Assimilation of land surface temperature data from ATSR in an NWP environment - a case study

B.J.J.M. van den Hurk, L. Jia, C. Jacobs, M. Menenti, Z.L. Li

    Research output: Contribution to journalArticleAcademicpeer-review

    12 Citations (Scopus)

    Abstract

    Directional thermal observations from the ATSR-2 satellite sensor were used to estimate separate vegetation and soil temperatures for a number of cloud free scenes covering south-east Spain over five days in 1999. Underlying methodology for this is a simplified radiative transfer scheme and a concurrent estimate of the fraction of ground covered with vegetation. The vegetation and soil temperatures were used together with near-surface relative humidity measurements to adjust the root zone soil moisture content and roughness length for heat in a newly developed multi-component land surface parameterization scheme, embedded in a regional weather forecast model. The ATSR surface temperature data have a strong influence on the modification of the thermal roughness length. The optimal roughness length gradually changes over the growing season, as can be expected from the dependence of thermal roughness on vegetation density. Application of the method to a grassland scene in The Netherlands resulted in amuch smaller adjustment to the thermal roughness length. The distribution of the roughness over the Spanish test area appeared to be consistent in time, as correlation coefficients of roughness values between two subsequent acquisition dates were significantly positive. Small improvements in the calculated surface energy balance appear from independent near-surface air temperature observations in the Spanish area. The use of bi-angular thermal infrared observations seems useful to improve the description of aerodynamic roughness properties on regional scales.
    Original languageEnglish
    Pages (from-to)5193-5209
    JournalInternational Journal of Remote Sensing
    Volume23
    Issue number24
    DOIs
    Publication statusPublished - 2002

      Fingerprint

    Keywords

    • remote sensing

    Cite this