Drought Change Trend Using MODIS TVDI and Its Relationship with Climate Factors in China from 2001 to 2010

Liang, Liang; Zhao, Shuang; Qin, Zhihao; He, Kexun; Cheng, Chong; Luo, Yunxiao; Xing-dong, Zhou

doi:10.1016/s2095-3119(14)60813-3

Cited by 84 publications

(43 citation statements)

References 21 publications

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“…Building on the vegetation supply water index (VSWI) developed by Nemani et al [13] and the vegetation index/temperature (VIT) trapezoid proposed by Moran [14], Sandholt et al [15] proposed a Temperature-Vegetation Dryness Index (TVDI) based on the VI (vegetation index) and LST (land surface temperature). Since then, more researchers have used TVDI or have improved the TVDI to assess and predict drought [16][17][18][19], and they have fit the TVDI value and observed value of soil moisture to the inverse of the soil moisture [20,21]. The results from a large number of studies [22][23][24][25][26] have demonstrated that it is more advantageous to use the combined vegetation index and the surface temperature to monitor drought or even to investigate the regional soil moisture distribution.…”

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

et al. 2016

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This study proposes a modified vegetation-dependent temperature-vegetation dryness index (TVDI) model for analyzing regional drought disasters in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) of China. First, MODIS monthly normalized difference vegetation index (NDVI), land surface temperature (LST) data and land use/cover data (Land cover type2) were pre-processed as a consistent big dataset. The land use/cover data were modified and integrated into six primary types. Then, these land types were used as the base data layer to calculate the TVDI by parameterizing the relationship between the MODIS NDVI and LST data. By emphasizing different types of land uses, this study was able to compare and analyze the differences of the TVDI indices between the entire study area (no consideration of the land types) and the six classified land uses. The soil moisture data were used to validate the modified TVDI values based on different land uses, which confirmed that the modified model more effectively reflected drought conditions. Finally, the aforementioned model was used to analyze the temporal and spatial variation of drought experienced by vegetation cover from 2000 to 2014. The results of the modified model were validated with the synchronized soil moisture and precipitation data. The case study clearly demonstrated that the modified TVDI model, which is based on different vegetation indexes, could better reflect the drought conditions of the study area.

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Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

et al. 2016

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“…It is noteworthy that using the TVDI normalized for each image is more common in the literature (Gao et al 2011;Holzman et al 2014;Liang et al 2014). Moreover, this is critical to the use of TVDI for crop monitoring purposes and can sometimes be misinterpreted.…”

Section: Resultsmentioning

confidence: 99%

TVDI spatiotemporal pattern of a soybean growing area in humid subtropical climate

2017

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ABSTRACT:The most southern Brazilian state, Rio Grande do Sul, is characterized by high crop yield and is, currently, the third leading soybean producer in the country. Therefore, agriculture is very important to the economy of the region. Because agriculture is highly dependent on variable weather parameters, the present study aimed to test the Vegetation Index (NDVI) images available from MODIS products, which were correlated to obtain a triangular scatter plot. Rainfall data from surface weather stations were also used. The results showed that the pattern of the TVDI is associated with rainfall variability. However, as the TVDI is based on normalization of the wet and dry edges of each image individually, the comparison of TVDI values across different images is challenging. This deficiency can be mitigated by analysis of the parameters used to derive the index. Therefore, both the TVDI and the "b" and T Smin parameters used to derive it can elucidate the patterns of crop response to water availability.

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“…The greatest variability was ob- (Goward et al, 2002;Liang et al, 2014). Figures 3A and 3B show the triangles of the two scenes chosen to exemplify different water conditions.…”

Section: Tvdi -Scene Parameterizationmentioning

confidence: 99%

“…This index is based on the relationship between VI and T S and the model allows parameterizing the vegetation water status between wet and dry limits of the evaporative triangle. TVDI has been applied to crop monitoring in several regions (Wang et al, 2006;Gao et al, 2011;Holzman et al, 2014;Liang et al, 2014), but it is not yet tested in large grain-producing areas in southern Brazil. Moreover, in most studies, the type of parameterization applied to TVDI and the consequences of using different approaches are not sufficiently explained.…”

Section: Introductionmentioning

confidence: 99%

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Schirmbeck

Fontana

Schirmbeck

2018

Sci. agric. (Piracicaba, Braz.)

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Drought Change Trend Using MODIS TVDI and Its Relationship with Climate Factors in China from 2001 to 2010

Cited by 84 publications

References 21 publications

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

The Spatiotemporal Variation of Drought in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) Based on the Modified TVDI

TVDI spatiotemporal pattern of a soybean growing area in humid subtropical climate

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

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