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United States Department of Agriculture

Agricultural Research Service

Research Project: REMOTE SENSING FOR CROP AND WATER MANAGEMENT IN IRRIGATED AGRICULTURE Title: Foreword to the Special Issue on Remote Sensing and Modeling of Surface Properties

Authors
item Karbou, Fatima -
item Weng, Fuzhong -
item French, Andrew

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Other
Publication Acceptance Date: March 9, 2011
Publication Date: April 1, 2011
Citation: Karbou, F., Weng, F., French, A.N. 2011. Foreword to the special issue on remote sensing and modeling of surface properties. IEEE Transactions on Geoscience and Remote Sensing. 49(4):1175-1176.

Interpretive Summary: CURRENTLY, the Numerical Weather Prediction (NWP) community is striving for better ways to extract information on the lower layer using current and future satellite systems to improve short-term to medium-range forecasts. The surface emissivity is highly variable and may cause biases in the forward model if its variability is not well taken into account. One consequence of this is the inability of the forward model to produce realistic simulations of surface-sensitive channels and therefore to reject many sounding channel measurements during the data assimilation process. Thus, assimilation of the surface-sensitive channel data from satellites in NWP models becomes a rapidly growing area of research which requires accurate surface emissivity models/database and more realistic estimates of surface temperature. For open seas, the emissivity is sufficiently accurate to meet NWP requirements, except at high microwave frequencies. Over land and sea-ice-covered areas, emissivity can be simulated, but with less accuracy. As a result, surface temperatures from NWP analyses and shortrange forecasts are biased, particularly so for polar regions and deserts. It is believed that addressing these and other surface modeling challenges will allow major breakthroughs in the fields of meteorology, data assimilation, and remote sensing.

Technical Abstract: CURRENTLY, the Numerical Weather Prediction (NWP) community is striving for better ways to extract information on the lower layer using current and future satellite systems to improve short-term to medium-range forecasts. The surface emissivity is highly variable and may cause biases in the forward model if its variability is not well taken into account. One consequence of this is the inability of the forward model to produce realistic simulations of surface-sensitive channels and therefore to reject many sounding channel measurements during the data assimilation process. Thus, assimilation of the surface-sensitive channel data from satellites in NWP models becomes a rapidly growing area of research which requires accurate surface emissivity models/database and more realistic estimates of surface temperature. For open seas, the emissivity is sufficiently accurate to meet NWP requirements, except at high microwave frequencies. Over land and sea-ice-covered areas, emissivity can be simulated, but with less accuracy. As a result, surface temperatures from NWP analyses and shortrange forecasts are biased, particularly so for polar regions and deserts. It is believed that addressing these and other surface modeling challenges will allow major breakthroughs in the fields of meteorology, data assimilation, and remote sensing. From June 9 to June 11, 2009, the 2nd workshop on “Remote Sensing and Modeling of Surface Properties” was held in Toulouse, France. The workshop theme covered emissivity modeling, remote sensing algorithms for surface radiometric and geophysical parameters, as well as the impact of assimilating surface-sensitive measurements into NWP systems. Advances in the assimilation of surface-sensitive channels over land were reported by many NWP centers including the Canadian Meteorological Centre, ECMWF, JCSDA, Météo- France, and the Met Office. In particular, it was found that considerable effort is being devoted to accommodate surface emissivity and temperature variability into models, with an increasing interest in land surfaces. A good knowledge of the land surface emissivity was found to be very helpful to extract useful information from satellite data on the atmospheric lower boundary layer and to improve short- to medium-range forecasts. The workshop was a great opportunity to advocate the involvement of the Land Surface Modeling community to help prepare for using SMOS data within land data assimilation Digital Object Identifier 10.1109/TGRS.2011.2127270 systems. Some interesting studies were presented for retrieving surface emissivity at infrared and microwave wavelengths for a variety of surface conditions such as snow, ice, soil, or canopy. For this special issue, 14 manuscripts were accepted (two did not arrive in time for this issue’s schedule) for publication after a rigorous peer-review process. We take this opportunity to sincerely thank all reviewers who gave their time and effort for high-quality assessment of the manuscripts.We also wish to express our sincere thanks to Prof. Chris Ruf (Editor-in-Chief) and to Alison Larkin for their support and valuable assistance during the preparation of this special issue. The two papers by Wigneron et al. and Calvet et al. that follow this introduction discuss some issues about soil moisture using data from a field campaign. The next two articles by Grohmann et al. and Baek et al. deal with the subject of surface classification and surface roughness using remote sensing data. Afterward, the article by Harlow et al. and discusses the issue of sea ice emissivity modeling and temperature estimation and concludes with an improved oceanic microwave emissivity model. The remaining study examines the sourcess of error associated with land surface emissivity and assimilation experiments and shows improved use of surface-sensitive observations over land in NWP models. Finally, the last four articles are dedicated to the study of land surface emissivity and temperature at infrared wavelengths, which will be important for future progress with the use of infrared sounding observations in NWP models.

Last Modified: 11/28/2014
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