The objective in this work is to develop downscalingmethodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the Global Inundation Extent fromMulti-Satellites (GIEMS) dataset. In semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from theModerateResolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta whereMODIS-derived inundation extent has been estimated at a 500-m resolution. The space–time variability is first analyzed using a principal component analysis (PCA). This is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. Two innovative methods are developed (linear regression and matrix inversion) both based on the PCA representation. These GIEMS downscaling techniques have been calibrated using the 500-mMODIS data. The downscaled fields show the expected space–time behaviors from MODIS. A 20-yr dataset of the inundation extent at 500m is derived from this analysis for the InnerNigerDelta. Themethods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori high-spatial-resolution information is available. The derived high-spatial-resolution dataset will be used in the framework of the Surface Water Ocean Topography (SWOT) mission to develop and test the instrument

Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land

A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature

[1] Despite the abundance and variety of remote sensing measurements, land surface characterization from satellite observations is still very challenging. The links between the three sources of surface information, namely the satellite observations, the in situ measurements, and the land surface model outputs, are complex. Innovative techniques have to be developed to merge these information sources and optimize the use of satellite measurements for better surface products and more predictability. Concepts such as multi-instrument/multiparameter retrieval algorithms are discussed, as well as the synergetic use of satellite observations, model outputs, and in situ data. The need for careful satellite calibration is stressed, and the scaling problem is emphasized. Recent results are reviewed to indicate what the land surface remote sensing problems are and how they might be attacked. Two concrete applications are presented: an ‘‘all weather’’ retrieval of surface skin temperature from combined microwave and infrared observations and a soil moisture analysis from the merging of multisatellite observations and land surface model outputs. This paper is intended to stimulate debates and collaborations between the land surface modelers and the satellite remote sensing community for the design of the next generation of land surface products. Citation: Aires, F., and C. Prigent (2006), Toward a new generation of satellite surface products?, J. Geophys. Res., 111, D22S10, doi:10.1029/2006JD007362.

[1] A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993–1995 is presented. The intercomparison includes some of the first emerging

remote sensing

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an estimation of global land surface heat

(LST) Products and their Applications

observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements