VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing

References

• Adegoke, J. O., and A. M. Carleton. 2002. “Relations Between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt.” Journal of Hydrometeorology 3: 395–405.
   Web of Science ®Google Scholar
• Aldakheel, Y. Y., and F. M. Danson. 1997. “Spectral Reflectance of Dehydrating Leaves: Measurements and Modeling.” International Journal of Remote Sensing 18: 3683–3690.
   Web of Science ®Google Scholar
• Anyamba, A., C. J. Tucker, and J. R. Eastman. 2001. “NDVI Anomaly Patterns Over Africa During the 1997/98 ENSO Warm Event.” International Journal of Remote Sensing 22: 1847–1859.
   Web of Science ®Google Scholar
• Bach, H., and W. Verhoef. 2003. “Sensitivity Studies on the Effect of Surface Soil Moisture on Canopy Reflectance Using the Radiative Transfer Model GeoSAIL.” In Geoscience and Remote Sensing Symposium, IGARSS ‘03, 1679–1681. IEEE International. doi: 10.1109/IGARSS.2003.1294215.
   Google Scholar
• Baumgardner, M. F., L. F. Silva, L. L. Biehl, and E. R. Stoner. 1985. “Reflectance Properties of Soils.” Advances in Agronomy 38: 1–44.
   Web of Science ®Google Scholar
• Bowers, S. A., and R. J. Hanks. 1965. “Reflectance of Radiant Energy from Soils.” Soil Science 100: 130–138.
   Google Scholar
• Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts. 1995. “The Oklahoma Mesonet: A Technical Overview.” Journal of Atmospheric and Oceanic Technology 12: 5–19.
   Web of Science ®Google Scholar
• Brown, J. F., B. D. Wardlow, T. Tadesse, M. J. Hayes, and B. C. Reed. 2008. “The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation.” GIScience Remote Sensing 45: 16–46. doi:10.2747/1548-1603.45.1.16.
   Web of Science ®Google Scholar
• Carter, A. C. 1991. “Primary and Secondary Effects of Water Content on the Spectral Reflectance of Leaves.” American Journal of Botany 78: 916–924.
   Web of Science ®Google Scholar
• Carter, A. C. 1993. “Responses of Leaf Spectral Reflectance to Plant Stress.” American Journal of Botany 80: 239–243.
   Web of Science ®Google Scholar
• Ceccato, P., S. Flasse, S. Tarantola, S. Jacquemoud, and J. Gregoire. 2001. “Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain.” Remote Sensing of Environment 77: 22–33.
   Web of Science ®Google Scholar
• Ceccato, P., N. Gobron, S. Flasse, B. Pinty, and S. Tarantola. 2002. “Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data: Part 1 Theoretical Approach.” Remote Sensing of Environment 82: 188–197.
   Web of Science ®Google Scholar
• Chen, D., J. Huang, and T. J. Jackson. 2005. “Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from Modis Near- and Short-Wave Infrared Bands.” Remote Sensing of Environment 98: 225–236.
   Web of Science ®Google Scholar
• Chen, W., Q. Xiao, and Y. Sheng. 1994. “Application of the Anomaly Vegetation Index to Monitoring Heavy Drought in 1992.” Remote Sensing of Environment 9: 106–112. ( in Chinese).
   Google Scholar
• Chuvieco, E., D. Riãno, I. Aguado, and D. Cocero. 2002. “Estimation of Fuel Moisture Content from Multitemporal Analysis of Landsat Thematic Mapper Reflectance Data: Applications in Fire Danger Assessment.” International Journal of Remote Sensing 23: 2145–2162.
   Web of Science ®Google Scholar
• Dawson, T. P., P. J. Curran, P. R. J. North, and S. E. Plummer. 1999. “The Propagation of Foliar Biochemical Absorption Features in Forest Canopy Reflectance: A Theoretical Analysis.” Remote Sensing of Environment 67: 147–159.
