Study on component temperatures inversion using satellite remotely sensed data

  • Authors:

  • X. Song;Y. Zhao

  • Affiliations:

  • College of Resources and Environment, Graduate School of Chinese Academy of Sciences, Beijing 100049, PR China,State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 10 ...;College of Resources and Environment, Graduate School of Chinese Academy of Sciences, Beijing 100049, PR China

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2007

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Abstract

Because of the low spatial resolution of MODIS data, it is important to decompose mixed pixels to retrieve component temperatures using thermal infrared bands. Grasslands with different coverage conditions are prominent in the area under study. Because of the simple vegetation structure, radiation is less influenced by vegetation shade. If the internal structure of the component parts of the mixed pixel is ignored, the total radiation emitted by the mixed pixel is approximately the sum of the radiation emitted by each component part of the pixel, weighted according to the percentage area of each component part. Vegetation/soil component temperatures based on the sub-pixel scale are inverted using a constrained optimization algorithm-the genetic algorithm. The study not only broadens the application of the linear spectral mixing model but also develops a practical method for component temperatures retrieval from MODIS satellite data. The results provide more precise parameters for estimation of land surface energy balance and evapotranspiration.