| In recent years,with the implementation of the China High-Resolution Earth Observation System(CHEOS)and the implementation of the National long-term development plan for civil space infrastructure(2015-2025),Chinese earth observation satellites have been increasing and improving,and remote sensing data resources are continuously enriched.The field and scale of satellite remote sensing data application is expanding,industry and regional applications are flourishing,and the application level is improved rapidly.Satellite remote sensing data have played an important role in weather forecasting,disaster prevention and controlling,agriculture monitoring,resource investigation,land use monitoring,environmental monitoring,ocean monitoring,public safety,major construction projects and other aspects.China has become a satellite remote sensing power,initially with the ability to serve the national economic and social development.However,whether it is international or domestic,terrestrial application using optical satellite remote sensing data has been a problem,the bottleneck is the low level of quantitative conversion from the "data" to "information".Therefore,for the application requirements,it is needed to conduct the application performance evaluation and information extraction method development for Chinese optical satellite data,which is of great significance to promote the operational application of Chinese optical satellite data,and improve the application performance of Chinese earth observation system,and to build aerospace power.The objective of this study is evaluation of the application performance of Chinese optical satellite data.Based on the investigation and analysis of the application requirements of each government industry department,this study takes the GF-1 satellite WFV data as an example,and then analyzes and evaluates the application performance of Chinese optical satellite data by investigating the performance of the typical factors,such as land surface reflectance,vegetation index,land cover classification and leaf area index inversion.This study will provide technical support to effectively promote the Chinese optical satellite data quantitatively transforming from the "data" to "information",and serve the operational application requirements of Chinese satellite data.The detailed research contents and results are as follows:(1)There are many difficulties in scientifically,quantitatively,systematically and standardly describe the requirements of remote sensing applications in China,because there are many departments of remote sensing applications and the descriptions of the application requirements are diverse.This study statistically analyzed the application requirements of remote sensing in Chinese major industry sectors by using the methods of investigation and surveying,and accessing to literatures and policy documents.And then,this study summarizes the common application requirements of the industry sectors,including the production of thematic maps,long-term stable data source guarantee,multi-scale data requirements and a wide range of global data acquisition needs.This study further transforms the application requirements into remote sensing application parameters and remote sensing observation parameters,and then identifies the typical factors in the three fields of atmosphere,land and ocean,which can reflect the application performance of optical satellite data.Finally,the land surface spectral reflectance,vegetation index,land cover classification and leaf area index inversion are selected as four typical evaluation factors to analyze application performance of Chinese optical satellite data.(2)The accurate acquisition of the optical satellite land surface reflectance data determines the accuracy of land cover classification and land surface parameter inversion using remote sensing data,which is the basis of remote sensing data application.Vegetation index can better reflect the growth status and spatial distribution of green vegetation,which can help to enhance the interpretation of remote sensing images and has been widely used in remote sensing applications such as land cover classification and vegetation parameter inversion.Therefore,verifying the reliability of Chinese satellite land surface reflectance and vegetation index data is evaluating the application performance of Chinese satellite data.Therefore,this study assesses and analyzes GF-1 satellite WFV data through the cross-comparison of land surface reflectances and vegetation indices with Landsat-7 Enhanced Thematic Mapper plus(ETM+)data.The four vegetation indices considered here are normalized difference vegetation index(NDVI),enhanced vegetation index(EVI),ratio vegetation index(RVI)and soil adjusted vegetation index(SAVI).The coefficients of determination(R2)values between the GF-1 satellite WFV and Landsat-7 satellite ETM+ data were 0.82,0.89,0.92 and 0.80 for the blue,green,red and near-infrared band reflectances,and 0.90,0.84,0.83 and 0.91 for NDVI,EVI,RVI and SAVI,respectively.The results display a high correlation between the land surface reflectances and vegetation indices from GF-1 satellite WFV and Landsat-7 satellite ETM+ data and indicate the reliability of GF-1 satellite WFV data.Furthermore,the GF-1 WFV data are superior to Landsat-7 ETM+ data with regards to spatial and temporal resolutions,and can be more effective for obtaining detailed and timely information of Earth's surface,which can provide stable data basis for related applications.(3)Land cover affects all aspects of the Earth's system process and has important scientific research values.There are different growth characteristics between different vegetation types and different temporal variations between vegetation and other land cover types,which have the potential to improve land cover classification accuracy.However,due to the lack of suitable remote sensing data sources,the temporal features are less used in high spatial resolution land cover classification using remote sensing data.In this study,time series GF-1 satellite WFV data covering the vegetation growth period were collected,and then the temporal features reflecting the dynamic characteristics of ground-object were extracted from the time series NDVI generated from the GF-1 satellite WFV data.The temporal features included the maximum,the minimum,the mean and the standard deviation value of the time series NDVI.Then,the support vector machine classification method was use to classify the land cover types based on the spectral features and their combination with the temporal features respectively.The validation results indicated that the temporal features extracted in this study could effectively reflect the growth characteristics of different vegetation types and enhanced the separability,and finally improved the land cover classification accuracy of about 7 percentage points,reaching 92.89%,in particular,greatly improved the vegetation type identification accuracy.The results indicated that the application performances of land cover classification using GF-1 satellite WFV data was satisfactory,which could provide reliable high spatial resolution land cover data for related applications.(4)Leaf area index(LAI)is an important vegetation parameter that characterizes leaf density and canopy structure,and plays an important role in global change research,land surface process simulation and ecological environment assessment.In this study,an automatic LAI inversion algorithm for GF-1 satellite WFV data was developed based on radiative transfer model.This algorithm utilized the PROSAIL radiative transfer model to simulate the physical relationship between surface reflectance and LAI under different soil and vegetation conditions,and then form a sample data set for the algorithm development.The neural network algorithm was then used to construct the LAI estimation model using the land surface reflectance.Green,red and near-infrared bands' reflectances of GF-1 satellite WFV data were the input variables of the neural network,as well as the corresponding LAI was the output variable.The validation results using field survey LAI data indicated that the LAI estimation algorithm could achieve satisfactory results(R2 = 0.818,RMSE = 0.50),which shown the GF-1 satellite WFV data having good application performance of LAI inversion.In addition,the LAI estimation algorithm had the potential to operationally generate LAI datasets using GF-1 satellite WFV land surface reflectance data,which could provide high spatial and temporal resolution LAI data for agriculture,ecosystem and environmental management researches.The main contributions of this thesis are as follows:(1)On the basis of extensive investigation and survey,the common application requirements of the government industry departments are extracted,and then the typical factors reflecting the application performance of remote sensing data are summarized.(2)The reliability of GF-1 satellite WFV data spectral reflectance data and vegetation index data is evaluated using the cross-validation method;(3)Land cover classification method using GF-1 satellite WFV data is proposed based comprehensive using spectral features and temporal features,which effectively improves the land cover classification accuracy,in particularly the vegetation type identification accuracy,and(4)A 10-meter spatial resolution level LAI automatic inversion method is developed based on the radiative transfer model and the machine learning algorithm,which overcomes the problem of determining the model parameters in the empirical method. |
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