Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results

References

• Agriculture and Agri-Food Canada (AAFC), 2016, The Canadian Drought Monitor [http://www.agr.gc.ca/eng/programs-and-services/list-of-programs-and-services/drought-watch/canadian-drought-monitor/?id=1463575104513], accessed June 27, 2016.
   Google Scholar
• Anderson, M. C., W. P. Kustas, J. M. Norman, C. R. Hain, J. R. Mecikalski, L. Schultz, M. P. González-Dugo, et al. 2010. “Mapping Daily Evapotranspiration at Field to Global Scales Using Geostationary and Polar Orbiting Satellite Imagery.” Hydrol. Earth Syst. Sci. Discuss 7: 5957–5990. doi:10.5194/hessd-7-5957-2010.
   Google Scholar
• Anderson, M. C., C. A. Zolin, P. C. Sentelhas, C. R. Hain, K. Semmens, M. Tugrul Yilmaz, F. Gao, J. A. Otkin, and R. Tetrault. 2016. ““The Evaporative Stress Index as an Indicator of Agricultural Drought in Brazil: An Assessment Based on Crop Yield Impacts.” Remote Sensing of Environment 174: 82–99. doi:10.1016/j.rse.2015.11.034.
   Web of Science ®Google Scholar
• Anderson, W. B., B. F. Zaitchik, C. R. Hain, M. C. Anderson, M. T. Yilmaz, J. Mecikalski, and L. Schultz. 2012. “Towards an Integrated Soil Moisture Drought Monitor for East Africa.” Hydrol. Earth Syst. Sci 16: 2893–2913. doi:10.5194/hess-16-2893-2012.
   Web of Science ®Google Scholar
• Asner, G. P., A. R. Townsend, and B. H. Braswell. 2000. “Satellite Observation of El Niño Effects on Amazon Forest Phenology and Productivity.” Geophysical Research Letters 27 (7): 981–984. doi:10.1029/1999GL011113.
   Web of Science ®Google Scholar
• Baret, F., and G. Guyot. 1991. “Potentials and Limits to Vegetation Indices for LAI and APAR Assessments.” Remote Sensing of Environment 35: 161–173. doi:10.1016/0034-4257(91)90009-U.
   Web of Science ®Google Scholar
• Bayarjargala, Y., A. Karnieli, M. Bayasgalan, S. Khudulmurb, C. Gandush, and C. J. Tucker. 2006. “A Comparative Study of NOAA–AVHRR Derived Drought Indices Using Change Vector Analysis.” Remote Sensing of Environment 105 (1): 9–22. doi:10.1016/j.rse.2006.06.003.
   Web of Science ®Google Scholar
• Braun, P., H. Balzter, and W. Kohler. 2000. “Analyzing Spatiotemporal Dynamics of Plant Populations and Vegetation.” In Quantifying Spatial Uncertainty in Natural Resources: Theory and Applications for GIS and Remote Sensing, Eds. H. T. Mowrer and R. G. Congalton, 203–211. Boca Raton, FL: CRC Press.
   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 and Remote Sensing 45 (1): 16–46. doi:10.2747/1548-1603.45.1.16.
   Web of Science ®Google Scholar
• Champagne, C., A. Berg, H. McNairn, G. Drewitt, and T. Huffman. 2012. “Evaluation of Soil Moisture Extremes over Agricultural Regions in the Canadian Prairies.” Agricultural and Forest Meteorology 165: 1–11. doi:10.1016/j.agrformet.2012.06.003.
   Web of Science ®Google Scholar
• Champagne, C., A. Davidson, P. Cherneski, J. L’Heureux, and T. Hadwen. 2015. “Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS.” Journal of Hydrometeorology 16: 5–18. doi:10.1175/JHM-D-14-0039.1.
   Web of Science ®Google Scholar
• Chipanshi, A., Y. Zhang, L. Kouadio, N. Newlands, A. Davidson, H. Hill, R. Warren, et al. 2015. “Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) Model for In-Season Prediction of Crop Yield across the Canadian Agricultural Landscape.” Agricultural and Forest Meteorology 206: 137–150. doi:10.1016/j.agrformet.2015.03.007.
   Web of Science ®Google Scholar
• Chipanshi, A. C., R. T. Warren, J. L’Heureux, D. Waldner, H. McLean, and D. Qi. 2013. “Use of the National Drought Model (NDM) in Monitoring Selected Agroclimatic Risks across the Agricultural Landscape of Canada.” Atmosphere - Ocean 51: 471–488. doi:10.1080/07055900.2013.835253.
