Remote sensing of drought: Progress, challenges and opportunities

Reviews of Geophysics

Volumn 53, Issue 2, 2015, Pages 452-480

  AghaKouchak, A ^a   Farahmand, A ^a   Melton, F S ^b   Teixeira, J ^c   Anderson, M C ^d   Wardlow, B D ^e   Hain, C R ^f  

^a University of California at Irvine   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^d AGRICULTURAL RESEARCH SERVICE   (United States)

^e UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^f UNIVERSITY OF MARYLAND   (United States)

Author keywords

drought; remote sensing

Indexed keywords

ATMOSPHERIC HUMIDITY; CARBON; CLIMATE MODELS; DROUGHT; ECOLOGY; ECOSYSTEMS; SATELLITES;

ATMOSPHERIC INFRARED SOUNDERS; CARBON AND NITROGEN; CLIMATE DATA RECORDS; GRAVITY RECOVERY AND CLIMATE EXPERIMENTS; MODELS AND ALGORITHMS; MULTIPLE DATA SETS; SATELLITE OBSERVATIONS; SATELLITE REMOTE SENSING;

REMOTE SENSING;

CLIMATOLOGY; DATA ASSIMILATION; DROUGHT; REMOTE SENSING; SATELLITE DATA;

EID: 84937522257     PISSN: 87551209     EISSN: 19449208     Source Type: Journal    
DOI: 10.1002/2014RG000456     Document Type: Review

References (329)

