Slope correction is important to improve the accuracy of satellite radar elevation measurements by mitigating the slope-induced error (SE), especially over uneven ground surfaces. Although several slope correction methods have been proposed, guidance in the form of stepwise algorithm on how to implement these methods in processing radar altimetric data at the coding level, and the differences among these methods need to be presented and discussed systematically. In this paper, three existing types of slope correction methods—the direct method (DM), intermediate method (IM), and relocation method (RM, further divided into RM1 and RM2)—are described in detail. In addition, their main differences and features for various scientific applications are analyzed. We conduct a systematic experiment with CryoSat-2 Low Resolution Mode (LRM) data in a physically stable area around Dome Argus in East Antarctica, where in-situ measurements were available for comparison. The slope correction is implemented separately using the three methods, with the latest high-accuracy Reference Elevation Model of Antarctica (REMA) as the a-priori topography model. The bias and precision of the slope-corrected CryoSat-2 data results from the RM2 is −0.18 ± 0.86 m based on the comparison with the field Global Navigation Satellite System (GNSS) data. The results from the RM2 indicate higher precision compared to those from the RM1. According to the correlation analysis of the slope-corrected CryoSat-2 data results (RM1 and RM2), the bias enlarges and the precision becomes worse when the surface slope increases from 0 to 0.85°. After a comprehensively comparative analysis, we find that the results from the RM1 and RM2 are superior in precision (0.93 m and 0.86 m) with respect to the GNSS data. The relatively low precision (1.22 m) from the IM is due to the potential error from the a-priori digital elevation model (DEM). The DM has the lowest precision (2.66 m). Another experiment over rough topography in West Antarctica is carried out for comparison, especially between the RM1 (precision of 15.27 m) and RM2 (precision of 16.25 m). In general, the RM is recommended for the SE elimination among the three methods. Moreover, the RM2 is firstly considered over smooth topography due to the superior performance in bias and precision, while the RM1 is more suggested over the rough topography because of the slightly smaller bias and better precision. The IM relies much on the accuracy of the a-prior DEM and is not usually recommended, because of the strict requirement in the sampling time between the radar altimetry data and the a-priori DEM to avoid any surface change over time.

斜率校正通过减轻斜率引起的误差（SE），尤其是在不平坦的地面上，对于提高卫星雷达高程测量的准确性非常重要。尽管已经提出了几种斜率校正方法，但是以逐步算法的形式提供了有关如何在编码级处理雷达高度数据的方法的指导，并且需要系统地介绍和讨论这些方法之间的差异。本文详细介绍了三种现有的坡度校正方法：直接方法（DM），中间方法（IM）和重定位方法（RM，进一步分为RM1和RM2）。此外，还分析了它们在各种科学应用中的主要区别和特点。我们使用CryoSat-2低分辨率模式（LRM）数据在南极东部Dome Argus周围的物理稳定区域中进行了系统的实验，可以在其中进行现场测量以进行比较。使用三种方法分别进行坡度校正，以最新的南极高精度参考高程模型（REMA）作为先验地形模型。根据与野外全球导航卫星系统（GNSS）数据的比较，由RM2产生的经过斜率校正的CryoSat-2数据的偏差和精度为-0.18±0.86 m。与RM1相比，RM2的结果表明精度更高。根据对斜率校正的CryoSat-2数据结果（RM1和RM2）的相关性分析，当表面坡度从0增加到0.85°时，偏差会增大，精度会变差。经过全面的比较分析，我们发现RM1和RM2的结果相对于GNSS数据的精度更高（0.93 m和0.86 m）。IM相对较低的精度（1.22 m）是由于先验数字高程模型（DEM）带来的潜在误差。DM的精度最低（2.66 m）。为了进行比较，对南极洲的粗糙地形进行了另一项实验，以进行比较，特别是在RM1（精度为15.27 m）和RM2（精度为16.25 m）之间。通常，建议在三种方法中使用RM消除SE。此外，由于偏置和精度方面的优越性能，首先考虑了RM2在平滑形貌上的优势，而在偏置形貌和精确度方面，RM1则比粗糙形貌更可取。