Preliminary study on the orbit cross-calibration of CMODIS by SeaWiFS

MostareasofChinaSeasbelongtocase 2water ,andthealgorithmbasedontheratioofgreentoblueisnotsuitabletothoseareas[1] becausetwoormoresubstanceswithdifferentopticalpropertiesarepre sentedwhichdonotco varywithchlorophyllacon centration .Thesemightbewaterwithexceptionalplanktonblooms (suchasredtides) ,discoloredbyretainedandorganicsuspendedmaterialsandthedis solvedorganismmaterial (DOM ) ,suchasacids .Itis,therefore ,essentialtodevelopmorechannelswithmoresensitivesensorsforChinesecoastalwaterdetec …     

Preliminary study on the orbit cross-calibration of CMODIS by Sea WiFS

China launched its third spaceship SZ-3 in March, 2002 on which the main remote sensor is the Chinese moderate imaging spectra radiometer (CMODIS). In this paper the properties of CMODIS are firstly introduced briefly. Then, the theory and algorithm of cross-calibration for CMODIS ocean color channels by the sea-viewing wide field-of-view sensor (SeaWiFS) data are discussed in detail. The total radiance (TOA) of four quasi-synchronized crossing ocean areas simulated by SeaWiFS and measured by CMODIS are compared and the calibration coefficients are derived from the relationship between them. Finally, the in-situ water leaving radiance data are used to validate the calibration results. The results show that the cross-calibration method could provide reasonable precision for ocean color measurement.     

Integrating genetic algorithm method with neural network for land use classification using SZ- 3 CMODIS data

This paper presents a methodology on land use mapping using CMODIS (Chinese Moderate Resolution Imaging Spectroradiometer) data on-board SZ-3 (Shenzhou 3) spacecraft. The integrated method is composed of genetic algorithm (GA) for feature extraction and neural network classifier for land use classification. In the data preprocessing, a moment matching method was adopted to remove the stripes in the images. Then by using the reproduction, crossover and mutation operators of GA based on the mechanism of “natural selection”, and with Jeffries-Matusita distance as its discriminate rule and the training samples, the optimal band combination for land use classification was obtained. To generate a land use map, the three layers back propagation neural network classifier is used for training the samples and classification. Compared with the Maximum Likelihood classification algorithm, the results show that the accuracy of land use classification is obviously improved by using our proposed method, the selected band number in the classification process is reduced, and the computational performance for training and classification is improved. The result also shows that the CMODIS data can be effectively used for land use/land cover classification and change monitoring at regional and global scale.