Abstract
The Temperature Vegetation Dryness Index (TVDI), a drought monitoring index based on an empirical parameterization of the Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) space, has been widely implemented in a variety of ecosystems worldwide because it does not depend on ancillary data. However, the simulation of dry/wet edges in the TVDI model can be problematic because remote sensing images do not have sufficient pixels to identify the wetness and dryness extremes of different vegetation coverages. In this study, an improvement in dry/wet edge simulation was proposed, and a comparison of the original TVDI and the modified Temperature Vegetation Dryness Index (TVDIm) was performed for drought monitoring in Ningxia Province, which is a typical semi-arid region in China. First, the difference between the land surface temperatures in day and night (∆LST) was used as an alternative to LST when building the TVDIm model. In addition, the wet edges were improved by removing outliers using a statistical method, and the dry edges were optimized by removing the “tail down” points in the NDVI range of 0.0–0.1. Here, the modeling process of TVDIm in 2005, one of recent extreme drought year is illustrated. The results show that both the TVDI and TVDIm can be used to monitor the temporal and spatial variations of drought, and the onset, duration, extent, and severity of drought can be reflected by TVDI and TVDIm maps. However, the magnitude of TVDI is higher than that of TVDIm, which could cause the TVDI-simulated drought condition to be elevated in normal years and underestimated in dry years. The TVDIm has higher coefficients of correlation with in situ meteorological drought index and agricultural drought statistical data than does the original TVDI, and it exhibits better performance in drought monitoring compared to that of the original TVDI in semi-arid regions of China.
Highlights
Drought is a weather-related natural phenomenon that can cause serious environmental, social, and economic consequences worldwide [1]
The results show that Temperature Vegetation Dryness Index (TVDI) and TVDIm have have statistically significant positive correlations (p-value < 0.05) with drought-affected crop area statistically significant positive correlations (p-value < 0.05) with drought-affected crop area (Figure 13a,b)
The simulation results of TVDIm under different Normalized Difference Vegetation Index (NDVI)/∆Land Surface Temperature (LST) conditions proved that the improved dry/wet edges promote the performance of the TVDI model, especially in areas of complex land cover
Summary
Drought is a weather-related natural phenomenon that can cause serious environmental, social, and economic consequences worldwide [1]. It is considered an insidious natural hazard and ranks first among all natural hazards in terms of the number of people affected [2]. The typical definition of drought is a period with a precipitation deficit that influences agriculture, water resources, Remote Sens. A number of different drought indices were developed during the 20th century in the domains of meteorology, hydrology, agriculture, remote sensing, and water resources management [7].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