   Web of Science ®Google Scholar
• Di, L., D. C. Rundquist, and L. Han. 1994. “Modelling Relationships Between NDVI and Precipitation During Vegetative Growth Cycles.” International Journal Remote Sensing 15: 2121–2136.
   Web of Science ®Google Scholar
• Dracup, J. A., K. S. Lee, and E. G. J. Paulson. 1980. “On the Definition of Drought.” Water Resources Research 16: 297–302.
   Web of Science ®Google Scholar
• Du, X., S. Wang, Y. Zhou, and H. Wei. 2007. “Construction and Validation of a New Model for Unified Surface Water Capacity Based on MODIS Data.” Geomatics and Information Science of Wuhan University 32: 205–207. ( in Chinese).
   Google Scholar
• Elachi, C.1987. Introduction to the Physics and Techniques of Remote Sensing, 66–71. New York: John Wiley.
   Google Scholar
• Elvidge, C. D. 1990. “Visible and Infrared Reflectance Characteristics of Dry Plant Materials.” International Journal of Remote Sensing 11: 1775–1795.
   Web of Science ®Google Scholar
• Elvidge, C. D., and R. J. P. Lyon. 1985. “Estimation of the Vegetation Contribution to the 1.65/2.22 μm Ratio in Airborne Thematic-Mapper Imagery of the Virginia Range, Nevada.” International Journal of Remote Sensing 6: 75–88.
   Web of Science ®Google Scholar
• FEMA. 1995. “National Mitigation Strategy: Partnerships for Building Safer Communities.” FEMA Mitigation Directorate, 40 p.
   Google Scholar
• Fensholt, R., and I. Sandholt. 2003. “Derivation of a Shortwave Infrared Stress Index from MODIS Near- and Shortwave Infrared Data in a Semiarid Environment.” Remote Sensing of Environment 87: 111−121.
   Web of Science ®Google Scholar
• Gao, B. C. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58: 257–266.
   Web of Science ®Google Scholar
• Ghulam, A., T. M. Kusky, T. Teyip, and Q. Qin. 2011. “Sub-Canopy Soil Moisture Modeling in n-Dimensional Spectral Feature Space.” Photogrammetric Engineering and Remote Sensing 77: 149–156.
   Web of Science ®Google Scholar
• Ghulam, A., Z. Li, Q. Qin, Q. Tong, J. Wang, A. Kasimu, and L. Zhu. 2007a. “A Method for Canopy Water Content Estimation for Highly Vegetated Surfaces-Shortwave Infrared Perpendicular Water Stress Index.” Science in China Series D: Earth Sciences 50: 957–965.
   Google Scholar
• Ghulam, A., Z. Li, Q. Qin, H. Yimit, and J. Wang. 2008a. “Estimating Crop Water Stress with ETM+ NIR and SWIR Data.” Agricultural and Forest Meteorology 148: 1679–1695.
   Web of Science ®Google Scholar
• Ghulam, A., Q. Qin, T. Kuskya, and Z. Li. 2008b. “A Re-Examination of Perpendicular Drought Indices.” International Journal of Remote Sensing 29: 6037–6044. doi:10.1080/01431160802235811.
   Web of Science ®Google Scholar
• Ghulam, A., Q. Qin, T. Teyip, and Z. Li. 2007b. “Modified Perpendicular Drought Index (MPDI): A Real-Time Drought Monitoring Method.” ISPRS Journal of Photogrammetry & Remote Sensing 62: 150–164.
   Web of Science ®Google Scholar
• Ghulam, A., Q. Qin, and Z. Zhan. 2006. “Designing of the Perpendicular Drought Index.” Environmental Geology 52: 1045–1052.
   Web of Science ®Google Scholar
• Gorry, P. A. 1990. “General Least-Squares Smoothing and Differentiation by the Convolution (Savitzky–Golay) Method.” Analytical Chemistry 62: 570–573.
   Web of Science ®Google Scholar
• Gu, Y., J. F. Brown, J. P. Verdin, and B. Wardlow. 2007. “A Five-Year Analysis of MODIS NDVI and NDWI for Grassland Drought Assessment Over the Central Great Plains of the United States.” Geophysical Research Letters 34: L06407.
