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)

Traditional NDVI-Ts space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-Ts 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 Ts data to reexamine if the NDVI-Ts 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-Ts 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 Ts and NDVI in the Shaanxi province, which was linear in the triangular NDVI-Ts space. The Temperature Vegetation Dryness Index (TVDIc) was obtained from bi-parabolic NDVI-Ts andTVDIt was obtained from the triangular NDVI-Ts space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDIc was better than TVDIt, which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDIc 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.

[1]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[2]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[3]  Li Li,et al.  Mapping MODIS LST NDVI Imagery for Drought Monitoring in Punjab Pakistan , 2018, IEEE Access.

[4]  T. Carlson,et al.  Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models , 1995 .

[5]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[6]  S. Goward,et al.  Estimation of air temperature from remotely sensed surface observations , 1997 .

[7]  Bahram Salehi,et al.  Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) , 2017 .

[8]  K. Omasa,et al.  Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran , 2012 .

[9]  Hongjie Xie,et al.  Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions , 2007 .

[10]  Amaury Tilmant,et al.  A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation , 2016, Remote. Sens..

[11]  Jiancang Xie,et al.  Use of four drought indices for evaluating drought characteristics under climate change in Shaanxi, China: 1951–2012 , 2015, Natural Hazards.

[12]  J. C. Price Using spatial context in satellite data to infer regional scale evapotranspiration , 1990 .

[13]  Miao Tian,et al.  Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain , 2016, Remote. Sens..

[14]  S. Running,et al.  Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data , 1989 .

[15]  Yasushi Yamaguchi,et al.  Vegetation, water and thermal stress index for study of drought in Nepal and central northeastern India , 2010 .

[16]  Aifeng Lv,et al.  A time domain solution of the Modified Temperature Vegetation Dryness Index (MTVDI) for continuous soil moisture monitoring , 2017 .

[17]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[18]  W. Oechel,et al.  Variability of the Seasonally Integrated Normalized Difference Vegetation Index Across the North Slope of Alaska in the 1990s , 2003 .

[19]  Hui Yue,et al.  Biparabolic NDVI-Ts Space and Soil Moisture Remote Sensing in an Arid and Semi arid Area , 2015 .

[20]  M. S. Moran,et al.  Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .

[21]  Reza Jafari,et al.  Assessment of PDI, MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level 1B data in natural lands , 2017, Natural Hazards.

[22]  Eric F. Lambin,et al.  Mapping forest-cover disturbances in Papua New Guinea with AVHRR data , 1996 .

[23]  Qiming Qin,et al.  Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China , 2008 .

[24]  Ke Liu,et al.  Comparison of Two Simulation Methods of the Temperature Vegetation Dryness Index (TVDI) for Drought Monitoring in Semi-Arid Regions of China , 2017, Remote. Sens..

[25]  F. Kogan Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data , 1995 .

[26]  Zhe Wang,et al.  The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003-2010). , 2017 .

[27]  Xiaoming Cao,et al.  An improvement of the Ts-NDVI space drought monitoring method and its applications in the Mongolian plateau with MODIS, 2000–2012 , 2016, Arabian Journal of Geosciences.

[28]  S. Quiring,et al.  Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas , 2010 .

[29]  V. K. Dadhwal,et al.  Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data , 2012, Environmental Monitoring and Assessment.

[30]  Jeremy Adler,et al.  Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander's overlap coefficient , 2010, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[31]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[32]  Ramesh P. Singh,et al.  Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India , 2003 .

[33]  H. Liniger,et al.  Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia , 2015 .

[34]  Lloyd L. Coulter,et al.  Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery , 2016 .

[35]  Samuel N. Goward,et al.  Evapotranspiration from combined reflected solar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data , 1989 .

[36]  Tim R. McVicar,et al.  Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery , 2001 .

[37]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[38]  F. Kogan Application of vegetation index and brightness temperature for drought detection , 1995 .

[39]  D. Dutta,et al.  Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) , 2015 .

[40]  Yi Xie,et al.  An Up-Scaled Vegetation Temperature Condition Index Retrieved From Landsat Data with Trend Surface Analysis , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  Xiaoming Cao,et al.  Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981–2012 , 2017, Journal of Earth System Science.

[42]  V. K. Dadhwal,et al.  Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status , 2009 .

[43]  S. M. Jong,et al.  Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing , 2009 .

[44]  Samuel N. Goward,et al.  Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape , 1985 .

[45]  Antonio Motisi,et al.  A time domain triangle method approach to estimate actual evapotranspiration: Application in a Mediterranean region using MODIS and MSG-SEVIRI products , 2016 .

[46]  Samuel N. Goward,et al.  Simulated Relationships Between Spectral Reflectance, Thermal Emissions, and Evapotranspiration of a Soybean Canopy , 1986 .