Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

被引:393
作者
Anderson, M. C. [1 ]
Kustas, W. P. [1 ]
Norman, J. M. [2 ]
Hain, C. R. [3 ]
Mecikalski, J. R. [4 ]
Schultz, L. [4 ]
Gonzalez-Dugo, M. P. [5 ]
Cammalleri, C. [6 ]
d'Urso, G. [7 ]
Pimstein, A. [8 ]
Gao, F. [9 ,10 ]
机构
[1] USDA, Beltsville, MD 20705 USA
[2] Univ Wisconsin, Dept Soil Sci, Madison, WI 53706 USA
[3] NOAA, IM Syst Grp, NESDIS, Camp Springs, MD USA
[4] Univ Alabama, Dept Atmospher Sci, Huntsville, AL 35899 USA
[5] IFAPA Andalusian Agr & Fisheries Dept, Cordoba, Spain
[6] Univ Palermo, Dept Civil Environ & Aerosp Eng, Palermo, Italy
[7] Univ Naples Federico II, Dept Agr Engn & Agron, Naples, Italy
[8] Pontificia Univ Catolica Chile, Dept Fruit Prod & Enol, Santiago, Chile
[9] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA
[10] Earth Resources Technol Inc, Laurel, MD USA
关键词
SURFACE-ENERGY FLUXES; RADIOMETRIC TEMPERATURE; VEGETATION INDEX; HEAT-FLUX; 2-SOURCE MODEL; BALANCE; SYSTEM; WATER; SOIL; EVAPORATION;
D O I
10.5194/hess-15-223-2011
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to subsurface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.
引用
收藏
页码:223 / 239
页数:17
相关论文
共 67 条
  • [21] D'Urso G., 2001, Simulation and management of on-demand irrigation systems
  • [22] DERBER JC, 1991, WEATHER FORECAST, V6, P538, DOI 10.1175/1520-0434(1991)006<0538:TNGOAS>2.0.CO
  • [23] 2
  • [24] Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy
    Detto, Matteo
    Montaldo, Nicola
    Albertson, John D.
    Mancini, Marco
    Katul, Gaby
    [J]. WATER RESOURCES RESEARCH, 2006, 42 (08)
  • [25] DIAZ A, 2009, REMOTE SENSING AGR E, V11, P7472, DOI DOI 10.1117/1112.830371
  • [26] Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation, analysis and validation
    Ganguly, Sangram
    Samanta, Arindam
    Schull, Mitchell A.
    Shabanov, Nikolay V.
    Milesi, Cristina
    Nemani, Ramakrishna R.
    Knyazikhin, Yuri
    Myneni, Ranga B.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2008, 112 (12) : 4318 - 4332
  • [27] On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance
    Gao, Feng
    Masek, Jeff
    Schwaller, Matt
    Hall, Forrest
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (08): : 2207 - 2218
  • [28] Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops
    Gonzalez-Dugo, M. P.
    Mateos, L.
    [J]. AGRICULTURAL WATER MANAGEMENT, 2008, 95 (01) : 48 - 58
  • [29] Goudriaan J., 1977, SIMULATION MONOGRAPH
  • [30] OBSERVED RELATION BETWEEN THERMAL EMISSION AND REFLECTED SPECTRAL RADIANCE OF A COMPLEX VEGETATED LANDSCAPE
    GOWARD, SN
    CRUICKSHANKS, GD
    HOPE, AS
    [J]. REMOTE SENSING OF ENVIRONMENT, 1985, 18 (02) : 137 - 146