   Web of Science ®Google Scholar
• Gu, Y., E. Hunt, B. Wardlow, J. B. Basara, J. F. Brown, and J. P. Verdin. 2008. “Evaluation of MODIS NDVI and NDWI for Vegetation Drought Monitoring Using Oklahoma Mesonet Soil Moisture Data.” Geophysical Research Letters 35: L22401.
   Web of Science ®Google Scholar
• Gutman, G.1990. “Towards Monitoring Droughts from Space.” Journal of Climate 2: 282–295.
   Web of Science ®Google Scholar
• Hardisky, M. A., V. Klemas, and R. M. Smart. 1983. “The Influences of Soil Salinity, Growth form, and Leaf Moisture on the Spectral Reflectance of Spartina Alterniflora Canopies.” Photogrammetric Engineering and Remote Sensing 49: 77–83.
   Web of Science ®Google Scholar
• Heim, R. R. 2002. “A Review of Twentieth-Century Drought Indices Used in the United States.” Bulletin of the American Meteorological Society 83: 1149–1165.
   Web of Science ®Google Scholar
• Hunt, E. R. Jr, and B. N. Rock. 1989. “Detection of Changes in Leaf Water Content Using Near and Middle–Infrared Reflectances.” Remote Sensing of Environment 30: 43–54.
   Web of Science ®Google Scholar
• Hunt, E. R. Jr, B. N. Rock, and P. S. Nobel. 1987. “Measurement of Leaf Relative Water Content by Infrared Reflectance.” Remote Sensing of Environment 22: 429–435.
   Web of Science ®Google Scholar
• Illston, B. G., J. B. Basara, and K. C. Crawford. 2004. “Seasonal to Interannual Variations of Soil Moisture Measured in Oklahoma.” International Journal of Climatology 24: 1883–1896.
   Web of Science ®Google Scholar
• Jackson, T. J., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, P. Doriaswamy, and E. R. Hunt. 2004. “Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans.” Remote Sensing of Environment 92: 475–482.
   Web of Science ®Google Scholar
• Jackson, R. D., P. N. Slater, and P. J. Pinter Jr. 1983. “Discrimination of Growth and Water Stress in Wheat by Various Vegetation Indices Through Clear and Turbid Atmospheres.” Remote Sensing of Environment 13: 187–208.
   Web of Science ®Google Scholar
• Jacquemoud, S. 1993. “Inversion of the PROSPECT + SAIL Canopy Reflectance Model from AVIRIS Equivalent Spectra: Theoretical Study.” Remote Sensing of Environment 44: 281–292.
   Web of Science ®Google Scholar
• Jacquemoud, S., and F. Baret. 1990. “PROSPECT: A Model of Leaf Optical Properties Spectra.” Remote Sensing of Environment 34: 75–91.
   Web of Science ®Google Scholar
• Jacquemoud, S., F. Baret, B. Andrieu, F. M. Danson, and K. W. Jaggard. 1995. “Extraction of Vegetation Biophysical Parameters by Inversion of the PROSPECT-SAIL Models on Sugar Beet Canopy Reflectance Data. Application to TM and AVIRIS Sensors.” Remote Sensing of Environment 52: 163–172.
   Web of Science ®Google Scholar
• Javadnia, E., M. R. Mobasheri, and G. A. Kamali. 2009. “MODIS NDVI Quality Enhancement Using ASTER Images.” Journal of Agricultural Science and Technology 11: 549–558.
   Web of Science ®Google Scholar
• Jensen, J. R. 2007. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd ed. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Ji, L., and A. J. Peters. 2003. “Assessing Vegetation Response to Drought in the Northern Great Plains Using Vegetation and Drought Indices.” Remote Sensing of Environment 87: 85–98.
