Research progress on parameter sensitivity analysis in ecological and hydrological models of remote sensing

Abstract

Parameter Sensitivity Analysis (SA) is an important research method for Uncertainty Analysis(UA), key parameters identification and parameters optimization in remote sensing, ecological and hydrological models. In this paper, the sensitivity analysis of ecological and hydrological research based on remote sensing is analyzed. The sensitivity analysis methods commonly used in remote sensing ecological hydrology are reviewed, and the advantages and applicable conditions of each SA method are summarized. Parameter sensitivity analysis methods are generally divided into Local Sensitivity Analysis (LSA) and Global Sensitivity Analysis (GSA), also can be divided into variance based, statistics based and graphic based methods from mathematical mechanism. Sobol 'and EFAST are the most reliable and stable global sensitivity methods among the current sensitivity algorithms, which are most suitable for most remote sensing inversion and model. There are many methods for parameter sensitivity analysis, so it is very important to select the appropriate method. The initial setting of sensitivity analysis will also affect the results of the analysis. The sensitivity of parameters varies at different scales, The parameter of remote sensing fluorescence model is also one of the key scientific issues. Parametric sensitivity analysis methods have also promoted the development and use of microwave scattering/radiation models. Parameter sensitivity In the process of remote sensing inversion, the order of importance of parameters can be judged according to the sensitivity order, thus providing prior knowledge for multi-stage inversion. In conclusion, sensitivity analysis can effectively improve the simulation accuracy of hydrological, ecological and growth models driven by remote sensing data, and effectively analyze the uncertainties caused by parameters at different scales. Parameter sensitivity analysis can be judged according to the order of sensitivity so as to provide a priori knowledge for multi-stage inversion in the process of remote sensing inversion. The difference of parameter sensitivity analysis in different scales, different bands and different observation angles, as well as the parameter uncertainty, must be paid attention to and analyzed. The four platforms for sensitivity analysis and uncertainty analysis also are introduced in order to make it more convenient for remote sensing scientists to use parameter sensitivity analysis method. Parameter sensitivity analysis as the prior knowledge of the model promotes the development of uncertainty analysis and parameter optimization. In future studies, Under the framework of Uncertainty and Sensitivity Matrix (USM), it is necessary to pay more attention to the research of multi-stage remote sensing inversion by combining global SA, scale effect of parameter sensitivity index and spatio-temporal heterogeneity of parameter Sensitivity. Meanwhile, the model construction and parameter setting are supported by prior knowledge of parameter sensitivity analysis. Parameter sensitivity analysis should be combined with parameter optimization, data assimilation, spatial analysis and multi-stage inversion to optimize remote sensing inversion and reduce uncertainty. The improvement of computational efficiency and stability of parameter sensitivity analysis is the trend of future research, which requires multi-threaded synchronization, grouping strategy and cloud computing platform.