MODIS (Moderate Resolution Imaging Spectroradiometer) is a kind of new weather satellite data. Few weather satellite images obtained are all clear sky and they are always influenced by cloud more or less. Cloud is a large obstacle to remote sensing image processing and analysis all the while. In order to extract objective information more effective, cloud should be removed from the remote sensing images, which is an essential sector in the image preprocessing. Cloud detection is the most important processing before removing cloud. Taking it into account that MODIS data includes thirty-six bands, especially the infrared channels subdivided, it has realized cloud detection in MODIS images by multi-spectral synthesis method and cloud detection index in this paper. Owing to the limitation to a certainty of the above methods, an automatic cloud detection algorithm is applied based on the spatial texture analysis and neural network in this research. At last the cloud detection results gained by different ways are testified each other and analyzed by comparison. It found that the results are consistent, which shows that the cloud-contaminate pixels are detected successfully. It not only lays a good foundation for the cloud removing, but also can improve the precision of remote sensing image recognition, classification and inverse in this study.

Cloud detection and analysis of MODIS image

A complete data set is crucial for many applications of satellite images. Therefore, this paper tries to reconstruct the
missing data sets by combining Empirical Orthogonal Functions(EOF) decomposition with Kriging methods. The
EOF-based method is an effective way of reconstructing missing data for large gappiness and can maintain the
macro-scale and middle-scale information of oceanographic variables. As for sparse data area (area without data or with
little data all the time), EOF-based method breaks down, while Kriging interpolation turns effective. Here are the main
procedures of EOF-Kriging(EOF-K) method: firstly, the data sets are processed by the EOF decomposition and the
spatial EOFs and temporal EOFs are obtained; then the temporal EOFs are analyzed with Singular Spectrum
Analysis(SSA); thirdly, the sparse data area is interpolated in the spatial EOFs by using Kriging interpolation; lastly, the
missing data is reconstructed by using the modified spatial-temporal EOFs. Furthermore, the EOF-K method has been
applied to a large data set, i.e. 151 daily Sea Surface Temperature satellite images of the East China Sea and its adjacent
areas. After reconstruction with EOF-K, comparing with original data sets, the root mean square error (RMSE) of
cross-validation is 0.58 °C, and comparing with in-situ Argo data, the RMSE is 0.68 °C. Thus, it has been proved that
EOF-K reconstruction method is robust for reconstructing satellite missing data.

Reconstruction of incomplete satellite oceanographic data sets based on EOF and Kriging methods

The presence of clouds interferes with optical remote sensing monitoring of macroalgae blooms. To solve this problem, we propose a simple method for estimating macroalgae area under clouds (Area_cloud_GT) on MODIS imagery using the principle behind the lowpass filter. The method is based on a rectangle with clouds and eight identical adjacent rectangles surrounding it that contain macroalgae. The cloud rectangle is a central ‘pixel’ (Cloud) and the eight adjacent rectangles are ‘pixels’ GT1–GT8. The core operation is to calculate the central ‘pixel’ value, i.e., the macroalgae coverage rate in the Cloud rectangle. The macroalgae area detected by semi-simultaneous fine resolution images in the same region was taken as the ‘real’ value. A comparison of the estimation results and the ‘real’ value shown that the mean relative difference between them (MRD) was 30.09% when the time interval of the images within 10 minutes. When the time interval was over 3 hours, the MRD was more than 60%. The MRD increased significantly with increasing time interval because of the constant movement of the macroalgae and the limitations of the remote sensing image. The results indicate that this simple method is effective to a certain extent. These results can provide a reference for the quantitative analysis of green tide.

/pdf/a-simple-method-for-estimating-macroalgae-area-under-clouds-1tsbp9wa.pdf

A simple method for estimating macroalgae area under clouds on MODIS imagery

In this paper, the techniques and models of cloud detection and cloud rehabilitation applied to AVHRR imagery of NOAA meteorological satellite and the basic functions of the operation system on base of Visual Basic language has been introduced.By experimentation and comparing with the fact, the high operation efficiency and precision of the cloud detection and cloud rehabilitation has been acquired. Thereby, the cloud detection and cloud rehabilitation operation system can provide the guarantee of credibled NOAA/ AVHRR imagery in the fields of remote sensing application.

Research and Establishment in Operation System of Cloud Detection and Rehabilitation Applied to NOAA Satellite

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Research and Establishment in Operation System of Cloud Detection and Rehabilitation Applied to NOAA Satellite