   Web of Science ®Google Scholar
• Johnson, D. M., and R. Mueller. 2010. “The 2009 Cropland Data Layer. ” Photogrammetric Engineering and Remote Sensing 1202–1205. Accessed March 12, 2013. http://www.nass.usda.gov/research/Cropland/docs/JohnsonPE&RS_Nov2010.pdf
   Google Scholar
• Kogan, F. N. 1990. “Remote Sensing of Weather Impacts on Vegetation in non-homogenous areas.” International Journal Remote Sensing 11: 1405–1419.
   Web of Science ®Google Scholar
• Kogan, F. N. 1995. “Application of Vegetation Index and Brightness Temperature for Drought Detection.” Advances in Space Research 15: 91–100.
   PubMed Web of Science ®Google Scholar
• Kogan, F. N. 1997. “Global Drought Watch from Space.” Bulletin of the American Meteorological Society 78: 621–636.
   Web of Science ®Google Scholar
• Kogan, F. N. 2000. “Contribution of Remote Sensing to Drought Early Warning. In Early Warning Systems for Drought Preparedness and Drought Management. Proceedings of the Expert Group Meeting, edited by D. A. Wilhite, M. V. K. Sivakumar, and D. A. Wood, Lisbon. World Meteorological Organization, 86–100. Accessed March 12, 2013. http://www.wamis.org/agm/pubs/agm2/agm02.pdf
   Google Scholar
• Lillesand, T., R. W. Kiefer, and J. W. Chipman. 2008. Remote Sensing and Image Interpretation. 6th ed. 1–60. New York: John Wiley & Sons.
   Google Scholar
• Liu, W., F. Baret, X. Gu, Q. Tong, L. Zheng, and B. Zhang. 2002. “Relating Soil Surface Moisture to Reflectance.” Remote Sensing of Environment 81: 238–246.
   Web of Science ®Google Scholar
• Liu, W., F. Baret, X. Gu, B. Zhang, Q. Tong, and L. Zheng. 2003. “Evaluation of Methods for Soil Surface Moisture Estimation from Reflectance Data.” International Journal of Remote Sensing 24: 2069–2083.
   Web of Science ®Google Scholar
• Lobell, D. B., and G. P. Asner. 2002. “Moisture Effects on Soil Reflectance.” Soil Science Society of America Journal 66(3): 722–27. doi: 10.2136/sssaj2002.7220.
   Web of Science ®Google Scholar
• Maki, M., M. Ishiahra, and M. Tamura. 2004. “Estimation of Leaf Water Status to Monitor the Risk of Forest Fires by Using Remotely Sensed Imagery.” Remote Sensing of Environment 90: 441–450.
   Web of Science ®Google Scholar
• McVicar, T. R., and P. N. Bierwirth. 2001. “Rapidly Assessing the 1997 Drought in Papua New Guinea Using Composite AVHRR Imagery.” International Journal Remote Sensing 22: 2109–2128.
   Web of Science ®Google Scholar
• McVicar, T. R., and D. L. B. Jupp. 1998. “The Current and Potential Operational Uses of Remote Sensing to Aid Decisions on Drought Exceptional Circumstances in Australia: A Review.” Agriculture System 57: 399–468.
   Web of Science ®Google Scholar
• Mishra, A. K., and V. P. Singh. 2010. “A Review of Drought Concepts.” Journal of Hydrology 391: 202–216.
   Google Scholar
• Momeni, M., and M. R. Saradjian. 2007. “Evaluating NDVI-Based Emissivities of MODIS Bands 31 and 32 Using Emissivities Derived by Day/Night LST Algorithm.” Remote Sensing of Environment 106: 190–198.
   Web of Science ®Google Scholar
• Niemeyer, S. 2008. “New Drought Indices.” Options Méditerranéennes, Series A 80: 267–274.
   Google Scholar
• Ollinger, S. V. 2011. “Sources of Variability in Canopy Reflectance and the Convergent Properties of Plants.” New Phytologist 189: 375–394. doi:10.1111/j.1469-8137.2010.03536.x.
   PubMed Web of Science ®Google Scholar
• Phillips, D. 2002. “The Top Ten Canadian Weather Stories for 2001.” CMOS Bull 30: 19–23.