   Web of Science ®Google Scholar
• Churkina, G., S. W. Running, and A. L. Schloss. 1999. “Comparing Global Models of Terrestrial Net Primary Productivity (NPP): The Importance of Water Availability.” Global Change Biology 5 (Suppl. 1): 46–55. doi:10.1046/j.1365-2486.1999.00006.x.
   Web of Science ®Google Scholar
• Commission for Environmental Cooperation (CEC), 2016, Land Cover, 2010 [http://www.cec.org/tools-and-resources/map-files/land-cover-2010], accessed 30 June 2016
   Google Scholar
• Davidson, A., 2015, “An Operational Canadian Ag-Land Monitoring System (CALMS): Near-Real-Time Agricultural Assessment from Space,” Agriculture and Agri-Food Canada. 60 pp.
   Google Scholar
• De’ath, G., and K. E. Fabricius. 2000. “Classification and Regression Trees—A Powerful yet Simple Technique for Ecological Data Analysis.” Ecology 81 (11): 3178–3192. doi:10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2.
   Web of Science ®Google Scholar
• DeBeurs, K. M., and G. M. Henebry. 2004. “Land Surface Phenology, Climatic Variation, and Institutional Change: Analyzing Agricultural Land Cover Change in Kazakhstan.” Remote Sensing of Environment 89 (4): 497–509. doi:10.1016/j.rse.2003.11.006.
   Web of Science ®Google Scholar
• Du, L., Q. Tian, T. Yu, Q. Meng, T. Jancso, P. Udvardy, and Y. Huang. 2013. “A Comprehensive Drought Monitoring Method Integrating MODIS and TRMM Data.” Int. J. Appl. Earth Obs. Geoinf 23: 245–253. doi:10.1016/j.jag.2012.09.010.
   Web of Science ®Google Scholar
• Eidenshink, J. C. 2006. “A 16-Year Time Series of 1 Km AVHRR Satellite Data of the Conterminous United States and Alaska.” Photogrammetric Engineering and Remote Sensing 72: 1027–1035. doi:10.14358/PERS.72.9.1027.
   Web of Science ®Google Scholar
• Ek, M. B. 2003. “Implementation of Noah Land Surface Model Advances in the National Centers for Environmental Prediction Operational Mesoscale Eta Model.” J. Geophys. Res 108: 8851. doi:10.1029/2002JD003296.
   Web of Science ®Google Scholar
• Environment Canada, 2016, Historical Data [http://climate.weather.gc.ca/historical_data/search_historic_data_e.html], accessed June 27, 2016.
   Google Scholar
• Fisette, T., P. Rollin, Z. Aly, L. Campbell, B. Daneshfar, P. Filyer, A. Smith, A. Davidson, J. Shang, and I. Jarvis, 2013, “AAFC Annual Crop Inventory: Status and Challenges,” The Second International Conference on Agro-Geoinformatics 12–16 August 2013, Fairfax, VA, USA.
   Google Scholar
• Fry, J., G. Xian, S. Jin, J. Dewitz, C. Homer, L. Yang, C. Barnes, N. Herold, and J. Wickham. 2011. “Completion of the 2006 National Land Cover Database for the Conterminous United States.” Photogrammetric Engineering and Remote Sensing 77 (9): 858–864.
   Google Scholar
• Funk, C. C., and M. E. Brown. 2006. “Intra-Seasonal NDVI Change Projections in Semi-Arid Africa.” Remote Sensing of Environment 101: 249–256. doi:10.1016/j.rse.2005.12.014.
   Web of Science ®Google Scholar
• Gallo, K., L. Ji, B. C. Reed, J. Dwyer, and J. Eidenshink. 2004. “Comparison of MODIS and AVHRR 16-Day Normalized Difference Vegetation Index Composite Data.” Geophysical Research Letters 31. doi:10.1029/2003GL019385.
   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. doi:10.1016/S0034-4257(96)00067-3.
   Web of Science ®Google Scholar
• Gao, M., Z. Qin, H. Zhang, L. Lu, X. Zhou, and X. Yang. 2008. “Remote Sensing of Agro-Droughts in Guangdong Province of China Using MODIS Satellite Data.” Sensors, no. 8: 4687–4708. doi:10.3390/s8084687.
   PubMed Web of Science ®Google Scholar
• Gitelson, A. A. 2004. “Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation.” Journal of Plant Physiology 161: 165–173. doi:10.1078/0176-1617-01176.
   PubMed 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 (6): 1–6. doi:10.1029/2006GL029127.
   Web of Science ®Google Scholar
• Hao, Z., and A. AghaKouchak. 2013. “Multivariate Standardized Drought Index: A Parametric Multi-Index Model.” Adv. Water Resour 57: 12–18. doi:10.1016/j.advwatres.2013.03.009.
