A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

被引:10
|
作者
Kalluri, SNV [1 ]
Townshend, JRG
Doraiswamy, P
机构
[1] Univ Maryland, Dept Geog, College Pk, MD 20742 USA
[2] Univ Maryland, LGRSS, College Pk, MD 20742 USA
[3] USDA ARS, Beltsville, MD 20705 USA
关键词
D O I
10.1080/014311698215595
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
A methodology is developed here to model evapotranspiration (lambda E-c) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where lambda E-c, is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground Bur (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resistance, a model is developed to scale the transpiration estimates from the full canopy to give an area averaged estimate from the mean NDVI of the study area. The model was tested using data collected from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), and the results show that the modelled values of total surface evapotranspiration from the soil and canopy layers vary from the ground measurements by less than 9 per cent.
引用
收藏
页码:1037 / 1053
页数:17
相关论文
共 50 条
  • [31] Sparse Reconstruction of Compressive Sensing Multi-Spectral Data Using an Inter-Spectral Multi-Layered Conditional Random Field Model
    Li, Edward
    Shafiee, Mohammad Javad
    Kazemzadeh, Farnoud
    Wong, Alexander
    IEEE ACCESS, 2016, 4 : 5540 - 5554
  • [32] Extraction of spectral reflectance images from multi-spectral images by the HIS transformation model
    Qi, Z
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1996, 17 (17) : 3467 - 3475
  • [33] Stochastic EM algorithm for fast analysis of single waveform multi-spectral Lidar data
    Legros, Q.
    McLaughlin, S.
    Altmann, Y.
    Meignen, S.
    28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 2021, : 2413 - 2417
  • [34] Landscape modelling using integrated airborne multi-spectral and laser scanning data
    Hill, RA
    Smith, GM
    Fuller, RM
    Veitch, N
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (11) : 2327 - 2334
  • [35] Detection of faint companions in multi-spectral data using a maximum likelihood approach
    Hanley, Kenneth
    Devaney, Nicholas
    Thiebaut, Eric
    ADAPTIVE OPTICS SYSTEMS V, 2016, 9909
  • [36] Detection of Artificially Ripened Banana Using Spectral Signature From Multi-Spectral Imaging
    Vetrekar, Narayan
    Ramachandra, Raghavendra
    Raja, Kiran B.
    Gad, R. S.
    Naik, Aparajita
    Prabhu, Anish
    4TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES; MICRO TO NANO, 2019: (ETMN 2019), 2021, 2335
  • [37] Prediction of the Spectral Transmittance and Reflectance of Multi-Layer Materials from Single-Layer Data
    Lashansky, Shimshon
    Erez, Yuval
    JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY, 2014, 58 (06)
  • [38] Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data
    Eckardt, Robert
    Berger, Christian
    Thiel, Christian
    Schmullius, Christiane
    REMOTE SENSING, 2013, 5 (06) : 2973 - 3006
  • [39] Characterizing seabed sediments using multi-spectral backscatter data in the North Sea
    Bai, Qian
    Mestdagh, Sebastiaan
    Snellen, Mirjam
    Amiri-Simkooei, AliReza
    OCEANS 2023 - LIMERICK, 2023,
  • [40] Underwater Multi-Spectral Photometric Stereo Reconstruction from a Single RGBD Image
    Jiao, Hengchao
    Luo, Yisong
    Wang, Nan
    Qi, Lin
    Dong, Junyu
    Lei, Hansheng
    2016 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA), 2016,