PROGRESS IN GEOGRAPHY ›› 2008, Vol. 27 ›› Issue (2): 53-59.doi: 10.11820/dlkxjz.2008.02.008

• Original Articles • Previous Articles     Next Articles

Resear ch on Tempor al Reconstruction of Evapotr anspir ation by Using Remote Sensing

XIONG Jun1, WU Bingfang1, YAN Nana1, HU Minggang2, SUN Minzhang3   

  1. 1. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101;
    2. Beijing Water Technology Center of Beijing Water Authority, Beijing 100073, China;
    3. China drainage Center, Minister of Water Rsources, Beijing, 100038. China
  • Received:2007-12-01 Revised:2008-02-01 Online:2008-03-25 Published:2008-03-25


Temporal reconstruction of evapotranspiration (ET), which means deriving continuous, complete annual ET from fragmentary satellite measurement, is a problem full of uncertainty in remote sensing application. Traditionally, evaporative fraction (LE/H) is simplified as constant in a short period so that weekly or long term ET could be estimated from a single clear satellite image. In this way, variation of daily ET is often neglected and the amount of ET is hard to compare with those at the same time in another year. The objective of this research is to develop a new reconstruction algorithm to retrieve continuous and actual ET dataset and provide valuable temporal profile for agriculture and ecology application. This algorithm considers both the spatial and temporal discontinuity, and is a combination of SEBS (Surface Energy Balance System) and Penman- Monteith model: SEBS model is used to derived latent heat in clear days, and then surface resistance is inverted from PM equation; Leaf area index (LAI), is interpolated and smoothed to daily term by using HANTS (Harmonic Analysis of Time Series) method. Then surface resistance of the cloudy days is related to those from neighboring clear days with a function of LAI, minimal air temperature and vapour pressure deficit. Daily ET estimation is compared to lysimeter measurements recorded in Yucheng agriculture site and shows a good correlation coefficient in crop growing season (R2≈0.7). Model result is not satisfactory on bare and sparse land because of the limitation of the one- layer assumption in PM equation, which requests that an independent component of soil evaporation should be added into the algorithm.

Key words: MODIS, regional evapotranspiration(ET), remote sensing, retrieve, temporal reconstruction