1. Adams, H.D., M. Guardiola-Claramonte, G.A. Barron-Gafford, J.C. Villegas, D.D. Breshears, C.B. Zou, P.A. Troch, and, T.E. Huxman, (2009), Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought, Proc. Nat. Acad. Sci., 106 (17), 7063-7066, doi: 10.1073/pnas.0901438106.
2. Adegoke, J.O., and, A.M. Carleton, (2002), Relations between soil moisture and satellite vegetation indices in the US corn belt, J. Hydrometeorol., 3 (4), 395-405, doi: 10.1175/1525-7541(2002)003<0395:RBSMAS>2.0.CO;2.
3. Adler, R., et al., (2003), The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present), J. Hydrometeorol., 4 (6), 1147-1167, doi: 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
4. AghaKouchak, A., (2014a), A baseline probabilistic drought forecasting framework using Standardized Soil Moisture Index: Application to the 2012 United States drought, Hydrol. Earth Syst. Sci., 18 (7), 2485-2492, doi: 10.5194/hess-18-2485-2014.
5. AghaKouchak, A., (2014b), A multivariate approach for persistence-based drought prediction: Application to the 2010-2011 East Africa drought, J. Hydrol., 526, 127-135, doi: 10.1016/j.jhydrol.2014.09.063.
6. AghaKouchak, A., and, A. Mehran, (2013), Extended contingency table: Performance metrics for satellite observations and climate model simulations, Water Resour. Res., 49 (10), 7144-7149, doi: 10.1002/wrcr.20498.
7. AghaKouchak, A., and, N. Nakhjiri, (2012), A near real-time satellite-based global drought climate data record, Environ. Res. Lett., 7 (4), 044037, doi: 10.1088/1748-9326/7/4/044037.
8. AghaKouchak, A., A. Mehran, H. Norouzi, and, A. Behrangi, (2012), Systematic and random error components in satellite precipitation data sets, Geophys. Res. Lett., 39, L09406, doi: 10.1029/2012GL051592.
9. Aghakouchak, A., L. Cheng, O. Mazdiyasni, and, A. Farahmand, (2014), Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought, Geophys. Res. Lett., 41, 8847-8852, doi: 10.1002/2014GL062308.
10. Aires, F., C. Prigent, and, W. Rossow, (2004), Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions, J. Geophys. Res., 109, D04313, doi: 10.1029/2003JD003527.
11. Allen, C. D., et al., (2010), A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests, For. Ecol. Manage., 259 (4), 660-684, doi: 10.1016/j.foreco.2009.09.001.
12. Allen, R., L. Pereira, D. Raes, and, M. Smith, (1998), Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Tech. Rep.
13. Allen, R.G., M. Tasumi, and, R. Trezza, (2007), Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) model, J. Irrig. Drain. Eng., 133 (4), 380-394, doi: 10.1061/(ASCE)0733-9437(2007)133:4(380).
14. Ambaw, G.M., (2013), Satellite based remote sensing of soil moisture for drought detection and monitoring in the horn of Africa, PhD thesis, Politecnico di Torino, Turin, Italy. [Available at http://porto.polito.it/2507436/.]
15. Andela, N., Y.Y. Liu, A.I.J.M. van Dijk, R.A.M. de Jeu, and, T.R. McVicar, (2013), Global changes in dryland vegetation dynamics (1988-2008) assessed by satellite remote sensing: Comparing a new passive microwave vegetation density record with reflective greenness data, Biogeosci. Discuss., 10 (5), 8749-8797, doi: 10.5194/bg-10-6657-2013.
16. Anderson, L., Y. Malhi, L. Aragao, and, S. Saatchi, (2008), Spatial patterns of the canopy stress during 2005 drought in Amazonia, Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International, pp. 2294-2297, Barcelona, Spain, doi: 10.1109/IGARSS.2007.4423299.
17. Anderson, M., J. Norman, G. Diak, W. Kustas, and, J. Mecikalski, (1997), A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing, Remote Sens. Environ., 60 (2), 195-216, doi: 10.1016/S0034-4257(96)00215-5.
18. Anderson, M.C., and, W.P. Kustas, (2008), Thermal remote sensing of drought and evapotranspiration, EOS Trans. AGU, 89 (26), 233-234, doi: 10.1029/2008EO260001.
19. Anderson, M.C., J.M. Norman, J.R. Mecikalski, J.A. Otkin, and, W.P. Kustas, (2007), A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation, J. Geophys. Res., 112, D10117, doi: 10.1029/2006JD007506.
20. Anderson, M. C., et al., (2011a), Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15 (1), 223-239, doi: 10.5194/hess-15-223-2011.
21. Anderson, M.C., C. Hain, B. Wardlow, A. Pimstein, J.R. Mecikalski, and, W.P. Kustas, (2011b), Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States, J. Clim., 24 (8), 2025-2044, doi: 10.1175/2010JCLI3812.1.
22. Anderson, M. C., C. Hain, B. Wardlow, A. Pimstein, J. Mecikalski, and, W. Kustas, (2012), A thermal-based evaporative stress index for monitoring surface moisture depletion, in Remote Sensing of Drought: Innovative Monitoring Approaches, pp. 145-167, CRC Press.
23. Anderson, M.C., C. Cammalleri, C.R. Hain, J. Otkin, X. Zhan, and, W. Kustas, (2013a), Using a diagnostic soil-plant-atmosphere model for monitoring drought at field to continental scales, Procedia Environ. Sci., 19, 47-56, doi: 10.1016/j.proenv.2013.06.006.
24. Anderson, M.C., C. Hain, J. Otkin, X. Zhan, K. Mo, M. Svoboda, B. Wardlow, and, A. Pimstein, (2013b), An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US Drought Monitor classifications, J. Hydrometeorol., 14 (4), 1035-1056, doi: 10.1175/JHM-D-12-0140.1.
25. Anderson, W. B., B. Zaitchik, C. Hain, M. Anderson, M. Yilmaz, J. Mecikalski, and, L. Schultz, (2012), Towards an integrated soil moisture drought monitor for East Africa, Hydrol. Earth Syst. Sci., 16 (8), 2893-2913, doi: 10.5194/hess-16-2893-2012.
26. Andreadis, K.M., and, D. Lettenmaier, (2005), Assimilating passive microwave brightness temperature for snow water equivalent estimation, AMS Annual Meeting, 19th Conference on Hydrology, San Diego, Calif.
27. Arkin, P.A., R. Joyce, and, J.E. Janowiak, (1994), The estimation of global monthly mean rainfall using infrared satellite data: The GOES Precipitation Index (GPI), Remote Sens. Rev., 11 (1-4), 107-124, doi: 10.1080/02757259409532261.
28. Asner, G.P., and, A. Alencar, (2010), Drought impacts on the Amazon forest: The remote sensing perspective, New Phytol., 187 (3), 569-578, doi: 10.1111/j.1469-8137.2010.03310.x.
29. Asner, G.P., S. Archer, R.F. Hughes, R.J. Ansley, and, C.A. Wessman, (2003), Net changes in regional woody vegetation cover and carbon storage in Texas drylands, 1937-1999, Global Change Biol., 9 (3), 316-335, doi: 10.1046/j.1365-2486.2003.00594.x.
30. Asner, G.P., D. Nepstad, G. Cardinot, and, D. Ray, (2004), Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy, Proc. Natl. Acad. Sci. U.S.A., 101 (16), 6039-6044, doi: 10.1073/pnas.0400168101.
31. Asrar, G., M. Fuchs, E. Kanemasu, and, J. Hatfield, (1984), Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat, Agron. J., 76 (2), 300-306, doi: 10.2134/agronj1984.00021962007600020029x.
32. Asrar, G., R.B. Myneni, and, E.T. Kanemasu, (1989), Estimation of plant-canopy attributes from spectral reflectance measurements, in Theory and Applications of Optical Remote Sensing, edited by, G. Asrar, pp. 252-296, John Wiley, New York.
33. Bales, R.C., N.P. Molotch, T.H. Painter, M.D. Dettinger, R. Rice, and, J. Dozier, (2006), Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, doi: 10.1029/2005WR004387.
34. Baret, F., and, G. Guyot, (1991), Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sens. Environ., 35 (2), 161-173, doi: 10.1016/0034-4257(91)90009-U.
35. Barrett, E., and, R. Herschy, (1989), Opportunities for satellite remote sensing in hydrology and water management, Geocarto Int., 4 (2), 11-18, doi: 10.1080/10106048909354204.
36. Barrett, E., M. Beaumont, and, R. Herschy, (1990), Satellite remote sensing for operational hydrology: Present needs and future opportunities, Remote Sens. Rev., 4 (2), 451-466, doi: 10.1080/02757259009532113.
37. Bayarjargal, Y., A. Karnieli, M. Bayasgalan, S. Khudulmur, C. Gandush, and, C.J. Tucker, (2006), A comparative study of NOAA-AVHRR derived drought indices using change vector analysis, Remote Sens. Environ., 105 (1), 9-22, doi: 10.1016/j.rse.2006.06.003.
38. Beck, H.E., T.R. McVicar, A.I. Dijk, J. Schellekens, R.A. de Jeu, and, L.A. Bruijnzeel, (2011), Global evaluation of four AVHRR-NDVI data sets: Intercomparison and assessment against Landsat imagery, Remote Sens. Environ., 115 (10), 2547-2563, doi: 10.1016/j.rse.2011.05.012.
39. Boken, V. K., et al., (2005), Monitoring and Predicting Agricultural Drought: A Global Study, p. 496, Oxford Univ. Press, New York.
40. Bolten, J.D., W.T. Crow, X. Zhan, T.J. Jackson, and, C.A. Reynolds, (2010), Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3 (1), 57-66, doi: 10.1109/JSTARS.2009.2037163.
41. Bromwich, D. H., et al., (2012), Tropospheric clouds in Antarctica, Rev. Geophys., 50, RG1004, doi: 10.1029/2011RG000363.
42. 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, GISci. Remote Sens., 45 (1), 16-46, doi: 10.2747/1548-1603.45.1.16.
43. Caccamo, G., L. Chisholm, R.A. Bradstock, and, M. Puotinen, (2011), Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems, Remote Sens. Environ., 115 (10), 2626-2639, doi: 10.1016/j.rse.2011.05.018.
44. Carlson, T., (2007), An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery, Sensors, 7 (8), 1612-1629.
45. Cashion, J., V. Lakshmi, D. Bosch, and, T.J. Jackson, (2005), Microwave remote sensing of soil moisture: Evaluation of the TRMM microwave imager (TMI) satellite for the Little River Watershed Tifton, Georgia, J. Hydrol., 307 (1), 242-253, doi: 10.1016/j.jhydrol.2004.10.019.
46. Chapin III, F., et al., (2006), Reconciling carbon-cycle concepts, terminology, and methods, Ecosystems, 9 (7), 1041-1050, doi: 10.1007/s10021-005-0105-7.
47. Chappell, A., L.J. Renzullo, T.H. Raupach, and, M. Haylock, (2013), Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia, J. Hydrol., 493 (17), 105-114, doi: 10.1016/j.jhydrol.2013.04.024.
48. 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 Sens. Environ., 98 (2), 225-236, doi: 10.1016/j.rse.2005.07.008.
49. Chen, J., C. Wilson, B. Tapley, Z. Yang, and, G. Niu, (2009), 2005 drought event in the Amazon River basin as measured by GRACE and estimated by climate models, J. Geophys. Res., 114, B05404, doi: 10.1029/2008JB006056.
50. Choi, M., J. Jacobs, M. Anderson, and, D. Bosch, (2013), Evaluation of drought indices via remotely sensed data with hydrological variables, J. Hydrol., 476 (7), 265-273, doi: 10.1016/j.jhydrol.2012.10.042.
51. Choudhury, B., C. Tucker, R. Golus, and, W. Newcomb, (1987), Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer's data, Int. J. Remote Sens., 8 (3), 533-538, doi: 10.1080/01431168708948660.
52. Choudhury, B.J., (1993), Reflectivities of selected land surface types at 19 and 37 GHz from SSM/I observations, Remote Sens. Environ., 46 (1), 1-17, doi: 10.1016/0034-4257(93)90028-V.
53. Ciais, P., et al., (2005), Europe-wide reduction in primary productivity caused by the heat and drought in 2003, Nature, 437 (7058), 529-533, doi: 10.1038/nature03972.
54. Dale, V. H., et al., (2001), Climate change and forest disturbances: Climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides, BioScience, 51 (9), 723-734, doi: 10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2.
55. Dalezios, N., A. Blanta, and, N. Spyropoulos, (2012), Assessment of remotely sensed drought features in vulnerable agriculture, Nat. Hazard. Earth Syst. Sci., 12 (10), 3139-3150, doi: 10.5194/nhess-12-3139-2012.
56. Damberg, L., and, A. AghaKouchak, (2014), Global trends and patterns of drought from space, Theor. Appl. Climatol., 117 (3), 441-448, doi: 10.1007/s00704-013-1019-5.
57. Deering, D., and, J. Rouse, (1975), Measuring "forage production" of grazing units from landsat MSS data, 10th International Symposium on Remote Sensing of Environment, Ann Arbor, Mich, pp. 1169-1178.
58. Di, L., D.C. Rundquist, and, L. Han, (1994), Modelling relationships between NDVI and precipitation during vegetative growth cycles, Int. J. Remote Sens., 15 (10), 2121-2136, doi: 10.1080/01431169408954231.
59. D'Odorico, P., K. Caylor, G.S. Okin, and, T.M. Scanlon, (2007), On soil moisture-vegetation feedbacks and their possible effects on the dynamics of dryland ecosystems, J. Geophys. Res., 112, G04010, doi: 10.1029/2006JG000379.
60. Dong, J., J.P. Walker, P.R. Houser, and, C. Sun, (2007), Scanning multichannel microwave radiometer snow water equivalent assimilation, J. Geophys. Res., 112, D07108, doi: 10.1029/2006JD007209.
61. Donohue, R.J., T. McVicar, and, M.L. Roderick, (2009), Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981-2006, Global Change Biol., 15 (4), 1025-1039, doi: 10.1111/j.1365-2486.2008.01746.x.
62. Donohue, R.J., T.R. McVicar, and, M.L. Roderick, (2010), Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate, J. Hydrol., 386 (1), 186-197, doi: 10.1016/j.jhydrol.2010.03.020.
63. Donohue, R.J., M.L. Roderick, T.R. McVicar, and, G.D. Farquhar, (2013), Impact of CO2 fertilization on maximum foliage cover across the globe's warm, arid environments, Geophys. Res. Lett., 40, 3031-3035, doi: 10.1002/grl.50563.
64. Dorigo, W., K. Scipal, R. Parinussa, Y. Liu, W. Wagner, R. De Jeu, and, V. Naeimi, (2010), Error characterisation of global active and passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14 (12), 2605-2616, doi: 10.5194/hess-14-2605-2010.
65. Dozier, J., R.O. Green, A.W. Nolin, and, T.H. Painter, (2009), Interpretation of snow properties from imaging spectrometry, Remote Sens. Environ., 113 (1), S25-S37, doi: 10.1016/j.rse.2007.07.029.
66. Dubayah, R., et al., (2014), The Global Ecosystem Dynamics Investigation (GEDI) lidar, ForestSAT2014 Open Conference System, 4-7 November 2014, Riva del Garda, Italy.
67. Durand, M., N.P. Molotch, and, S.A. Margulis, (2008), Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction, Remote Sens. Environ., 112 (3), 1212-1225, doi: 10.1016/j.rse.2007.08.010.
68. Durand, M., E.J. Kim, and, S.A. Margulis, (2008), Quantifying uncertainty in modeling snow microwave radiance for a mountain snowpack at the point-scale, including stratigraphic effects, IEEE Trans. Geosci. Remote Sens., 46 (6), 1753-1767, doi: 10.1109/TGRS.2008.916221.
69. Easterling, D., (2013), Global data sets for analysis of climate extremes, Extremes Changing Clim., 65, 347-361, doi: 10.1007/978-94-007-4479-012.
70. Ebert, E., J. Janowiak, and, C. Kidd, (2007), Comparison of near real time precipitation estimates from satellite observations and numerical models, Bull. Am. Meteorol. Soc., 88 (1), 47-64, doi: 10.1175/BAMS-88-1-47.
71. Entekhabi, D., et al., (2004), The hydrosphere state (Hydros) satellite mission: An Earth system pathfinder for global mapping of soil moisture and land freeze/thaw, IEEE Trans. Geosci. Remote Sens., 42 (10), 2184-2195, doi: 10.1109/TGRS.2004.834631.
72. Entekhabi, D., et al., (2010a), The soil moisture active passive (SMAP) mission, Proc. IEEE, 98 (5), 704-716, doi: 10.1109/JPROC.2010.2043918.
73. Entekhabi, D., R.H. Reichle, R.D. Koster, and, W.T. Crow, (2010b), Performance metrics for soil moisture retrievals and application requirements, J. Hydrometeorol., 11 (3), 832-840, doi: 10.1175/2010JHM1223.1.
74. Famiglietti, J.S., and, M. Rodell, (2013), Water in the balance, Science, 340 (6138), 1300-1301, doi: 10.1126/science.1236460.
75. Farahmand, A., and, A. AghaKouchak, (2014), A generalized framework for deriving nonparametric standardized drought indicators, Adv. Water Res., 76, 140-145, doi: 10.1016/j.advwatres.2014.11.012.
76. Farahmand, A., J. Teixeira, and, A. AghaKouchak, (2014), A vantage from space can detect earlier drought onset: An approach using relative humidity, Sci. Rep., 5, 8553, doi: 10.1038/srep08553.
77. Farrar, T., S. Nicholson, and, A. Lare, (1994), The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. II. NDVI response to soil moisture, Remote Sens. Environ., 50 (2), 121-133, doi: 10.1016/0034-4257(94)90039-6.
78. Fensholt, R., I. Sandholt, S. Stisen, and, C. Tucker, (2006), Analysing NDVI for the African continent using the geostationary METEOSAT second generation SEVIRI sensor, Remote Sens. Environ., 101 (2), 212-229, doi: 10.1016/j.rse.2005.11.013.
79. Foster, J.L., C. Sun, J.P. Walker, R. Kelly, A. Chang, J. Dong, and, H. Powell, (2005), Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sens. Environ., 94 (2), 187-203, doi: 10.1016/j.rse.2004.09.012.
80. Foster, J. L., et al., (2011), A blended global snow product using visible, passive microwave and scatterometer satellite data, Int. J. Remote Sens., 32 (5), 1371-1395, doi: 10.1080/01431160903548013.
81. Funk, C., (2011), We thought trouble was coming, Nature, 476 (7358), 7, doi: 10.1038/476007a.
82. Funk, C., and, M.E. Budde, (2009), Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe, Remote Sens. Environ., 113 (1), 115-125, doi: 10.1016/j.rse.2008.08.015.
83. Gallagher, J., P. Biscoe, and, B. Hunter, (1976), Effects of drought on grain growth, Nature, 264, 541-542.
84. Gao, B.-C., (1996), NDWI a normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58 (3), 257-266, doi: 10.1038/264541a0.
85. Gatti, L., et al., (2014), Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements, Nature, 506 (7486), 76-80, doi: 10.1038/nature12957.
86. Gebremichael, M., (2010), Framework for satellite rainfall product evaluation, Geophys. Monogr. Ser., 191, 265-275, doi: 10.1029/2010GM000974.
87. Ghulam, A., Q. Qin, and, Z. Zhan, (2007a), Designing of the perpendicular drought index, Environ. Geol., 52 (6), 1045-1052, doi: 10.1007/s00254-006-0544-2.
88. Ghulam, A., Q. Qin, T. Teyip, and, Z.-L. Li, (2007b), Modified perpendicular drought index (MPDI): A real-time drought monitoring method, ISPRS J. Photogramm. Remote Sens., 62 (2), 150-164, doi: 10.1016/j.isprsjprs.2007.03.002.
89. Glenn, E.P., A.R. Huete, P.L. Nagler, K.K. Hirschboeck, and, P. Brown, (2007), Integrating remote sensing and ground methods to estimate evapotranspiration, Crit. Rev. Plant Sci., 26 (3), 139-168, doi: 10.1080/07352680701402503.
90. Glenn, E.P., C.M. Neale, D.J. Hunsaker, and, P.L. Nagler, (2011), Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems, Hydrol. Processes, 25 (26), 4050-4062, doi: 10.1002/hyp.8392.
91. Godfray, H.C.J., J.R. Beddington, I.R. Crute, L. Haddad, D. Lawrence, J.F. Muir, J. Pretty, S. Robinson, S.M. Thomas, and, C. Toulmin, (2010), Food security: The challenge of feeding 9 billion people, Science, 327 (5967), 812-818, doi: 10.1126/science.1185383.
92. Golian, S., O. Mazdiyasni, and, A. AghaKouchak, (2014), Trends in meteorological and agricultural droughts in Iran, Theor. Appl. Climatol., 119 (3-4), 679-688, doi: 10.1007/s00704-014-1139-6.
93. Goll, D.S., N. Moosdorf, J. Hartmann, and, V. Brovkin, (2014), Climate-driven changes in chemical weathering and associated phosphorus release since 1850: Implications for the land carbon balance, Geophys. Res. Lett., 41, 3553-3558, doi: 10.1002/2014GL059471.
