Rotatable retarder fixed polarizer (RRFP) Stokes polarimeters, which employ uniformly spaced angles over 180° or 360°, are most commonly used to detect the state of polarization (SOP) of an electromagnetic (EM) wave. The misalignment error of the retarder is one of the major error sources. We suppose that the misalignment errors of the retarder obey a uniform normal distribution and are independent of each other. Then, we derive analytically the covariance matrices of the measurement errors. Based on the covariance matrices derived, we can conclude that 1) the measurement errors are independent of the incident intensity s₀, but seriously depend on the Stokes parameters (s₁, s₂, s₃) and the retardance of the retarder δ; 2) for any mean incident SOP, the optimal initial angle and retardance to minimize the measurement error both can be achieved; 3) when N = 5, 10, 12, the initial orienting angle could be used as an added degree of freedom to strengthen the immunity of RRFP Stokes polarimeters to the misalignment error. Finally, a series of simulations are performed to verify these theoretical results.

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and ${\mathrm{R}}^2$ obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.