Using path analysis to study correlation and causation in remote sensing inversion

One problem in quantitative remote sensing inversion is the correlations between variables. Path analysis is a statistical technique that differentiates between correlation and causation, features multiple linear regressions, and generates path coefficients. In this paper, path analysis was applied to study the correlations and causations of two cases in remote sensing reversion. One is the retrieval of land surface temperature, and another is to explain the results of land surface moisture estimation. We found that path analysis can be used to study the direct effect and the indirect effects of a variable in remote sensing inversion, and gave a better explanation of the results of multiple linear regression analysis.