   Google Scholar
• Qin, Q., A. Ghulam, L. Zhu, L. Wang, J. Li, and P. Nan. 2008. “Evaluation of MODIS Derived Perpendicular Drought Index for Estimation of Surface Dryness Over Northwestern China.” International Journal of Remote Sensing 29: 1983–1995. doi:10.1080/01431160701355264.
   PubMed Web of Science ®Google Scholar
• Qin, Q., C. Jin, N. Zhang, and X. Yang. 2010. “An Two-Dimensional Spectral Space Based Model for Drought Monitoring and Its Re-Examination.” International Geoscience and Remote Sensing Symposium (IGARSS 2010) 3869–3872. doi: 10.1109/IGARSS.2010.5649710.
   Google Scholar
• Ripple, W. J. 1986. “Spectral Reflectance Relationships to Leaf Water Stress.” Environmental Sciences 52: 1669–1675.
   Google Scholar
• Roberts, D. A., R. O. Green, and J. B. Adams. 1997. “Temporal and Spatial Patterns in Vegetation and Atmospheric Properties from AVIRIS.” Remote Sensing of Environment 62: 223–240.
   Web of Science ®Google Scholar
• Savitzky, A., and M. J. E. Golay. 1964. “Smoothing and Differentiation of Data by Simplified Least-Squares Procedures.” Analytical Chemistry 36: 1627–1639.
   Web of Science ®Google Scholar
• Schneider, J. M., D. K. Fisher, R. L. Elliott, G. O. Brown, and C. P. Bahrmann. 2003. “Spatiotemporal Variations in Soil Water: First Results from the ARM SGP CART Network.” Journal of Hydrometeorology 4: 106–120.
   Web of Science ®Google Scholar
• Shahabfar, A., A. Ghulam, and J. Eitzinger. 2012. “Drought Monitoring in Iran Using the Perpendicular Drought Indices.” International Journal of Applied Earth Observation and Geoinformation 18: 119–127.
   Web of Science ®Google Scholar
• Sims, D. A., and J. A. Gamon. 2003. “Estimation of Vegetation Water Content and Photosynthetic Tissue Area from Spectral Reflectance: A Comparison of Indices Based on Liquid Water and Chlorophyll Absorption Features.” Remote Sensing of Environment 84: 526–537.
   Web of Science ®Google Scholar
• Sobrino, J. A., N. Raissouni, and Z. Li. 2001. “A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data.” Remote Sensing of Environment 75: 256–266.
   Web of Science ®Google Scholar
• Svoboda, M., D. LeComte, M. Hayes, R. Heim, K. Gleason, J. Angel, B. Rippey, R. Tinker, M. Palecki, D. Stooksbury, D. Miskus, and S. Stephens. 2002. “The Drought Monitor.” Bulletin of the American Meteorological Society 83: 1181–1190.
   Web of Science ®Google Scholar
• Tucker, C. J. 1980. “Remote Sensing of Leaf Water Content in the Near-Infrared.” Remote Sensing of Environment 10: 23–32.
   Web of Science ®Google Scholar
• Wan, Z., P. Wang, and X. Li. 2004. “Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index for Monitoring Drought in the Southern Great Plains, USA.” International Journal of Remote Sensing 25: 61–72.
   Web of Science ®Google Scholar
• Wang, J., K. P. Price, and P. M. Rich. 2001. “Spatial Patterns of NDVI in Response to Precipitation and Temperature in the Central Great Plains.” International Journal Remote Sensing 22: 3827–3844.
   Web of Science ®Google Scholar
• Wang, L., and J. J. Qu. 2007. “NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing.” Geophysical Research Letters 34: L20405. doi:10.1029/2007GL031021.
   Web of Science ®Google Scholar
• Wang, L., and J. J. Qu. 2009. “Satellite Remote Sensing Applications for Surface Soil Moisture Monitoring: A Review.” Frontiers of Earth Science in China 3: 237–247.