94. Goodison, B.E., (1989), Determination of areal snow water equivalent on the Canadian prairies using passive microwave satellite data, in Geoscience and Remote Sensing Symposium, 1989. IGARSS'89. 12th Canadian Symposium on Remote Sensing, vol. 3, pp. 1243-1246, IEEE.
95. Grasso, V.F., and, A. Singh, (2011), Early warning systems: State-of-art analysis and future directions, United Nations Environment Programme (UNEP). [Available at http://na.unep.net/siouxfalls/publications/Early-Warning.pdf.]
96. Grody, N.C., and, A.N. Basist, (1996), Global identification of snowcover using SSM/I measurements, IEEE Trans. Geosci. Remote Sens., 34 (1), 237-249, doi: 10.1109/36.481908.
97. Gruhier, C., et al., (2010), Soil moisture active and passive microwave products: Intercomparison and evaluation over a Sahelian site, Hydrol. Earth Syst. Sci., 14, 141-156, doi: 10.5194/hess-14-141-2010.
98. 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, Geophys. Res. Lett., 34, L06407, doi: 10.1029/2006GL029127.
99. 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, Geophys. Res. Lett., 35, L22401, doi: 10.1029/2008GL035772.
100. Guan, B., N.P. Molotch, D.E. Waliser, S.M. Jepsen, T.H. Painter, and, J. Dozier, (2013), Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations, Water Resour. Res., 49, 5029-5046, doi: 10.1002/wrcr.20387.
101. Gutman, G.G., (1990), Towards monitoring droughts from space, J. Clim., 3 (2), 282-295, doi: 10.1175/1520-0442(1990)003<0282:TMDFS>2.0.CO;2.
102. Hall, D.K., G.A. Riggs, V.V. Salomonson, N.E. DiGirolamo, and, K.J. Bayr, (2002), MODIS snow-cover products, Remote Sens. Environ., 83 (1), 181-194, doi: 10.1016/S0034-4257(02)00095-0.
103. Hao, Z., and, A. AghaKouchak, (2013), Multivariate standardized drought index: A parametric multi-index model, Adv. Water Res., 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.
104. Hao, Z., and, A. AghaKouchak, (2014), A nonparametric multivariate multi-index drought monitoring framework, J. Hydrometeorol., 15, 89-101, doi: 10.1175/JHM-D-12-0160.1.
105. Hao, Z., and, V. P. Singh, (2015), Drought characterization from a multivariate perspective: A review, J. Hydrol., 527, 668-678.
106. Hao, Z., A. AghaKouchak, N. Nakhjiri, and, A. Farahmand, (2014), Global Integrated Drought Monitoring and Prediction System, Sci. Data, 1, 140001, doi: 10.1038/sdata.2014.1.
107. He, M., T.S. Hogue, S.A. Margulis, and, K.J. Franz, (2012), An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions, Hydrol. Earth Syst. Sci., 16 (3), 815-831, doi: 10.5194/hess-16-815-2012.
108. Hatfield, J., G. Asrar, and, E. Kanemasu, (1984), Intercepted photosynthetically active radiation estimated by spectral reflectance, Remote Sens. Environ., 14 (1), 65-75, doi: 10.1016/0034-4257(84)90008-7.
109. Hayes, M., M. Svoboda, D. Wilhite, and, O. Vanyarkho, (1999), Monitoring the 1996 drought using the Standardized Precipitation Index, Bull. Am. Meteorol. Soc., 80 (3), 429-438, doi: 10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2.
110. Hayes, M., M. Svoboda, N. Wall, and, M. Widhalm, (2011), The Lincoln declaration on drought indices: Universal meteorological drought index recommended, Bull. Am. Meteorol. Soc., 92 (4), 485-488, doi: 10.1175/2010BAMS3103.1.
111. Heim, R., (2002), A review of twentieth-century drought indices used in the United States, Bull. Am. Meteorol. Soc., 83 (8), 1149-1165, doi: 10.1175/1520-0477(2002)083<1149:AROTDI>2.3.CO;2.
112. Heumann, B.W., (2011), Satellite remote sensing of mangrove forests: Recent advances and future opportunities, Prog. Phys. Geogr., 35 (1), 87-108, doi: 10.1177/0309133310385371.
113. Hidalgo, H.G., (2004), Climate precursors of multidecadal drought variability in the western United States, Water Resour. Res., 40, W12504, doi: 10.1029/2004WR003350.
114. Hielkema, J., S. Prince, and, W. Astle, (1986), Rainfall and vegetation monitoring in the savanna zone of the Democratic Republic of Sudan using the NOAA advanced very high resolution radiometer, Int. J. Remote Sens., 7 (11), 1499-1513, doi: 10.1080/01431168608948950.
115. Hobbins, M.T., A. Dai, M.L. Roderick, and, G.D. Farquhar, (2008), Revisiting the parameterization of potential evaporation as a driver of long-term water balance trends, Geophys. Res. Lett., 35, L12403, doi: 10.1029/2008GL033840.
116. Hoerling, M., and, A. Kumar, (2003), The perfect ocean for drought, Science, 299 (5607), 691-694, doi: 10.1126/science.1079053.
117. Hong, Y., K. Hsu, X. Gao, and, S. Sorooshian, (2004), Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system, J. Appl. Meteorol. Climatol., 43 (12), 1834-1853, doi: 10.1175/JAM2173.1.
118. Hossain, F., and, G. Huffman, (2008), Investigating error metrics for satellite rainfall data at hydrologically relevant scales, J. Hydrometeorol., 9 (3), 563-575, doi: 10.1175/2007JHM925.1.
119. Houborg, R., M. Rodell, B. Li, R. Reichle, and, B.F. Zaitchik, (2012), Drought indicators based on model-assimilated Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage observations, Water Resour. Res., 48, W07525, doi: 10.1029/2011WR011291.
120. Hsu, K., X. Gao, S. Sorooshian, and, H. Gupta, (1997), Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36 (9), 1176-1190, doi: 10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.
121. Huete, A., C. Justice, and, W. Van Leeuwen, (1999), MODIS Vegetation Index (MOD13), Algorithm Theoretical Basis Document. [Available at http://modis.gsfc.nasa.gov/data/atbd/atbd-mod13.pdf.]
122. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and, L.G. Ferreira, (2002), Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83 (1), 195-213, doi: 10.1016/S0034-4257(02)00096-2.
123. Huete, A.R., (1988), A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25 (3), 295-309, doi: 10.1016/0034-4257(88)90106-X.
124. 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, Geophys. Res. Lett., 33, L06405, doi: 10.1029/2005GL025583.
125. Huffman, G., R. Adler, D. Bolvin, G. Gu, E. Nelkin, K. Bowman, E. Stocker, and, D. Wolff, (2007), The TRMM Multi-satellite Precipitation Analysis: Quasi-global, multiyear, combined-sensor precipitation estimates at fine scale, J. Hydrometeorol., 8 (1), 38-55, doi: 10.1175/JHM560.1.
126. Hunt, E.R., and, B.N. Rock, (1989), Detection of changes in leaf water content using near-and middle-infrared reflectances, Remote Sens. Environ., 30 (1), 43-54, doi: 10.1016/0034-4257(89)90046-1.
127. Huntingford, C., et al., (2013), Simulated resilience of tropical rainforests to CO2-induced climate change, Nat. Geosci., 6 (4), 268-273, doi: 10.1038/ngeo1741.
128. Idso, S., R. Jackson, P. Pinter, R. Reginato, and, J. Hatfield, (1981), Normalizing the stress-degree-day parameter for environmental variability, Agric. Meteorol., 24 (1), 45-55, doi: 10.1016/0002-1571(81)90032-7.
129. Jackson, R.D., S. Idso, R. Reginato, and, P. Pinter Jr., (1981), Canopy temperature as a crop water stress indicator, Water Resour. Res., 17 (4), 1133-1138, doi: 10.1029/WR017i004p01133.
130. Jackson, T.J., (1993), III. measuring surface soil moisture using passive microwave remote sensing, Hydrol. Processes, 7 (2), 139-152, doi: 10.1002/hyp.3360070205.
131. Jackson, T.J., (1997), Soil moisture estimation using special satellite microwave/imager satellite data over a grassland region, Water Resour. Res., 33 (6), 1475-1484, doi: 10.1029/97WR00661.
132. Jackson, T.J., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, P. Doriaswamy, and, E. Hunt, (2004), Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans, Remote Sens. Environ., 92 (4), 475-482, doi: 10.1016/j.rse.2003.10.021.
133. Jain, S.K., R. Keshri, A. Goswami, A. Sarkar, and, A. Chaudhry, (2009), Identification of drought-vulnerable areas using NOAA AVHRR data, Int. J. Remote Sens., 30 (10), 2653-2668, doi: 10.1080/01431160802555788.
134. Ji, L., and, A.J. Peters, (2003), Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices, Remote Sens. Environ., 87 (1), 85-98, doi: 10.1016/S0034-4257(03)00174-3.
135. Jones, M.O., L.A. Jones, J.S. Kimball, and, K.C. McDonald, (2011), Satellite passive microwave remote sensing for monitoring global land surface phenology, Remote Sens. Environ., 115 (4), 1102-1114, doi: 10.1016/j.rse.2010.12.015.
136. Joyce, R., and, P.A. Arkin, (1997), Improved estimates of tropical and subtropical precipitation using the GOES Precipitation Index, J. Atmos. Oceanic Technol., 14 (5), 997-1011, doi: 10.1175/1520-0426(1997)014<0997:IEOTAS>2.0.CO;2.
137. Joyce, R., J. Janowiak, P. Arkin, and, P. Xie, (2004), CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5 (3), 487-503, doi: 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.
138. Justice, C., J. Townshend, E. Vermote, E. Masuoka, R. Wolfe, N. Saleous, D. Roy, and, J. Morisette, (2002), An overview of MODIS Land data processing and product status, Remote Sens. Environ., 83 (1), 3-15, doi: 10.1016/S0034-4257(02)00084-6.
139. Justice, C. O., et al., (2013), Land and cryosphere products from SUOMI NPP VIIRS: Overview and status, J. Geophys. Res. Atmos., 118, 9753-9765, doi: 10.1002/jgrd.50771.
140. Kalma, J.D., T.R. McVicar, and, M.F. McCabe, (2008), Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surv. Geophys., 29 (4-5), 421-469, doi: 10.1007/s10712-008-9037-z.
