Advertisement

Journal of Mountain Science

, Volume 14, Issue 11, pp 2284–2294 | Cite as

An improved temperature vegetation dryness index (iTVDI) and its applicability to drought monitoring

  • Ruo-wen Yang
  • Hai Wang
  • Jin-ming Hu
  • Jie CaoEmail author
  • Yu Yang
Article
  • 78 Downloads

Abstract

Using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the dry season during 2010–2012 over the whole Yunnan Province, an improved temperature vegetation dryness index (iTVDI), in which a parabolic dry-edge equation replaces the traditional linear dry-edge equation, was developed, to reveal the regional drought regime in the dry season. After calculating the correlation coefficient, root-mean-square error, and standard deviation between the iTVDI and observed topsoil moisture at 10 and 20 cm for seven sites, the effectiveness of the new index in depicting topsoil moisture conditions was verified. The drought area indicated by iTVDI mapping was then compared with the drought-affected area reported by the local government. The results indicated that the iTVDI can monitor drought more accurately than the traditional TVDI during the dry season in Yunnan Province. Using iTVDI facilitates drought warning and irrigation scheduling, and the expectation is that this new index can be broadly applied in other areas.

Keywords

Improved temperature vegetation dryness index (iTVDI) Drought monitoring Linear dry-edge equation Parabolic dry-edge equation Soil moisture 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2016YFA0601601), National Natural Science Foundation of China (Grants Nos. U1502233, 41405001), the Jiangsu Collaborative Innovation Center for Climate Change and Ph.D. Programs Foundation of Ministry of Education of China (20135301120010).

