index in many studies [26], [37]- [39]. In combination with the actual scatter plot, the wet edge is not considered as a parallel line.…”

Section: A Temperature Vegetation Dryness Indexmentioning

confidence: 99%

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Evaluation of Temperature Vegetation Dryness Index on Drought Monitoring Over Eurasia

et al. 2020

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This study explored the applicability of Temperature Vegetation Dryness Index (TVDI) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and land surface temperature (LST) data for drought monitoring in Eurasia from April to September during 2005-2014. TVDI was calculated based on the vegetation index/temperature trapezoid eigenspace (VITT) in this study. The Standardized Precipitation Evapotranspiration Index in three time scales (SPEI-01, SPEI-03, SPEI-06) and the Essential Climate Variable surface soil moisture product (ECV-SM) was used to evaluate the performance of TVDI over Eurasia, and to assess the sensitivity of TVDI to different land-cover types. The records of drought events from Emergency Events Database (EM-DAT) were also used to validate TVDI. The results indicated that TVDI negatively responded to SPEIs about 77.43%, 72.01%, and 75.95% of lands respectively, of which 29.84%, 33.93%, and 25.59% were subjected significant relationships (p<0.05). A negative correlation between TVDI and SPEIs was detected in most land-cover types, except for evergreen broadleaf forest and deciduous needleaf forest. Similarly, TVDI was also negative correlated with ECV-SM. About 66% of the study areas show negative correlation and 33% of them were statistically significant. There is a highly negative correlation between TVDI and ECV-SM in most types of land cover. Moreover, TVDI could not reflect drought condition correctly when TVDI < 0.40; it could only reflect long-time excessive drought condition when TVDI > 0.86. A comparison of TVDI with drought records demonstrated that the TVDI could capture drought events in study area. INDEX TERMS Continental scales, drought monitoring, land-cover types, Eurasia, temperature vegetation dryness index.

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Section: A Temperature Vegetation Dryness Indexmentioning

confidence: 99%

“…A conceptual LST-NDVI triangle (a, modified from Sandholt et al[25]) and a conceptual LST-NDVI trapezoid (b, modified from Yan et al[39]). …”

mentioning

confidence: 99%

Evaluation of Temperature Vegetation Dryness Index on Drought Monitoring Over Eurasia

et al. 2020

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“…Considering the influence of topography and land cover types on the LST, many studies performed relative comprehensive investigations [24,[26][27][28][29]. Nevertheless, bushfires and topography will not need to be responsible for the estimation of MPDI.…”

Section: Estimation Of the Dry And Wet Edges For Tvdi And Cvdimentioning

confidence: 99%

“…The values of CVDI and MPDI on DOY242 both have a relatively small difference between the mountain and plain areas, which indicates that the plain areas in Victoria are mostly covered with crop and grass in winter. However, the TVDI on DOY242 has no similar spatial pattern because the LST is greatly affected by the topography and DEM data is considered to calibrate the LST in the mountain areas [24,26].…”

Section: Spatial Comparison Of Sm Estimation Between Mpdi Tvdi and Cvdimentioning

confidence: 99%

“…Several assumptions and limitations need to be considered when applying the TVDI: (1) a given spatial domain is needed to present SM and vegetation cover conditions, and the dimension of the mapping area should be large enough to ensure that the scatterplots of LST versus VI can adequately form a regular triangular or trapezoid shape including completely wet edges and extremely dry edges; (2) in the spatial domain, surface properties and atmospheric conditions are relatively homogeneous and the variability of SM simply depends on variations of LST; (3) land cover types and topography should be fully considered to reduce the uncertainty of TVDI in SM retrieval. Previous studies have proposed many calibrated or improved TVDI methods according to these assumptions and limitations, which are classified into three categories: (1) a temperature correction model using surface-air temperature differences based on regional DEM data [26][27][28][29], day-night temperature differences [30] to effectively eliminate the impact of solar radiation and atmospheric background reflectance; (2) a dry edge correction method [31] or bi-parabolic LST-NDVI space method [32,33] to reshape the fitting equations of the dry edge or wet edge; (3) a time domain solution considering the maximum surface temperature of bare soil, which is a special case of TVDI [7,34]. These improvements have considerable scientific significances and provide promising approaches with potential application for SM estimation and drought monitoring.…”

Section: Introductionmentioning

confidence: 99%

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A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

et al. 2020

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The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

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Agricultural drought survey using MODIS-based image indices at the regional scale: case study of the Urmia Lake Basin, Iran

Shamloo

Sattari

Apaydín

2022

Theor Appl Climatol

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DEM correction to the TVDI method on drought monitoring in karst areas

Cited by 28 publications

References 35 publications

Evaluation of Temperature Vegetation Dryness Index on Drought Monitoring Over Eurasia

Evaluation of Temperature Vegetation Dryness Index on Drought Monitoring Over Eurasia

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

Agricultural drought survey using MODIS-based image indices at the regional scale: case study of the Urmia Lake Basin, Iran

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