   Web of Science ®Google Scholar
• Hayes, M. J., D. A. Wilhite, M. Svoboda, and O. Vanyarkho. 1999. “Monitoring the 1996 Drought Using the Standardized Precipitation Index.” Bulletin of the American Meteorological Society 80 (3): 429–438. doi:10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2.
   Web of Science ®Google Scholar
• Huete, A. R., K. Didan, Y. E. Shimabukuro, P. Ratana, S. R. Saleska, L. R. Hutyra, W. Yang, R. R. Nemani, and R. Myneni. 2006. “Amazon Rainforests Green-Up with Sunlight in Dry Season.” Geophysical Research Letters 33: L06405. doi:10.1029/2005GL025583.
   Web of Science ®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. doi:10.1016/S0034-4257(03)00174-3.
   Web of Science ®Google Scholar
• Justice, C. O., M. O. Román, I. Csiszar, E. F. Vermote, R. E. Wolfe, S. J. Hook, M. Friedl, et al. 2013. “Land and Cryosphere Products from Suomi NPP VIIRS: Overview and Status.” Journal of Geophysical Research D: Atmospheres 118 (17): 9753–9765.
   PubMed Web of Science ®Google Scholar
• Kogan, F. N. 1990. “Remote Sensing of Weather Impacts on Vegetation in Non-Homogeneous Areas.” International Journal of Remote Sensing 11 (8): 1405–1419. doi:10.1080/01431169008955102.
   Web of Science ®Google Scholar
• Kouadio, L., N. K. Newlands, A. Davidson, Y. Zhang, and A. Chipanshi. 2014. “Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale.” Remote Sensing 6: 10193–10214. doi:10.3390/rs61010193.
   Web of Science ®Google Scholar
• Lelyk, G., 2012, Canadian Soil Data, Global Soil Map and Soil Survey Uncertainty Workshop, Lincoln, NE, August 29, 2012 [ftp://ftp-fc.sc.egov.usda.gov/NSSC/global_soil_map/uncertainty/Canada.pdf], accessed 30 June 2016.
   Google Scholar
• Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, and L. Yang. 2000. “Development of a Global Land Cover Characteristics Database and IGBP Discover from 1 Km AVHRR Data.” International Journal of Remote Sensing 21 (6–7): 1303–1330. doi:10.1080/014311600210191.
   Web of Science ®Google Scholar
• Lunetta, R. S., J. F. Knight, J. Ediriwickrema, J. G. Lyon, and L. D. Worthy. 2006. “Land-Cover Change Detection Using Multi-Temporal MODIS NDVI Data.” Remote Sensing of Environment 105: 142–154. doi:10.1016/j.rse.2006.06.018.
   Web of Science ®Google Scholar
• MathWorks, 2016, Detrending Data [http://www.mathworks.com/help/matlab/data_analysis/detrending-data.html?requestedDomain=www.mathworks.com], accessed June 26, 2016.
   Google Scholar
• McKee, T. B., N. J. Doesken, and J. Kleist, 1995, “Drought Monitoring with Multiple Time Scales, Preprints,” 9th Conference on Applied Climatology, January 15–20, Dallas, Texas, 233–236.
   Google Scholar
• Mkhabela, M. S., P. Bullock, S. Raj, S. Wang, and Y. Yang. 2011. “Crop Yield Forecasting on the Canadian Prairies Using MODIS NDVI Data.” Agricultural and Forest Meteorology 151: 385–393. doi:10.1016/j.agrformet.2010.11.012.
   Web of Science ®Google Scholar
• Omernik, J. M. 1987. “Ecoregions of the Conterminous United States.” Annals of the Association of American Geographers 77 (1): 118–125. doi:10.1111/j.1467-8306.1987.tb00149.x.
   Web of Science ®Google Scholar
• Omernik, J. M. 1995. “Ecoregions: A Spatial Framework for Environmental Management.” In Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making, Eds. W. Davis and T. Simon, 49–62. Boca Raton, FL: Lewis Publishers.
   Google Scholar
• Palmer, W. C. 1965. Meteorological Drought. Research Paper No. 45, 58. Washington, D.C: U.S. Department of Commerce Weather Bureau.
   Google Scholar
• Pervez, M. S., and J. F. Brown. 2010. “Mapping Irrigated Lands at 250-M Scale by Merging MODIS Data and National Agricultural Statistics.” Remote Sensing 2 (10): 2388–2412. doi:10.3390/rs2102388.