References

  1. Cai GY, Xue Y, Hu Y C (2007) Soil Moisture Retrieval from MODIS data in Northern China Plain using thermal inertia Model. International Journal of Remote Sensing 28(16): 3567–3581. https://doi.org/10.1080/01431160601034886CrossRefGoogle Scholar
  2. Cao J, Hu JM, Tao Y (2012) An index for the interface between the Indian summer monsoon and the East Asian summer monsoon. Journal of Geophysical Research 117(D18): 119–130. https://doi.org/10.1029/2012JD017841CrossRefGoogle Scholar
  3. Cao J, Yao P, Wang L, et al. (2014) Summer rainfall variability in low-latitude highlands of China and subtropical Indian Ocean Dipole. Journal of Climate 27(2):880–892.CrossRefGoogle Scholar
  4. Cao X, Feng Y, Wang J (2016) An improvement of the Ts-NDVI space drought monitoring method and its applications in the Mongolian plateau with MODIS, 2000–2012. Arabian Journal of Geosciences 9(6): 1–14.CrossRefGoogle Scholar
  5. Carlson TN, Dodd JK, Benjamin SG, et al. (1981) Satellite estimation of the surface energy balance, moisture availability and thermal inertia. Journal of Applied Meteorology 20(1): 67–87. https://doi.org/10.1175/1520-0450(1981)020<0067: SEOTSE>2.0.CO;2CrossRefGoogle Scholar
  6. Carlson TN, Buffum MJ (1989) On estimating the total daily evapotranspiration from remote surface temperature measurements. Remote Sensing Environment 29(2): 197–207. https://doi.org/10.1016/0034-4257(89)90027-8CrossRefGoogle Scholar
  7. Carlson TN, Perry EM, Schmugge TJ (1990) Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agricultural and Forest Meteorology 52(90):45–69.https://doi.org/10.1016/0168-1923(90)90100-KCrossRefGoogle Scholar
  8. Carlson TN, Capehart WJ, Gilies RR (1995a) A new look at the simplified method for remote sensing of daily evapotranspiration, Remote Sensing of Environment 54(2): 161–167. https://doi.org/10.1016/0034-4257(95)00139-RCrossRefGoogle Scholar
  9. Carlson TN, Gillies RR, Schmugge TJ (1995b) An interpretation of methodologies for indirect measurement of soil water content. Agricultural and Forest Meteorology 77(3): 191–205. https://doi.org/10.1016/0168-1923(95)02261-UCrossRefGoogle Scholar
  10. Carlson TN (2007) An overview of the ‘triangle method’ for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors 7(8): 1612–1629. https://doi.org/ 10.3390/s7081612CrossRefGoogle Scholar
  11. Chen CF, Son NT, Chang LY, et al. (2011) Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Applied Geography 31(2): 463–475. https://doi.org/10.1016/j.apgeog. 2010.10.002CrossRefGoogle Scholar
  12. Dalezios NR, Blanta A, Spyropoulos N (2013) Remotely sensed spatiotemporal features of agrometeorological drought, in Advances in Meteorology. Climatology and Atmospheric Physics 409–414. https://doi.org/10.1007/978-3-642-29172-2_58CrossRefGoogle Scholar
  13. Alyaari A, Wigneron JP, Ducharne A, et al. (2014) Global-scale evaluation of two satellite-based passive microwave soil moisture data sets (SMOS and AMSR-E) with respect to modelled estimates. EGU General Assembly Conference. EGU General Assembly Conference Abstracts. pp 181–195.Google Scholar
  14. Gao ZQ, Gao W, Chang NB (2011) Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. International Journal of Applied Earth Observation and Geoinformation 13(3): 495–503. https://doi.org/10.1039/c39940002047CrossRefGoogle Scholar
  15. Gillies RR, Carlson TN (1995) Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology 34(4): 745–756. https://doi.org/10.1175/1520- 0450(1995)034<0745:TRSOSS>2.0.CO;2CrossRefGoogle Scholar
  16. Gillies RR, Carlson TN, Cui J, et al. (1997) Verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index NDVI and surface radiant temperature. International Journal of Remote Sensing 18(15): 3145–3166. https://doi.org/10.1080/014311697217026CrossRefGoogle Scholar
  17. Goetz SJ (1997) Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing 18(1): 71–94. https://doi.org/10.1080/014311697219286CrossRefGoogle Scholar
  18. Goward SN, Xue Y, Czajkowski KP (2002) Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: an exploration with the simplified simple biosphere model. Remote Sensing of Environment 79(2–3): 225–242. https://doi.org/10.1016/S0034-4257(01)00275-9CrossRefGoogle Scholar
  19. Jeong SJ, Ho CH, Jeong JH (2009) Increase in vegetation greenness and decrease in springtime warming over east Asia. Geophysical Research Letters 36(2):436–448. https://doi.org/ 10.1029/2008GL036583CrossRefGoogle Scholar
  20. Kaufmann RK, Zhou L, Myneni RB, et al. (2003) The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. Geophysical Research Letters 30(22). https://doi.org/10.1029/2003GL018251Google Scholar
  21. Kerr YH, Waldteufel P, Wigneron JP, et al. (2010) The SMOS mission: New tool for monitoring key elements of the global water cycle. Proceedings of the IEEE 98(5): 666–687.CrossRefGoogle Scholar
  22. Kogan FN (1990) Remote sensing of weather impacts on vegetation in nonhomogeneous area. International Journal of Remote Sensing 11(8): 1405–1420. https://doi.org/10.1080/ 01431169008955102CrossRefGoogle Scholar
  23. Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15(11): 91–100.CrossRefGoogle Scholar
  24. Lambin EF, Ehrlich D (1996) The surface temperaturevegetation index space for land cover and land cover-change analysis. International Journal of Remote Sensing 17(3): 463–487. https://doi.org/10.1080/01431169608949021CrossRefGoogle Scholar
