Real-time monitoring and short-term forecasting of land surface phenology

Remote Sensing of Environment

Volumn 104, Issue 1, 2006, Pages 43-49

  White, Michael A ^a   Nemani, Ramakrishna R ^b  

^a UTAH STATE UNIVERSITY   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

Author keywords

0426; 0439; 0476; 0480; Bud break; Budburst; Fall; Growing season; Senescence; Spring

Indexed keywords

COMPUTATIONAL METHODS; HYDROLOGY; RADIOMETERS; REAL TIME SYSTEMS; REMOTE SENSING; SURFACE PHENOMENA;

BUD BREAK; BUDBURST; GROWING SEASON; SENESCENCE; SPRING;

FORECASTING;

AVHRR; FORECASTING METHOD; LAND MANAGEMENT; LAND SURFACE; MONITORING; PHENOLOGY; REAL TIME;

INSECTA;

EID: 33747354051     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2006.04.014     Document Type: Article

References (27)

1. Chuine I. A unified model for budburst of trees. Journal of Theoretical Biology 207 (2000) 337-347
2. de Beurs K.M., and Henebry G.M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 89 (2004) 497-509
3. Delbart N., Kergoat L., Le Toan T., Lhermitte J., and Picard G. Determination of phenological dates in boreal regions using normalized difference water index. Remote Sensing of Environment 97 (2005) 26-38
4. Gage S.H., Isard S.A., and Colunga M. Ecological scaling of aerobiological dispersal processes. Agricultural and Forest Meteorology 97 (1999) 249-261
5. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., and Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83 (2002) 195-213
6. Ji L., and Peters A. Forecasting vegetation greenness with satellite and climate data. IEEE Geoscience and Remote Sensing Letters 1 (2004) 3-6
7. Jolly W.M., Nemani R., and Running S.W. A generalized, bioclimatic index to predict foliar phenology in response to climate. Global Change Biology 11 (2005) 619-632
8. Jonsson P., and Eklundh L. TIMESAT - A program for analyzing time-series of satellite sensor data. Computers & Geosciences 30 (2004) 833-845
9. Justice C.O., Townshend J.R.G., Holben B.N., and Tucker C.J. Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing 6 (1985) 1271-1318
10. Kaitho, R., Stuth, J., Angerer, J., Jama, A., Mnene, W., Kingamkono, M. et al. (2003). A new system to forecast near-term forage conditions for early warning systems in pastoral regions of East Africa. Research Brief 03-02-LEWS, Global Livestock Collaborative Research Support Program, Davis, CA.
11. Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing 11 (1990) 2269-2279
12. Mkhabela M.S., Mkhabela M.S., and Mashinini N.N. Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAAs-AVHRR. Agricultural and Forest Meteorology 129 (2005) 1-9
13. Mussey G.J., and Potter D.A. Phenological correlations between flowering plants and activity of urban landscape pests in Kentucky. Journal of Economic Entomology 90 (1997) 1615-1627
14. Pinzon J., Brown M.E., and Tucker C.J. EMD correction of orbital drift artifacts in satellite data stream. In: Huang N., and Shen S. (Eds). The Hilbert-Huang transform and its applications (2005), World Scientific Publishing Co., Hackensack, NJ 167-183
15. Potter C., Tan P.N., Steinbach M., Klooster S., Kumar V., Myneni R., et al. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology 9 (2003) 1005-1021
16. Roads J., Fujioka F., Chen S., and Burgan R. Seasonal fire danger forecasts for the USA. International Journal of Wildland Fire 14 (2005) 1-18
17. Schwartz M.D., Reed B., and White M.A. Assessing satellite-derived start-of-season (SOS) measures in the conterminous USA. International Journal of Climatology 22 (2002) 1793-1805
18. Thórhallsdóttir T.E. Flowering phenology in the central highland of Iceland and implications for climatic warming in the arctic. Oecologia 114 (1998) 43-49
19. Traidl-Hoffmann C., Kasche A., Menzel A., Jakob T., Thiel M., Ring J., et al. Impact of pollen on human health: More than allergen carriers?. International Archives Of Allergy and Immunology 131 (2003) 1-13
20. Wan Z., Zhang Y., Li Z.-L., Wang R., Salomonson V.V., Yves A., et al. Preliminary estimate of calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using Lake Titicaca. Remote Sensing of Environment 80 (2002) 497-515
21. Wang Q., Adiku S., Tenhunen J., and Granier A. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment 94 (2005) 244-255
22. Weissteiner C.J., and Kuhbauch W. Regional yield forecasts of malting barley (Hordeum vulgare L.) by NOAA-AVHRR remote sensing data and ancillary data. Journal of Agronomy and Crop Science 191 (2005) 308-320
23. White M.A., Brunsell N., and Schwartz M.D. Vegetation phenology in global change studies. In: Schwartz M.D. (Ed). Phenology: An integrative environmental science (2003), Kluwer Academic Publishers, New York, NY 453-466
24. White M.A., Hoffman F., Hargrove W.W., and Nemani R.R. A global framework for monitoring phenological responses to climate change. Geophysical Research (2005) 32
25. White M.A., and Nemani R.R. Soil water forecasting in the continental United States: Relative forcing by meteorology versus leaf area index and the effects of meteorological forecast errors. Canadian Journal of Remote Sensing 30 (2004) 717-730
26. White M.A., Thornton P.E., and Running S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles 11 (1997) 217-234
27. Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84 (2003) 471-475