141. Kao, S., and, R. Govindaraju, (2010), A copula-based joint deficit index for droughts, J. Hydrol., 380 (1), 121-134, doi: 10.1016/j.jhydrol.2009.10.029.
142. Katiraie-Boroujerdy, P. S., N. Nasrollahi, K. Hsu, and, S. Sorooshian, (2013), Evaluation of satellite-based precipitation estimation over Iran, J. Arid Environ., 97, 205-219.
143. Kim, J., and, T. Hogue, (2012), Improving spatial soil moisture representation through integration of AMSR-E and MODIS products, IEEE Trans. Geosci. Remote Sens., 50 (2), 446-460.
144. Krajewski, W.F., M.C. Anderson, W.E. Eichinger, D. Entekhabi, B.K. Hornbuckle, P.R. Houser, G.G. Katul, W.P. Kustas, J.M. Norman, and, C. Peters-Lidard, (2006), A remote sensing observatory for hydrologic sciences: A genesis for scaling to continental hydrology, Water Resour. Res., 42, W07301, doi: 10.1029/2005WR004435.
145. Karnieli, A., N. Agam, R.T. Pinker, M. Anderson, M.L. Imhoff, G.G. Gutman, N. Panov, and, A. Goldberg, (2010), Use of NDVI and land surface temperature for drought assessment: Merits and limitations, J. Clim., 23 (3), 618-633, doi: 10.1175/2009JCLI2900.1.
146. Kelly, R.E., A.T. Chang, L. Tsang, and, J.L. Foster, (2003), A prototype AMSR-E global snow area and snow depth algorithm, IEEE Trans. Geosci. Remote Sens., 41 (2), 230-242, doi: 10.1109/TGRS.2003.809118.
147. Keyantash, J., and, J. Dracup, (2004), An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage, Water Resour. Res., 40, W09304, doi: 10.1029/2003WR002610.
148. Kidd, C., (2001), Satellite rainfall climatology: A review, Int. J. Climatol., 21 (9), 1041-1066, doi: 10.1002/joc.635.
149. Kiladze, R.I., and, A.S. Sochilina, (2003), On the new theory of geostationary satellite motion, Astron. Astrophys. Trans., 22 (4-5), 525-528, doi: 10.1080/1055679031000111109.
150. Kogan, F., (1995), Application of vegetation index and brightness temperature for drought detection, Adv. Space Res., 15 (11), 91-100, doi: 10.1016/0273-1177(95)00079-T.
151. Kogan, F., and, J. Sullivan, (1993), Development of global drought-watch system using NOAA/AVHRR data, Adv. Space Res., 13 (5), 219-222, doi: 10.1016/0273-1177(93)90548-P.
152. Kogan, F.N., (1997), Global drought watch from space, Bull. Am. Meteorol. Soc., 78 (4), 621-636, doi: 10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2.
153. Kogan, F.N., (2001), Operational space technology for global vegetation assessment, Bull. Am. Meteorol. Soc., 82 (9), 1949-1964, doi: 10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2.
154. Kongoli, C., C.A. Dean, S.R. Helfrich, and, R.R. Ferraro, (2007), Evaluating the potential of a blended passive microwave-interactive multi-sensor product for improved mapping of snow cover and estimations of snow water equivalent, Hydrol. Processes, 21 (12), 1597-1607, doi: 10.1002/hyp.6722.
155. Kongoli, C., P. Romanov, and, R. Ferraro, (2012), Snow cover monitoring from remote sensing satellites, in Remote Sensing of Drought: Innovative Monitoring Approaches, pp. 359-386, CRC Press.
156. Koster, R.D., M.J. Suarez, A. Ducharne, M. Stieglitz, and, P. Kumar, (2000), A catchment-based approach to modeling land surface processes in a General Circulation Model: 1. Model structure, J. Geophys. Res., 105 (D20), 24,809-24,822, doi: 10.1029/2000JD900327.
157. Kummerow, C., W.S. Olson, and, L. Giglio, (1996), A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE Trans. Geosci. Remote Sens., 34 (5), 1213-1232, doi: 10.1109/36.536538.
158. Kummerow, C., Y. Hong, W. Olson, S. Yang, R. Adler, J. McCollum, R. Ferraro, G. Petty, D.-B. Shin, and, T. Wilheit, (2001), The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors, J. Appl. Meteorol., 40 (11), 1801-1820, doi: 10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.
159. Kunzi, K.F., S. Patil, and, H. Rott, (1982), Snow-cover parameters retrieved from NIMBUS-7 scanning multichannel microwave radiometer (SMMR) data, IEEE Trans. Geosci. Remote Sens., GE-20 (4), 452-467, doi: 10.1109/TGRS.1982.350411.
160. Lambin, E., and, D. Ehrlich, (1996), The surface temperature-vegetation index space for land cover and land-cover change analysis, Int. J. Remote Sens., 17 (3), 463-487, doi: 10.1080/01431169608949021.
161. Leblanc, M.J., P. Tregoning, G. Ramillien, S.O. Tweed, and, A. Fakes, (2009), Basin-scale, integrated observations of the early 21st century multiyear drought in southeast Australia, Water Resour. Res., 45, W04408, doi: 10.1029/2008WR007333.
162. Levizzani, V., P. Bauer, and, F. J. Turk, (Eds.) (2007), Measuring Precipitation From Space: EURAINSAT and the Future, Springer, Dordrecht, Netherlands.
163. Lewis, S.L., P.M. Brando, O.L. Phillips, G.M.F. van der Heijden, and, D. Nepstad, (2011), The 2010 Amazon drought, Science, 331 (6017), 554, doi: 10.1126/science.1200807.
164. Li, B., M. Rodell, B.F. Zaitchik, R.H. Reichle, R.D. Koster, and, T.M. van Dam, (2012), Assimilation of GRACE terrestrial water storage into a land surface model: Evaluation and potential value for drought monitoring in western and central Europe, J. Hydrol., 446, 103-115, doi: 10.1016/j.jhydrol.2012.04.035.
165. Liang, T., X. Zhang, H. Xie, C. Wu, Q. Feng, X. Huang, and, Q. Chen, (2008), Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements, Remote Sens. Environ., 112 (10), 3750-3761, doi: 10.1016/j.rse.2008.05.010.
166. Liu, J., (2004), Investigation of ecosystem drought stress and its impacts on carbon exchange in tropical forests, PhD thesis, Colo. State Univ., Fort Collins.
167. Liu, W., and, F. Kogan, (1996), Monitoring regional drought using the Vegetation Condition Index, Int. J. Remote Sens., 17 (14), 2761-2782, doi: 10.1080/01431169608949106.
168. Liu, Y. Y., R. Parinussa, W. Dorigo, R.D. Jeu, W. Wagner, A.I.J.M. van Dijk, M. McCabe, and, J. Evans, (2011a), Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15 (2), 425-436, doi: 10.5194/hess-15-425-2011.
169. Liu, Y.Y., R.A. de Jeu, M.F. McCabe, J.P. Evans, and, A.I.J.M. van Dijk, (2011b), Global long-term passive microwave satellite-based retrievals of vegetation optical depth, Geophys. Res. Lett., 38, L18402, doi: 10.1029/2011GL048684.
170. Liu, Y.Y., A.I.J.M. van Dijk, M.F. McCabe, J.P. Evans, and, R.A. Jeu, (2013a), Global vegetation biomass change (1988-2008) and attribution to environmental and human drivers, Global Ecol. Biogeogr., 22 (6), 692-705, doi: 10.1111/geb.12024.
171. Liu, Y.Y., J.P. Evans, M.F. McCabe, R.A. de Jeu, A.I.J.M. van Dijk, A.J. Dolman, and, I. Saizen, (2013b), Changing climate and overgrazing are decimating Mongolian steppes, PloS one, 8 (2), e57599, doi: 10.1371/journal.pone.0057599.
172. Long, D., B.R. Scanlon, L. Longuevergne, A.-Y. Sun, D.N. Fernando, and, S. Himanshu, (2013), GRACE satellites monitor large depletion in water storage in response to the 2011 drought in Texas, Geophys. Res. Lett., 40, 3395-3401, doi: 10.1002/grl.50655.
173. Lu, H., M.R. Raupach, T.R. McVicar, and, D.J. Barrett, (2003), Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series, Remote Sens. Environ., 86 (1), 1-18, doi: 10.1016/S0034-4257(03)00054-3.
174. Marengo, J.A., J. Tomasella, L.M. Alves, W.R. Soares, and, D.A. Rodriguez, (2011), The drought of 2010 in the context of historical droughts in the Amazon region, Geophys. Res. Lett., 38, L12703, doi: 10.1029/2011GL047436.
175. Margulis, S.A., E.F. Wood, and, P.A. Troch, (2006), The terrestrial water cycle: Modeling and data assimilation across catchment scales, J. Hydrometeorol., 7 (3), 309-311, doi: 10.1175/JHM999.1.
176. McKee, T., N. Doesken, and, J. Kleist, (1993), The relationship of drought frequency and duration to time scales, in Proceedings of the 8th Conference of Applied Climatology, January 1993, vol.17, pp. 179-184, Am. Meteorol. Soc., Anaheim, Calif.
177. McVicar, T.R., and, P.B. Bierwirth, (2001), Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery, Int. J. Remote Sens., 22 (11), 2109-2128, doi: 10.1080/01431160120728.
178. McVicar, T.R., and, D.L. Jupp, (1998), The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: A review, Agric. Syst., 57 (3), 399-468, doi: 10.1016/S0308-521X(98)00026-2.
179. McVicar, T.R., and, D.L. Jupp, (1999), Estimating one-time-of-day meteorological data from standard daily data as inputs to thermal remote sensing based energy balance models, Agric. For. Meteorol., 96 (4), 219-238, doi: 10.1016/S0168-1923(99)00052-0.
180. McVicar, T.R., and, D.L. Jupp, (2002), Using covariates to spatially interpolate moisture availability in the Murray-Darling Basin: A novel use of remotely sensed data, Remote Sens. Environ., 79 (2), 199-212, doi: 10.1016/S0034-4257(01)00273-5.
181. McVicar, T.R., and, C. Körner, (2013), On the use of elevation, altitude, and height in the ecological and climatological literature, Oecologia, 171 (2), 335-337.
182. McVicar, T. R., et al., (2012), Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation, J. Hydrol., 416, 182-205, doi: 10.1016/j.jhydrol.2011.10.024.
183. Mecikalski, J.R., G.R. Diak, M.C. Anderson, and, J.M. Norman, (1999), Estimating fluxes on continental scales using remotely sensed data in an atmospheric-land exchange model, J. Appl. Meteorol., 38 (9), 1352-1369, doi: 10.1175/1520-0450(1999)038<1352:EFOCSU>2.0.CO;2.
184. Meesters, A.G., R.A. De Jeu, and, M. Owe, (2005), Analytical derivation of the vegetation optical depth from the microwave polarization difference index, IEEE Geosci. Remote Sens. Lett., 2 (2), 121-123, doi: 10.1109/LGRS.2005.843983.