  25. Liu ZY, Notaro M, Kutzbach J (2006) Assessing global vegetation-climate feedbacks from observation. Journal of climate 19(5): 787–814. https://doi.org/10.1175/JCLI3658.1CrossRefGoogle Scholar
  26. Mcffters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7): 1425–1143.CrossRefGoogle Scholar
  27. Moran MS, Clarke TR, Inoue Y, et al. (1994) Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment 49(3): 246–263. https://doi.org/10.1016/0034-4257(94)90020-5CrossRefGoogle Scholar
  28. Moran MS, Rahman AF, Washburne JC, et al. (1996) Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semi-arid grassland. Agricultural and Forest Meteorology 80(2–4): 87–109. https://doi.org/10.1016/0168-1923(95)02292-9CrossRefGoogle Scholar
  29. Murray T, Verhoef A (2007) Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements: I. A universal approach to calculate thermal inertia. Agricultural and Forest Meteorology 147(1): 80–87. https://doi.org/10.1016/j.agrformet.2007.07.004CrossRefGoogle Scholar
  30. Nemani R, Pierce L, Running S, et al. (1993) Developing satellite-derived estimates of surface moisture status. Journal of Applied Meteorology 2(3): 548–557.CrossRefGoogle Scholar
  31. Nishida K, Nemani RR, Glassy J, et al. (2003a) Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status. IEEE Transactions on Geoscience and Remote Sensing 41(2): 493–501. https://doi.org/10.1109/TGRS.2003.811744CrossRefGoogle Scholar
  32. Nishida K, Nemani RR, Running SW, et al. (2003b) An operational remote sensing algorithm of land surface evaporation. Journal of Geophysical Research 108(108):469–474. https://doi.org/10.1029/2002JD002062Google Scholar
  33. Patel NR, Anapashsha R, Kumar S, et al. (2009) Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status. International Journal of Remote Sensing 30(1): 23–39. https://doi.org/10.1080/01431160802108497CrossRefGoogle Scholar
  34. Ridd M (1995) Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy of cities. International Journal of Remote Sensing 16(12): 2165–2185. https://doi.org/10.1080/01431169508954549CrossRefGoogle Scholar
  35. Rahimzadeh BP, Omasa K, Shimizum Y (2012) Comparative evaluation of the vegetation dryness index (VDI), the temperature vegetation dryness index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semiarid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing 68:1–12.CrossRefGoogle Scholar
  36. Rouse J, Haas R, Schell J, et al. (1974) Monitoring Vegetation Systems in the Great Plains with ERTS. Nasa Special Publication 351: 309.Google Scholar
  37. Sandholt I, Rasmussen K, Andersen J (2002) A simple interpretation of the surface temperature/vegetation index space for assessment of soil moisture status. Remote Sensing of Environment 79(2–3): 213–224. https://doi.org/10.1016/ S0034-4257(01)00274-7CrossRefGoogle Scholar
  38. Schwartz MD (1996) Examining the spring discontinuity in daily temperature ranges. Journal of Climate 9(9): 803–808. https://doi.org/10.1175/1520-0442(1996)009<0803:ETSDID >2.0.CO;2CrossRefGoogle Scholar
  39. Son NT, Chen CF, Chen CR, et al. (2012) Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation 18(1): 417–427. https://doi.org/10.1016/j.jag. 2012.03.014CrossRefGoogle Scholar
  40. Sun D, Kafatos M (2007) Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America. Geophysical Research Letters 34(24): 497–507. https://doi.org/10.1029/2007GL031485CrossRefGoogle Scholar
  41. Sun YJ, Wang JF, Zhang RH, et al. (2005) Air temperature retrieval from remote sensing data based on thermodynamics. Theoretical and Applied Climatology 80(1): 37–48. https://doi.org/10.1007/s00704-004-0079-yCrossRefGoogle Scholar
  42. Tao Y, Cao J, Hu JM, et al. (2013) A cusp catastrophe model of mid–long-term landslide evolution over low latitude highlands of China. Geomorphology 187(5): 80–85. https://doi.org/10.1016/j.geomorph.2012.12.036CrossRefGoogle Scholar
  43. Thiruvengadachari S, Gopalkrishna HR (1993) An integrated PC environment for assessment of drought. International Journal of Remote Sensing 14(17): 3201–3208. https://doi.org/ 10.1080/01431169308904434CrossRefGoogle Scholar
  44. Wang C, Qi S, Niu Z, et al. (2004) Evaluating soil moisture status in China using the temperature–vegetation dryness index (TVDI). Canadian Journal of Remote Sensing 30(5): 671–679. https://doi.org/10.5589/m04-029CrossRefGoogle Scholar
  45. Yuan L, Tao HP, Wu H (2007) Dynamic drought monitoring in Guangxi using revised temperature vegetation dryness index. Wuhan University Journal of Natural Sciences 12(4): 663–668. https://doi.org/10.1007/s11859-006-0315-7CrossRefGoogle Scholar
  46. Zhang F, Zhang LW, Wang XZ, et al. (2013) Detecting Agro-Droughts in Southwest of China Using MODIS Satellite Data. Journal of Integrative Agriculture 12(1): 159–168. https://doi. org/10.1016/S2095-3119(13)60216-6CrossRefGoogle Scholar
  47. Qin GR (2013) Four years continuous drought disaster in Yunnan Province make us do more efforts in water conservancy. (In Chinese) http://www.chinanews.com/ ny/2013/04-22/4752209.shtml (Accessed on 22 April 2013)Google Scholar
  48. Didan K (2015) MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1km SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/modis/mod13a3.006Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesYunnan UniversityKunmingChina
  2. 2.Yunnan Key Laboratory of International Rivers and Transboundary Eco-securityKunmingChina
  3. 3.Hubei Province Meteorological BureauWuhanChina

Personalised recommendations