Comparison of Two Simulation Methods of the Temperature Vegetation Dryness Index (TVDI) for Drought Monitoring in Semi-Arid Regions of China

Du, Lingtong; Song, Naiping; Liu, Ke; Hou, Jing; Hu, Yue; Zhu, Yuguo; XinYun, Wang; Wang, Lei; Guo, Yige

doi:10.3390/rs9020177

Cited by 66 publications

(34 citation statements)

References 49 publications

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“…Thus, the green color lines in the NDVI-LST space can be considered as a set of SM contours and the location of a pixel in the NDVI-LST triangular space is influenced mainly by SM availability (Figure 2). [29,73,74]). Soil moisture is minimal along the dry edge and maximal along the wet edge.…”

Section: Interpretation Of the Ndvi-lst Triangle Spacementioning

confidence: 99%

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

Yuan

Zhang

et al. 2020

Remote Sensing

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Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.

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Section: Interpretation Of the Ndvi-lst Triangle Spacementioning

confidence: 99%

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

Yuan

Zhang

et al. 2020

Remote Sensing

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“…In the area with higher vegetation index, Land Surface Temperature (LST) is very sensitive to the water pressure resulting from the relationship between canopy temperature and transpiration. In the lower areas vegetation index, LST is determined by the soil moisture variation through evaporative control and thermal inertia [Du et al, 2017. Schirmbeck, 2017.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Selected Peatland uses and Soil Moisture Based on TVDI

Holidi¹,

Armanto²,

Damiri³

et al. 2019

J. Ecol. Eng.

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The conversion of peatland from forest to non-forest causes environmental damage and increases high land dryness. Mapping of peatland based on dryness is very important to control and prevent fires. This study aims at characterizing peatland based on the level of temperature vegetation dryness index (TVDI) and evaluating the correlation between TVDI value and soil moisture. The research was conducted in August 2018 during the dry season. The area of research located in peat hydrological unit of Sibumbung River-Batok River in Ogan Komering Ilir (OKI) District of South Sumatra Province covering 63,427 ha area that consists of various land uses. The result showed that extreme wet category is found in water bodies and secondary forests that have high density, moderate wet is found in paddy fields and grass, normal area is found in the area covered by low-density trees, moderate dry is found in shrubs and oil palm plantations with good management and extreme dry areas is found in grasses and oil palm with poor cultivation management. There is a correlation between the TVDI value to the soil moisture on 0-10 cm and 10-20 cm and 20-30 cm depth.

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“…The principle of VTCI is almost the same as TVDI and VTCI = 1-TVDI. These two indexes were only acquired from remotely sensed data and are widely applied to evaluate the drought status and assess its impacts [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

Liu

Yue

2018

Remote Sensing

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Traditional NDVI-T s space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-T s space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and T s data to reexamine if the NDVI-T s space with "time" and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-T s space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of T s and NDVI in the Shaanxi province, which was linear in the triangular NDVI-T s space. The Temperature Vegetation Dryness Index (TVDI c) was obtained from bi-parabolic NDVI-T s andTVDI t was obtained from the triangular NDVI-T s space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDI c was better than TVDI t , which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDI c between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.

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Comparison of Two Simulation Methods of the Temperature Vegetation Dryness Index (TVDI) for Drought Monitoring in Semi-Arid Regions of China

Cited by 66 publications

References 49 publications

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

Characteristics of Selected Peatland uses and Soil Moisture Based on TVDI

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

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