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Wuhan University (2011)

The Research of Cloud Parameters Indexs Model for Drought Monitoring Based on Remote Sensing Data

向大享;Xiang Da Xiang

Titre : The Research of Cloud Parameters Indexs Model for Drought Monitoring Based on Remote Sensing Data

Auteur : 向大享;Xiang Da Xiang

Grade : Doctoral Dissertation 2011

Université : Wuhan University

Résumé partiel
Drought is the major natural disaster as well as one of the most severe meteorological disasters throughout the world. Aridity seriously threatens to do harm to national economic, social development and national production and life. With the rapid development of satellite remote sensing technology, which has the characteristics of high timeliness, wide space range, objectivity, accuracy, low-cost and so on, its application to drought monitoring becomes trends and hot spots. So far, many drought monitoring models based on the visible, near infrared, far infrared and microwave remote sensing technology have been presented at home and abroad. Although each method has its advantages, some problems still exist :(1) Firstly, as the methods, using vegetation index and surface temperature, which are supported by the data of visible, infrared data, have a strong dependence on land cover type, they are poor in temporal-spatial adaptability. In addition, for drought monitoring based on microwave remote sensing, no mature algorithm is available and no drought index has been built yet. (2) Another problem is the lack of forward-based drought monitoring model using remote sensing technology. The current models are basically inversion-type ones, which have the effect of latency, because they are built by the various drought-induced land surface phenomena and natural features objects responses. (3) In addition, it is the lack of time sequence data-based drought monitoring methods in the time and frequency domain. As the methods that currently exist are basically spatial monitoring methods and mainly for drought monitoring that is based on single remote sensing images. They do not give full play to the advantages of high temporal resolution remote sensing data. (4) Finally, there is a problem of the lack of large-scale space-time adaptive method for drought monitoring based on remote sensing, as the current drought monitoring studies are conducted for small areas, various methods are deficient in regional adaptability, coefficients of regional influence need to be established. Accordingly, their value of practical application and development are limited. For the above-mentioned problems, the proposition of a large-scale space-time adaptive forward-based drought monitoring method based on time-sequence remote sensing data has an important practical significance for drought disaster prevention and mitigation, sustainable development of national economy and society. This dissertation focus on the following content and work :(1) The status of domestic and foreign remote sensing drought monitoring were comprehensively summarized, followed by the analysis for the existing problems and deficiencies in the field. (2) A detailed and exact definition of drought was presented, following on which is a summary of formation and characteristics of drought and a analysis of the impact of drought, taken the drought condition in China into consideration. (3) Existing methods of remote sensing drought monitoring were classified in detail. Then following on a systematical summary of the drought monitoring methods is a comparative analysis of the advantages and disadvantages of each method in sub-categories. (4) Based on the analysis of parameters which affect solar radiation model, after the factors of reflectivity and brightness temperature in clear sky been extracted, with the combination of remote sensing spectral information, the two functions affected by these factors were specifically analyzed and the clear sky brightness temperature and reflectivity calculation formula with normal dynamic range was constructed in consideration of the impact of each factor

Mots clés : drought monitoring; FY-2C/D/E; dynamic threshold; cloud detecting; cloud parameter; temporal and spatial modification; background field;

Présentation (CNKI)

Page publiée le 3 mai 2013, mise à jour le 16 mai 2018