   Web of Science ®Google Scholar
• Pouliot, D., R. Latifovic, N. Zabcic, L. Guindon, and I. Olthof. 2014. “Development and Assessment of a 250m Spatial Resolution MODIS Annual Land Cover Time Series (2000-2011) for Canada Derived from Change Based Updating.” Remote Sensing of Environment 140: 731–743. doi:10.1016/j.rse.2013.10.004.
   Web of Science ®Google Scholar
• Quinlan, J. R. 1993. C4.5 Programs for Machine Learning, 302. San Mateo, CA: Morgan Kaufmann Publishers.
   Google Scholar
• Reed, B. C., T. R. Loveland, and L. L. Tieszen. 1996. “An Approach for Using AVHRR Data to Monitor U.S. Great Plains Grasslands.” Geocarto International 11 (3): 13–22. doi:10.1080/10106049609354544.
   Google Scholar
• Rhee, J., J. Im, and G. J. Carbone. 2010. “Monitoring Agricultural Drought for Arid and Humid Regions Using Multi-Sensor Remote Sensing Data.” Remote Sens. Environ 114: 2875–2887. doi:10.1016/j.rse.2010.07.005.
   Web of Science ®Google Scholar
• Rulequest, 2016, Data Mining with Cubist [https://www.rulequest.com/cubist-info.html], accessed June 26, 2016.
   Google Scholar
• Schwartz, M. D., R. Ahas, and A. Aasa. 2006. “Onset of Spring Starting Earlier across the Northern Hemisphere.” Global Change Biology 12: 343–351. doi:10.1111/gcb.2006.12.issue-2.
   Web of Science ®Google Scholar
• Shabanov, N., M. Vargas, T. Miura, A. Sei, and A. Danial. 2015. “Evaluation of the Performance of Suomi NPP VIIRS Top of Canopy Vegetation Indices over AERONET Sites.” Remote Sensing of Environment 162: 29–44. doi:10.1016/j.rse.2015.02.004.
   Web of Science ®Google Scholar
• Soil Landscapes of Canada Working Group, 2010, Soil Landscapes of Canada Version 3.2, Agriculture and Agri-Food Canada [http://sis.agr.gc.ca/cansis/index.html], accessed June 26, 2016.
   Google Scholar
• Statistics Canada, 2012, The 2011 Census of Agriculture [http://www.statcan.gc.ca/daily-quotidien/120510/dq120510a-eng.htm], accessed June 20, 2016.
   Google Scholar
• Statistics Canada, 2016, “Canola: A Canadian Success Story” [http://www.statcan.gc.ca/pub/96-325-x/2007000/article/10778-eng.htm], accessed June 26, 2016.
   Google Scholar
• Svoboda, M., M. J. Hayes, D. Wilhite, and T. Tadesse, 2004, “Recent Advances in Drought Monitoring,” Proceedings of the 14th Conference on Applied Climatology, Seattle, WA [https://ams.confex.com/ams/84Annual/techprogram/paper_72976.htm], accessed June 26, 2016.
   Google Scholar
• Svoboda, M., D. LeComte, M. Hayes, R. Heim, K. Gleason, J. Angel, B. Rippey, R. Tinker, M. Palecki, D. Stooksbury, and D. Miskus. 2002. “The Drought Monitor.” Bulletin of The American Meteorological Society 83 (8): 1181-1190. doi:10.1175/1520-0477(2002)083<1181:TDM>2.3.CO;2.
   Web of Science ®Google Scholar
• Tadesse, T., J. F. Brown, and M. J. Hayes. 2005. “A New Approach for Predicting Drought-Related Vegetation Stress: Integrating Satellite, Climate, and Biophysical Data over the U.S. Central Plains.” ISPRS Journal of Photogrammetry and Remote Sensing 59 (4): 244–253. doi:10.1016/j.isprsjprs.2005.02.003.
   Web of Science ®Google Scholar
• Tadesse, T., G. B. Senay, G. Berhan, T. Regassa, and S. Beyene. 2015b. “Evaluating A Satellite-Based Seasonal Evapotranspiration Product and Identifying Its Relationship with Other Satellite-Derived Products and Crop Yield: A Case Study for Ethiopia.” International Journal of Applied Earth Observation and Geoinformation 40: 39–54. doi:10.1016/j.jag.2015.03.006.
   Web of Science ®Google Scholar
• Tadesse, T., B. D. Wardlow, J. Brown, M. Hayes, M. Svoboda, B. Fuchs, and D. Gutzmer. 2015a. “Assessing the Vegetation Condition Impacts of the 2011 Drought across the U.S. Southern Great Plains Using the Vegetation Drought Response Index (Vegdri).” Journal of Applied Meteorology and Climatology 54 (1): 153–169. doi:10.1175/JAMC-D-14-0048.1.