   Google Scholar
• Wang, X., H. Xie, H. Guan, and X. Zhou. 2007. “Different Responses of MODIS-Derived NDVI to Root-Zone Soil Moisture in Semi-Arid and Humid Regions.” Journal of Hydrology 340: 12–24.
   Web of Science ®Google Scholar
• Wardlow, B. D., M. C. Anderson, and J. P. Verdin. 2012. Remote Sensing of Drought: Innovative Monitoring Approaches. Boca Raton, FL: CRC Press.
   Google Scholar
• Wheaton, E. E., L. M. Arthur, B. Chorney, C. Shewchuk, J. Thorpe, J. Whitting, and K. Whittrock. 1992. “The Prairie Drought of 1988.” Climatological Bulletin 26: 188–205.
   Google Scholar
• Whiting, M. L., L. Li, and S. L.Ustin. 2004. “Predicting Water Content Using Gaussian Model on Soil Spectra.” Remote Sensing of Environment 89: 535–552.
   Web of Science ®Google Scholar
• Wilhite, D. A., ed. 2000. “Drought as a Natural Hazard: Concepts and Definitions. ” In Drought Volume I: A Global Assessment, 3–18. New York: Routledge.
   Google Scholar
• Xiao, X., D. Hollinger, J. Aber, M. Goltz, E. A. Davidson, Q. Zhang, and B. Moore III. 2004a. “Satellite-Based Modeling of Gross Primary Production in an Evergreen Needleleaf Forest.” Remote Sensing of Environment 89: 519–534.
   Web of Science ®Google Scholar
• Xiao, X., Q. Zhang, B. Braswell, S. Urbanski, S. Boles, S. C. Wofsy, B. Moore III, and D. Ojima. 2004b. “Modeling Gross Primary Production of Temperate Deciduous Broadleaf Forest Using Satellite Images and Climate Data.” Remote Sensing of Environment 91: 256–270.
   Web of Science ®Google Scholar
• Yang, N., Q. Qin, C. Jin, and Y. Yao. 2008. “The Comparison and Application of the Methods for Monitoring Farmland Drought Based on NIR-Red Spectral Space.” International Geoscience and Remote Sensing Symposium (IGARSS 2008) III871–III874. doi: 10.1109/IGARSS.2008.4779488.
   Google Scholar
• Yu, G., T. Miwa, K. Nakayama, N. Matsuoka, and H. Kon. 2000. “A Proposal for Universal Formulas for Estimating Leaf Water Status of Herbaceous and Woody Plants Based on Spectral Reflectance Properties.” Plant Soil 227: 47–58.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J., C. A. Rueda, and S. L. Ustin. 2003. “Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods.” Remote Sensing of Environment 85: 109–124.
   Web of Science ®Google Scholar
• Zhang, H., H. Chen, S. Shen, G. Zhou, and W. Yu. 2008. “Drought Remote Sensing Monitoring Based on the Surface Water Content Index (SWCI) Method.” Remote Sensing Technology and Application 23: 624–628. ( in Chinese)
   Google Scholar
• Zhang, J., Y. Xu, F. Yao, P. Wang, W. Guo, L. Li, and L. Yang. 2010. “Advances in Estimation Methods of Vegetation Water Content Based on Optical Remote Sensing Techniques.” Science China-Technological Sciences 53: 1159–1167.
   Web of Science ®Google Scholar
• Zhao, S., Q. Qin, L. You, Y. Yao, N. Yang, and J. Li. 2009. “Application of Two Shortwave Infrared Water Stress Indices to Drought Monitoring Over Northwestern China.” Geoscience and Remote Sensing Symposium(IGARSS 2009) III-530–III-533. doi: 10.1109/IGARSS.2009.5417809.
   Google Scholar
• Zygielbaum, A. I., A. A. Gitelson, T. J. Arkebauer, and D. C. Rundquist. 2009. “Non-Destructive Detection of Water Stress and Estimation of Relative Water Content in Maize.” Geophysical Research Letters 36: L12403.
   Web of Science ®Google Scholar