185. Mehran, A., and, A. AghaKouchak, (2014), Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations, Hydrol. Processes, 28, 2262-2270, doi: 10.1002/hyp.9779.
186. Meir, P., D. Metcalfe, A. Costa, and, R. Fisher, (2008), The fate of assimilated carbon during drought: Impacts on respiration in Amazon rainforests, Philos. Trans. R. Soc. London, Ser. B, 363 (1498), 1849-1855.
187. Miao, R., N. Lu, L. Yao, Y. Zhu, J. Wang, and, J. Sun, (2013), Multi-year comparison of carbon dioxide from satellite data with ground-based FTS measurements (2003-2011), Remote Sens., 5 (7), 3431-3456, doi: 10.3390/rs5073431.
188. Mo, K.C., (2011), Drought onset and recovery over the United States, J. Geophys. Res., 116, D20106, doi: 10.1029/2011JD016168.
189. Molotch, N.P., and, R.C. Bales, (2006), Comparison of ground-based and airborne snow surface albedo parameterizations in an alpine watershed: Impact on snowpack mass balance, Water Resour. Res., 42, W05410, doi: 10.1029/2005WR004522.
190. Molotch, N.P., and, S.A. Margulis, (2008), Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution, multi-sensor comparison, Adv. Water Res., 31 (11), 1503-1514, doi: 10.1016/j.advwatres.2008.07.017.
191. Moran, M., T. Clarke, Y. Inoue, and, A. Vidal, (1994), Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index, Remote Sens. Environ., 49 (3), 246-263, doi: 10.1016/0034-4257(94)90020-5.
192. Moran, M.S., C.D. Peters-Lidard, J.M. Watts, and, S. McElroy, (2004), Estimating soil moisture at the watershed scale with satellite-based radar and land surface models, Can. J. Remote Sens., 30 (5), 805-826, doi: 10.5589/m04-043.
193. Morgan, J., (1989), Satellite remote sensing in meteorology and climatology- status, perspectives and challenges, Deutsche Meteorologen-Tagung ueber Atmosphaere, Ozeane, Kontinente, Kiel, Federal Republic of Germany, May 16-19, 1989, Ann. Meteorol., 26, 39-43.
194. Morillas, L., R. Leuning, L. Villagarcía, M. García, P. Serrano-Ortiz, and, F. Domingo, (2013), Improving evapotranspiration estimates in Mediterranean drylands: The role of soil evaporation, Water Resour. Res., 49, 6572-6586, doi: 10.1002/wrcr.20468.
195. Mu, Q., F.A. Heinsch, M. Zhao, and, S.W. Running, (2007), Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111 (4), 519-536, doi: 10.1016/j.rse.2007.04.015.
196. Mu, Q., L.A. Jones, J.S. Kimball, K.C. McDonald, and, S.W. Running, (2009), Satellite assessment of land surface evapotranspiration for the pan-Arctic domain, Water Resour. Res., 45, W09420, doi: 10.1029/2008WR007189.
197. Mu, Q., M. Zhao, and, S.W. Running, (2011), Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115 (8), 1781-1800, doi: 10.1016/j.rse.2011.02.019.
198. Mu, Q., M. Zhao, J.S. Kimball, N.G. McDowell, and, S.W. Running, (2013), A remotely sensed global terrestrial drought severity index, Bull. Am. Meteorol. Soc., 94 (1), 83-98, doi: 10.1175/BAMS-D-11-00213.1.
199. NASA (1987), Space-based Remote Sensing of the Earth: A Report to the Congress, National Aeronautics and Space Administration, Washington, D. C. [Available at http://catalog.hathitrust.org/Record/007403943.]
200. NASA (1995), Guidelines and assessment procedures for limiting orbital debris, vol. 1740. 14, National Aeronautics and Space Administration, Washington, D. C. [Available at http://snebulos.mit.edu/projects/reference/NASA-Generic/NSS-1740.14. pdf.]
201. NASA (2010), Science plan for NASA's science mission directorate, Tech. Rep., National Aeronautics and Space Administration, Washington, D. C.
202. Nasrollahi, N., K. Hsu, and, S. Sorooshian, (2013), An artificial neural network model to reduce false alarms in satellite precipitation products using MODIS and CloudSat observations, J. Hydrometeorol, 14 (6), 1872-1883.
203. Nemani, R., H. Hashimoto, P. Votava, F. Melton, W. Wang, A. Michaelis, L. Mutch, C. Milesi, S. Hiatt, and, M. White, (2009), Monitoring and forecasting ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS), Remote Sens. Environ., 113 (7), 1497-1509, doi: 10.1016/j.rse.2008.06.017.
204. Nicholson, S.E., C.J. Tucker, and, M. Ba, (1998), Desertification, drought, and surface vegetation: An example from the west African Sahel, Bull. Am. Meteorol. Soc., 79 (5), 815-829, doi: 10.1175/1520-0477(1998)079<0815:DDASVA>2.0.CO;2.
205. Njoku, E.G., and, D. Entekhabi, (1996), Passive microwave remote sensing of soil moisture, J. Hydrol., 184 (1), 101-129, doi: 10.1016/0022-1694(95)02970-2.
206. Njoku, E.G., T.J. Jackson, V. Lakshmi, T.K. Chan, and, S.V. Nghiem, (2003), Soil moisture retrieval from AMSR-E, IEEE Trans. Geosci. Remote Sens., 41 (2), 215-229, doi: 10.1109/TGRS.2002.808243.
207. Norman, J.M., M. Divakarla, and, N.S. Goel, (1995), Algorithms for extracting information from remote thermal-IR observations of the Earth's surface, Remote Sens. Environ., 51 (1), 157-168, doi: 10.1016/0034-4257(94)00072-U.
208. Norouzi, H., M. Temimi, W. Rossow, C. Pearl, M. Azarderakhsh, and, R. Khanbilvardi, (2011), The sensitivity of land emissivity estimates from AMSR-E at C and X bands to surface properties, Hydrol. Earth Syst. Sci., 15 (11), 3577-3589, doi: 10.5194/hess-15-3577-2011.
209. Norouzi, H., W. Rossow, M. Temimi, C. Prigent, M. Azarderakhsh, S. Boukabara, and, R. Khanbilvardi, (2012), Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land, Remote Sens. Environ., 123, 470-482, doi: 10.1016/j.rse.2012.04.015.
210. Norouzi, H., M. Temimi, C. Prigent, J. Turk, R. Khanbilvardi, Y. Tian, F. Furuzawa, and, H. Masunaga, (2014), Assessment of the consistency among global microwave land surface emissivity products, Atmos. Meas. Tech. Discuss., 7 (9), 9993-10,013, doi: 10.5194/amtd-7-9993-2014.
211. O'grady, A.P., X. Chen, D. Eamus, and, L.B. Hutley, (2000), Composition, leaf area index and standing biomass of eucalypt open forests near Darwin in the Northern Territory, Australia, Aust. J. Bot., 48 (5), 629-638, doi: 10.1071/BT99022.
212. Otkin, J.A., M.C. Anderson, C. Hain, and, M. Svoboda, (2014), Examining the relationship between drought development and rapid changes in the Evaporative Stress Index, J. Hydrometeorol., 15 (3), 938-956, doi: 10.1175/JHM-D-13-0110.1.
213. Owe, M., R. de Jeu, and, J. Walker, (2001), A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index, IEEE Trans. Geosci. Remote Sens., 39 (8), 1643-1654, doi: 10.1109/36.942542.
214. Painter, T.H., F.C. Seidel, A.C. Bryant, S. McKenzie Skiles, and, K. Rittger, (2013), Imaging spectroscopy of albedo and radiative forcing by light-absorbing impurities in mountain snow, J. Geophys. Res. Atmos., 118, 9511-9523, doi: 10.1002/jgrd.50520.
215. Palmer, W., (1965), Meteorological drought, Tech. Rep., Weather Bureau Res. Pap. 45, U. S. Dept. of Commerce, 58 pp.
216. Palmer, W.C., (1968), Keeping track of crop moisture conditions, nationwide: The new crop moisture index, Weatherwise, 21 (4), 156-161, doi: 10.1080/00431672.1968.9932814.
217. Palmer, W.C., and, A.V. Havens, (1958), A graphical technique for determining evapotranspiration by the Thornthwaite method, Mon. Weather Rev., 86 (4), 123-128.
218. Paridal, B.R., W.B. Collado, R. Borah, M.K. Hazarika, and, L. Sarnarakoon, (2008), Detecting drought-prone areas of rice agriculture using a MODIS-derived soil moisture index, GISci. Remote Sens., 45 (1), 109-129, doi: 10.2747/1548-1603.45.1.109.
219. Park, J.-S., K.-T. Kim, and, Y.-S. Choi, (2008), Application of vegetation condition index and standardized vegetation index for assessment of spring drought in South Korea, in Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, vol. 3, pp. 774-777, IEEE, Boston, Mass.
220. Patel, N., B. Parida, V. Venus, S. Saha, and, V. Dadhwal, (2012), Analysis of agricultural drought using Vegetation Temperature Condition Index (VTCI) from TERRA/MODIS satellite data, Environ. Monit. Assess., 184 (12), 7153-7163.
221. Payero, J., C. Neale, and, J. Wright, (2004), Comparison of eleven vegetation indices for estimating plant height of alfalfa and grass, Appl. Eng. Agric., 20 (3), 385-393.
222. Perry, C.R., and, L.F. Lautenschlager, (1984), Functional equivalence of spectral vegetation indices, Remote Sens. Environ., 14 (1), 169-182.
223. Peters, A.J., E.A. Walter-Shea, L. Ji, A. Vina, M. Hayes, and, M.D. Svoboda, (2002), Drought monitoring with NDVI-based standardized vegetation index, Photogramm. Eng. Remote Sens., 68 (1), 71-75.
224. Peters-Lidard, C.D., D.M. Mocko, M. Garcia, J.A. Santanello, M.A. Tischler, M.S. Moran, and, Y. Wu, (2008), Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment, Water Resour. Res., 44, W05S18, doi: 10.1029/2007WR005884.
225. Phillips, O. L., et al., (1998), Changes in the carbon balance of tropical forests: Evidence from long-term plots, Science, 282 (5388), 439-442.
226. Piechota, T.C., and, J.A. Dracup, (1996), Drought and regional hydrologic variation in the United States: Associations with the El Niño-Southern Oscillation, Water Resour. Res., 32 (5), 1359-1373.
227. Pinker, R.T., D. Sun, M.-P. Hung, C. Li, and, J.B. Basara, (2009), Evaluation of satellite estimates of land surface temperature from GOES over the United States, J. Appl. Meteorol. Climatol., 48 (1), 167-180.
228. Pinzon, J.E., and, C.J. Tucker, (2014), A non-stationary 1981-2012 AVHRR NDVI3g time series, Remote Sens., 6 (8), 6929-6960, doi: 10.3390/rs6086929.
