A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification

In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.

A Modified Becker's Split-Window Approach for Retrieving Land Surface Temperature from AVHRR and VIRR

In order to provide a long time-series,high spatial resolution,and high accuracy dataset of land surface temperature（LST） for climatic change research,a modified Becker and Li＇s split-window approach is proposed in this paper to retrieve LST from the measurements of Advanced Very High Resolution Radiometer（AVHRR） onboard National Oceanic and Atmospheric Administration（NOAA）-7 to-18 and the Visible and InfraRed Radiometer（VIRR） onboard FY-3A.For this purpose,the Moderate Resolution Transmittance Model（MODTRAN） 4.1 was first employed to compute the spectral radiance at the top of atmosphere（TOA） under a variety of surface and atmosphere conditions.Then,a temperature dataset consists of boundary temperature T s（which is one of the input parameters to MODTRAN）,and channels 4 and 5 brightness temperatures（T 4 and T 5） were constructed.Note that channels 4 and 5 brightness temperatures were simulated from the MODTRAN output spectral radiance by convolving them with the spectral response functions（SRFs） of channels 4 and 5 of AVHRRs and VIRR.The coefficients of modified Becker and Li＇s split-window approach for various AVHRRs and VIRR were subsequently regressed based on this temperature dataset using the least square method.As an example of validation,one AVHRR satellite image over Beijing acquired at 0312 UTC 27 April 2008 by AVHRR onboard NOAA-17 was selected to retrieve the LST image using the modified Becker and Li＇s approach.The comparison between this LST image and that from the MODIS level-2 LST product provided by the University of Tokyo in Japan indicates that the correlation coefficient is 0.88,the bias is 0.6 K,and the root mean square deviation（RMSD） is 2.1 K.Furthermore,about 70% and 37% pixels in the LST difference image,which is the result of retrieved LST image from AVHRR minus the corresponding MODIS LST image,have the values within ± 2 and ± 1 K,respectively.