   Web of Science ®Google Scholar
• Tadesse, T., B. D. Wardlow, M. J. Hayes, M. D. Svoboda, and J. F. Brown. 2010. “The Vegetation Condition Outlook (Vegdri): A New Method for Predicting Vegetation Seasonal Greenness.” Giscience and Remote Sensing 47 (1): 25–52. doi:10.2747/1548-1603.47.1.25.
   Web of Science ®Google Scholar
• The Canadian Encyclopedia (TCE), 2016, “Crops” [http://www.thecanadianencyclopedia.ca/en/article/crops/], accessed June 30, 2016.
   Google Scholar
• Tucker, C. J., J. R. G. Townshend, and T. E. Goff. 1985. “African Land Cover Classification Using Satellite Data.” Science 227 (4685): 369–375. doi:10.1126/science.227.4685.369.
   PubMed Web of Science ®Google Scholar
• U.S. Geological Survey (USGS), 2016, “Global 30 Arc-Second Elevation (GTOPO30),” available at https://lta.cr.usgs.gov/GTOPO30, accessed 30 June 30, 2016.
   Google Scholar
• Wardlow, B. D., and S. L. Egbert. 2008. “Large-Area Crop Mapping Using Time-Series MODIS 250 M NDVI Data: An Assessment for the U.S. Central Great Plains.” Remote Sensing of Environment 112 (3): 1096–1116. doi:10.1016/j.rse.2007.07.019.
   Web of Science ®Google Scholar
• Wardlow, B. D., T. Tadesse, J. F. Brown, K. Callahan, S. Swain, and E. Hunt. 2012. “The Vegetation Drought Response Index (Vegdri): An Integration of Satellite, Bioclimate, and Biophysical Data.” In Remote Sensing and Drought: Innovative Approaches to Monitoring, Eds. B. D. Wardlow, M. C. Anderson, and J. Verdin, 51–74. Boca Raton, FL: CRC Press.
   Google Scholar
• Wells, N., S. Goddard, and M. J. Hayes. 2004. “A Self-Calibrating Palmer Drought Severity Index.” Journal of Climate 17 (12): 2335–2351. doi:10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2.
   Web of Science ®Google Scholar
• Wheaton, E., S. Kulshreshtha, V. Wittrock, and G. Koshida. 2008. “Dry Times: Hard Lessons from the Canadian Drought of 2001 and 2002.” Canadian Geographic 52: 241–262. doi:10.1111/j.1541-0064.2008.00211.x.
   Web of Science ®Google Scholar
• Wilhite, D. A. 2000. “Preparing for Drought: A Methodology.” In Drought: A Global Assessment, edited by D. A. Wilhite, 89–104. Vol. 2. London, UK: Routledge.
   Google Scholar
• Wylie, B. K., L. Zhang, N. Bliss, L. Ji, L. L. Tieszen, and M. Jolly. 2008. “Integrating Modeling and Remote Sensing to Identify Ecosystem Performance Anomalies in the Boreal Forest, Yukon River Basin, Alaska.” International Journal of Digital Earth 1: 196–220. doi:10.1080/17538940802038366.
   Web of Science ®Google Scholar
• Yang, L., C. Huang, C. Homer, B. Wylie, and M. Coan. 2003. “An Approach for Mapping Large-Area Impervious Surfaces: Synergistic Use of Landsat-7 ETM+ and High Spatial Resolution Imagery.” Canadian Journal of Remote Sensing 29 (2): 230–240. doi:10.5589/m02-098.
   Web of Science ®Google Scholar
• Zhang, A., and G. Jia. 2013. “Monitoring Meteorological Drought in Semiarid Regions Using Multi-Sensor Microwave Remote Sensing Data.” Remote Sens. Environ 134: 12–23. doi:10.1016/j.rse.2013.02.023.
   Web of Science ®Google Scholar
• Zhang, L., B. W. Wylie, T. Loveland, E. A. Fosnight, L. Tieszen, and L. Ji. 2007. “Evaluation and Comparison of Gross Primary Production Estimates for the Northern Great Plains Grasslands.” Remote Sensing in Environment 106: 173–189. doi:10.1016/j.rse.2006.08.012.
   Web of Science ®Google Scholar
• Zwieback, S., K. Scipal, W. Dorigo, and W. Wagner. 2012. “Structural and Statistical Properties of the Collocation Technique for Error Characterization. Nonlinear Process.” Geophys 19: 69–80. doi:10.5194/npg-19-69-2012.
   Web of Science ®Google Scholar