229. Pook, E.W., (1985), Canopy dynamics of Eucalyptus maculata Hook. III. Effects of drought, Aust. J. Bot., 33 (1), 65-79.
230. Pook, E.W., (1986), Canopy dynamics of Eucalyptus-maculata Hook. 4. Contrasting responses to two severe droughts, Aust. J. Bot., 34 (1), 1-14.
231. Pook, E.W., A.M. Gill, and, P.H.R. Moore, (1997), Long-term variation of litter fall, canopy leaf area and flowering in a Eucalyptus maculata forest on the south coast of New South Wales, Aust. J. Bot., 45 (5), 737-755, doi: 10.1071/BT95063.
232. Poulter, B., et al., (2014), Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 509 (7502), 600-603.
233. Price, J.C., (1982), Estimation of regional scale evapotranspiration through analysis of satellite thermal-infrared data, IEEE Trans. Geosci. Remote Sens., GE-20 (3), 286-292.
234. Prince, S.D., D. Colstoun, E. Brown, and, L. Kravitz, (1998), Evidence from rain-use efficiencies does not indicate extensive Sahelian desertification, Global Change Biol., 4 (4), 359-374.
235. Qin, Q., C. Jin, N. Zhang, and, X. Yang, (2010), An two-dimensional spectral space based model for drought monitoring and its re-examination, in Geoscience and Remote Sensing Symposium (IGARSS), 2010, pp. 3869-3872, IEEE.
236. Quiring, S.M., and, S. Ganesh, (2010), Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas, Agric. For. Meteorol., 150 (3), 330-339.
237. Rajsekhar, D., V.P. Singh, and, A.K. Mishra, (2014), Multivariate drought index: An information theory based approach for integrated drought assessment, J. Hydrol., 526, 164-182, doi: 10.1016/j.jhydrol.2014.11.031.
238. Rasmusson, E. M., et al., (1983), Meteorological aspects of the El Niño-Southern Oscillation, Science, 222 (4629), 1195-1202.
239. Reichle, R.H., R.D. Koster, J. Dong, and, A.A. Berg, (2004), Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation, J. Hydrometeorol., 5 (3), 430-442.
240. Reichle, R. H., R. D. Koster, G. J. De Lannoy, B. A. Forman, Q. Liu, S. P. Mahanama, and, A. Touré, (2011), Assessment and enhancement of MERRA land surface hydrology estimates, J. Clim., 24 (24), 6322-6338.
241. 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 (12), 2875-2887.
242. Richard, Y., and, I. Poccard, (1998), A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa, Int. J. Remote Sens., 19 (15), 2907-2920.
243. Rodell, M., (2012), Satellite gravimetry applied to drought monitoring, in Remote Sensing of Drought: Innovative Monitoring Approaches, pp. 261-277, CRC Press.
244. Rodell, M., and, J. Famiglietti, (2002), The potential for satellite-based monitoring of groundwater storage changes using GRACE: The high plains aquifer, central US, J. Hydrol., 263 (1), 245-256.
245. Rodell, M., J. Chen, H. Kato, J.S. Famiglietti, J. Nigro, and, C.R. Wilson, (2007), Estimating groundwater storage changes in the Mississippi River Basin (USA) using GRACE, Hydrogeol. J., 15 (1), 159-166.
246. Roderick, M.L., I.R. Noble, and, S.W. Cridland, (1999), Estimating woody and herbaceous vegetation cover from time series satellite observations, Global Ecol. Biogeogr., 8 (6), 501-508.
247. Romanov, P., G. Gutman, and, I. Csiszar, (2000), Automated monitoring of snow cover over North America with multispectral satellite data, J. Appl. Meteorol., 39 (11), 1866-1880.
248. Rott, H., et al., (2010), Cold regions hydrology high-resolution observatory for snow and cold land processes, Proc. IEEE, 98 (5), 752-765.
249. Rouse, J., R. Haas, J. Schell, D. Deering, and, J. Harlan, (1974), Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation, 362 pp., Texas A & M Univ., Remote Sens. Cent., College Station, Tex.
250. Running, S.W., R.R. Nemani, D.L. Peterson, L.E. Band, D.F. Potts, L.L. Pierce, and, M.A. Spanner, (1989), Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation, Ecology, 70 (4), 1090-1101.
251. Running, S.W., R.R. Nemani, F.A. Heinsch, M. Zhao, M. Reeves, and, H. Hashimoto, (2004), A continuous satellite-derived measure of global terrestrial primary production, Bioscience, 54 (6), 547-560.
252. Saleska, S.R., K. Didan, A.R. Huete, and, H.R. da Rocha, (2007), Amazon forests green-up during 2005 drought, Science, 318 (5850), 612, doi: 10.1126/science.1146663.
253. Santos, J.F., I. Pulido-Calvo, and, M.M. Portela, (2010), Spatial and temporal variability of droughts in Portugal, Water Resour. Res., 46, W03503, doi: 10.1029/2009WR008071.
254. Schanda, E., C. Matzler, and, K. Kunzi, (1983), Microwave remote sensing of snow cover, Int. J. Remote Sens., 4 (1), 149-158.
255. Seiler, R., F. Kogan, and, J. Sullivan, (1998), AVHRR-based vegetation and temperature condition indices for drought detection in Argentina, Adv. Space Res., 21 (3), 481-484.
256. Sellars, S., P. Nguyen, W. Chu, X. Gao, K.-l. Hsu, and, S. Sorooshian, (2013), Computational Earth science: Big data transformed into insight, EOS Trans. AGU, 94 (32), 277-278.
257. Senay, G.B., (2008), Modeling landscape evapotranspiration by integrating land surface phenology and a water balance algorithm, Algorithms, 1 (2), 52-68.
258. Senay, G.B., M. Budde, J.P. Verdin, and, A.M. Melesse, (2007), A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields, Sensors, 7 (6), 979-1000.
259. Senay, G.B., S. Bohms, and, J.P. Verdin, (2012), Remote sensing of evapotranspiration for operational drought monitoring using principles of water and energy balance, in Remote Sensing of Drought: Innovative Monitoring Approaches, edited by, B.D. Wardlow, M.C. Anderson, and, J.P. Verdin, pp. 123-144, CRC Press.
260. Sheffield, J., G. Goteti, F. Wen, and, E. Wood, (2004), A simulated soil moisture based drought analysis for the United States, J. Geophys. Res., 109, D24108, doi: 10.1029/2004JD005182.
261. Sheffield, J., G. Goteti, and, E. Wood, (2006), Development of a 50-yr, high resolution global dataset of meteorological forcings for land surface modeling, J. Clim., 13, 3088-3111.
262. Sheffield, J., E. Wood, and, M. Roderick, (2012), Little change in global drought over the past 60 years, Nature, 491 (7424), 435-438.
263. Shen, H.W., and, G.Q. Tabios, (1996), Modeling of Precipitation-Based Drought Characteristics Over California, Centers for Water and Wildland Resources, Series: California Water Resources Center, 204. [Available at http://library.wur.nl/WebQuery/clc/929134.]
264. Shi, J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, and, K. Chen, (2008), Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E, Remote Sens. Environ., 112 (12), 4285-4300.
265. Silleos, N.G., T.K. Alexandridis, I.Z. Gitas, and, K. Perakis, (2006), Vegetation indices: Advances made in biomass estimation and vegetation monitoring in the last 30 years, Geocarto Int., 21 (4), 21-28.
266. Simpson, J., J. Stitt, and, M. Sienko, (1998), Improved estimates of the areal extent of snow cover from AVHRR data, J. Hydrol., 204 (1), 1-23.
267. Singh, R.P., S. Roy, and, F. Kogan, (2003), Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India, Int. J. Remote Sens., 24 (22), 4393-4402.
268. Smith, P., et al., (2010), European-wide simulations of croplands using an improved terrestrial biosphere model: Phenology and productivity, J. Geophys. Res., 115, G01014, doi: 10.1029/2008JG000800.
269. Son, N., C. Chen, C. Chen, L. Chang, and, V. Minh, (2012), Monitoring agricultural drought in the lower Mekong basin using MODIS NDVI and land surface temperature data, Int. J. Appl. Earth Obs. Geoinf., 18, 417-427.
270. Sorooshian, S., K. Hsu, X. Gao, H. Gupta, B. Imam, and, D. Braithwaite, (2000), Evolution of the PERSIANN system satellite-based estimates of tropical rainfall, Bull. Am. Meteorol. Soc., 81 (9), 2035-2046.
271. Sorooshian, S., et al., (2011), Advanced concepts on remote sensing of precipitation at multiple scales, Bull. Am. Meteorol. Soc., 92 (10), 1353-1357.
272. Specht, R., (1972), Water use by perennial evergreen plant communities in Australia and Papua New Guinea, Aust. J. Bot., 20 (3), 273-299, doi: 10.1071/BT9720273.
273. Su, H., M. McCabe, E. Wood, Z. Su, and, J. Prueger, (2005), Modeling evapotranspiration during SMACEX: Comparing two approaches for local-and regional-scale prediction, J. Hydrometeorol., 6 (6), 910-922.
274. Sun, D., and, M. Kafatos, (2007), Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America, Geophys. Res. Lett., 34, L24406, doi: 10.1029/2007GL031485.
275. Svoboda, M., et al., (2002), The drought monitor, Bull. Am. Meteorol. Soc., 83 (8), 1181-1190.
276. Swain, S., B.D. Wardlow, S. Narumalani, T. Tadesse, and, K. Callahan, (2011), Assessment of vegetation response to drought in Nebraska using TERRA-MODIS land surface temperature and normalized difference vegetation index, GISci. Remote Sens., 48 (3), 432-455.
277. Tadesse, T., J. Brown, and, M. Hayes, (2005), A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the US central plains, ISPRS J. Photogramm. Remote Sens., 59 (4), 244-253.
278. Takada, M., Y. Mishima, and, S. Natsume, (2009), Estimation of surface soil properties in peatland using ALOS/PALSAR, Landscape Ecol. Eng., 5 (1), 45-58.
279. Tedesco, M., and, E.J. Kim, (2006), Retrieval of dry-snow parameters from microwave radiometric data using a dense-medium model and genetic algorithms, IEEE Trans. Geosci. Remote Sens., 44 (8), 2143-2151.
280. Thiam, A.K., (1998), Geographic Information Systems and Remote Sensing Methods for Assessing and Monitoring Land Degradation in the Sahel Region: The Case of Southern Mauritania, Clark Univ., Worcester, Mass. [Available at http://adsabs.harvard.edu/abs/1998PhDT.94T.]
281. Thomas, A.C., J.T. Reager, J.S. Famiglietti, and, M. Rodell, (2014), A GRACE-based water storage deficit approach for hydrological drought characterization, Geophys. Res. Lett., 41, 1537-1545, doi: 10.1002/2014GL059323.
282. Tian, Y., C. Peters-Lidard, J. Eylander, R. Joyce, G. Huffman, R. Adler, K. Hsu, F. Turk, M. Garcia, and, J. Zeng, (2009), Component analysis of errors in satellite-based precipitation estimates, J. Geophys. Res., 114, D24101, doi: 10.1029/2009JD011949.
283. Tsakiris, G., and, H. Vangelis, (2005), Establishing a drought index incorporating evapotranspiration, European Water, 9 (10), 3-11.
284. Tsakiris, G., D. Pangalou, and, H. Vangelis, (2007), Regional drought assessment based on the Reconnaissance Drought Index (RDI), Water Resour. Manage., 21 (5), 821-833.
285. Tucker, C.J., (1979), Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8 (2), 127-150.
286. Tucker, C.J., and, B.J. Choudhury, (1987), Satellite remote sensing of drought conditions, Remote Sens. Environ., 23 (2), 243-251.
287. Tucker, C.J., J.E. Pinzon, M.E. Brown, D.A. Slayback, E.W. Pak, R. Mahoney, E.F. Vermote, and, N. El Saleous, (2005), An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26 (20), 4485-4498.
288. Turk, F.J., G.D. Rohaly, J. Hawkins, E.A. Smith, F.S. Marzano, A. Mugnai, and, V. Levizzani, (1999), Meteorological applications of precipitation estimation from combined SSM/I, TRMM and infrared geostationary satellite data, in Microwave Radiometry and Remote Sensing of the Earth's Surface and Atmosphere, edited by, P. Pampaloni, and, S. Paloscia, pp. 353-363, VSP Int. Sci. Publisher, Utrecht, Netherlands.
289. UNESCO (1979), Map of the world distribution of arid regions, Tech. Rep., The United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, France.
290. Unganai, L.S., and, F.N. Kogan, (1998), Drought monitoring and corn yield estimation in southern Africa from AVHRR data, Remote Sens. Environ., 63 (3), 219-232.
291. Van der Molen, M., et al., (2011), Drought and ecosystem carbon cycling, Agric. For. Meteorol., 151 (7), 765-773.
292. van Dijk, A.I.J.M., L. Renzullo, and, M. Rodell, (2011), Use of Gravity Recovery and Climate Experiment terrestrial water storage retrievals to evaluate model estimates by the Australian water resources assessment system, Water Resour. Res., 47, W11524, doi: 10.1029/2011WR010714.
293. van Dijk, A.I.J.M., H.E. Beck, R.S. Crosbie, R.A. Jeu, Y.Y. Liu, G.M. Podger, B. Timbal, and, N.R. Viney, (2013), The millennium drought in southeast Australia (2001-2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, 1040-1057, doi: 10.1002/wrcr.20123.
294. Van Niel, T., T.R. McVicar, H. Fang, and, S. Liang, (2003), Calculating environmental moisture for per-field discrimination of rice crops, Int. J. Remote Sens., 24 (4), 885-890.
295. Vargas, M., T. Miura, N. Shabanov, and, A. Kato, (2013), An initial assessment of SUOMI NPP VIIRS vegetation index EDR, J. Geophys. Res. Atmos., 118, 12-301, doi: 10.1002/2013JD020439.
296. Vetter, M., et al., (2008), Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models, Biogeosciences, 5 (2), 561-583.
297. Vicente-Serrano, S.M., S. Beguera, J. Lorenzo-Lacruz, J.J. Camarero, J.I. Lpez-Moreno, C. Azorin-Molina, J. Revuelto, E. Morn-Tejeda, and, A. Sanchez-Lorenzo, (2012), Performance of drought indices for ecological, agricultural, and hydrological applications, Earth Interact., 16 (10), 1-27, doi: 10.1175/2012EI000434.1.
298. Wagner, W., J. Noll, M. Borgeaud, and, H. Rott, (1999), Monitoring soil moisture over the Canadian prairies with the ERS scatterometer, IEEE Trans. Geosci. Remote Sens., 37 (1), 206-216.
299. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, and, M. Ertl, (2012), Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture, XXII ISPRS Congress, Melbourne, Australia. [Available at http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-7/315/2012/ isprsannals-I-7-315-2012.pdf.]
300. Wan, Z., P. Wang, and, X. Li, (2004), Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA, Int. J. Remote Sens., 25 (1), 61-72.
301. Wang, A., T.J. Bohn, S.P. Mahanama, R.D. Koster, and, D.P. Lettenmaier, (2009), Multimodel ensemble reconstruction of drought over the continental United States, J. Clim., 22 (10), 2694-2712.
302. Wang, J., K. Price, and, P. Rich, (2001), Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains, Int. J. Remote Sens., 22 (18), 3827-3844.
303. Wang, K., and, R.E. Dickinson, (2012), A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability, Rev. Geophys., 50, RG2005, doi: 10.1029/2011RG000373.
304. Wang, L., and, J.J. Qu, (2007), NMDI: A Normalized Multi-band Drought Index for monitoring soil and vegetation moisture with satellite remote sensing, Geophys. Res. Lett., 34, L20405, doi: 10.1029/2007GL031021.
305. Wang, L., and, J.J. Qu, (2009), Satellite remote sensing applications for surface soil moisture monitoring: A review, Front. Earth Sci. Chin., 3 (2), 237-247.
306. Wang, L., J.J. Qu, X. Hao, and, Q. Zhu, (2008), Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices, Int. J. Remote Sens., 29 (24), 7065-7075.
307. Wang, Y., R. Law, and, B. Pak, (2010), A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7 (7), 2261-2282.
308. Wardlow, B., M.C. Anderson, and, J. Verdin, (2012), Remote Sensing of Drought: Innovative Monitoring Approaches, pp. 484, CRC Press.
309. Wegren, S.K., (2011), Food security and Russia's 2010 drought, Eurasian Geogr. Econ., 52 (1), 140-156.
310. Welsch, C., H. Swenson, S.A. Cota, F. DeLuccia, J.M. Haas, C. Schueler, R.M. Durham, J.E. Clement, and, P.E. Ardanuy, (2001), VIIRS (visible infrared imager radiometer suite): A next-generation operational environmental sensor for NPOESS, in Geoscience and Remote Sensing Symposium, 2001. IGARSS'01, vol. 3, pp. 1020-1022, IEEE, Sydney, NSW.
311. Werick, W., G. Willeke, N. Guttman, J. Hosking, and, J. Wallis, (1994), National drought atlas developed, EOS Trans. AGU, 75 (8), 89.
312. Wiegand, C., A. Richardson, D. Escobar, and, A. Gerbermann, (1991), Vegetation indices in crop assessments, Remote Sens. Environ., 35 (2), 105-119.
313. Wiesnet, D., (1981), Winter snow drought, EOS Trans. AGU, 62 (14), 137-137.
314. Wilhite, D.A., (2005), Drought and Water Crises: Science, Technology, and Management Issues, pp. 432, vol.86, CRC Press.
315. Wilson, W.J., S.H. Yueh, S.J. Dinardo, S.L. Chazanoff, A. Kitiyakara, F.K. Li, and, Y. Rahmat-Samii, (2001), Passive active L-and S-band (pals) microwave sensor for ocean salinity and soil moisture measurements, IEEE Trans. Geosci. Remote Sens., 39 (5), 1039-1048.
316. Wiscombe, W.J., and, S.G. Warren, (1980), A model for the spectral albedo of snow. I: Pure snow, J. Atmos. Sci., 37 (12), 2712-2733.
317. WMO (2009), Inter-Regional Workshop on Indices and Early Warning Systems for Drought, World Meteorological Organization, Lincoln, Nebraska, 8-11 December 2009.
318. Woodwell, G.M., and, R.H. Whittaker, (1968), Primary production in terrestrial ecosystems, Am. Zool., 8 (1), 19-30.
319. Yang, Y., and, S. Shang, (2013), A hybrid dual-source scheme and trapezoid framework-based evapotranspiration model (HTEM) using satellite images: Algorithm and model test, J. Geophys. Res. Atmos., 118, 2284-2300, doi: 10.1002/jgrd.50259.
320. Yang, Y., D. Long, H. Guan, B.R. Scanlon, C.T. Simmons, L. Jiang, and, X. Xu, (2014), GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia, J. Geophys. Res. Biogeosci., 119, 2245-2260, doi: 10.1002/2014JG002670.
321. Yao, Y., S. Liang, Q. Qin, and, K. Wang, (2010), Monitoring drought over the Conterminous United States using MODIS and NCEP reanalysis-2 data, J. Appl. Meteorol. Climatol., 49 (8), 1665-1680.
322. Yao, Y., S. Liang, Q. Qin, K. Wang, and, S. Zhao, (2011), Monitoring global land surface drought based on a hybrid evapotranspiration model, Int. J. Appl. Earth Obs. Geoinf., 13 (3), 447-457.
323. Yebra, M., A.I.J.M. van Dijk, R. Leuning, A. Huete, and, J.P. Guerschman, (2013), Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance, Remote Sens. Environ., 129, 250-261.
324. Yin, D., M.L. Roderick, G. Leech, F. Sun, and, Y. Haung, (2014), The contribution of reduction in evaporative cooling to higher surface air temperatures during drought, Geophys. Res. Lett., 41, 7891-7897, doi: 10.1002/2014GL062039.
325. Yirdaw, S.Z., K.R. Snelgrove, and, C.O. Agboma, (2008), GRACE satellite observations of terrestrial moisture changes for drought characterization in the Canadian prairie, J. Hydrol., 356 (1), 84-92.
326. Yuan, W., et al., (2014), Multiyear precipitation reduction strongly decreases carbon uptake over northern China, J. Geophys. Res. Biogeosci., 119, 881-896, doi: 10.1002/2014JG002608.
327. Zaitchik, B.F., M. Rodell, and, R.H. Reichle, (2008), Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River Basin, J. Hydrometeorol., 9 (3), 535-548.
328. Zhang, A., and, G. Jia, (2013), Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data, Remote Sens. Environ., 134 (12-13), 12-23.
329. Zhang, N., Y. Hong, Q. Qin, and, L. Liu, (2013), VSDI: A Visible and Shortwave infrared Drought Index for monitoring soil and vegetation moisture based on optical remote sensing, Int. J. Remote Sens., 34 (13), 4585-4609.