Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

Yichuan Ma,Sike Li,Tao He,Ainong Li

doi:10.3390/rs13204120

Abstract

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions.

Highlights

Mountains cover around a quarter of the global terrestrial land surface [1] and are sensitive to climate changes [2,3]
The number of outliers in C, SCS+C, and variable empirical coefficient algorithm (VECA) was lower than in the Teillet regression model, and the reason is that algorithms with ratio format can introduce some invalid values when the denominator is close to 0, but they are beneficial for scope limitations, while the Teillet regression model may produce more outliers slightly exceeding the original range once coefficients are not ideal
This study validated the effect of different topographic correction methods in large areas and different seasons

Summary

Introduction

Mountains cover around a quarter of the global terrestrial land surface [1] and are sensitive to climate changes [2,3]. Topographic effects caused by diverse topography and illumination conditions have complicated further studies employing remote sensing data in mountain regions, such as geophysical parameter retrieval and land cover classification [4,5,6,7]. The band ratio method, categorized as an empirical method, was the earliest and simplest one used [14]. It assumes that reflectance values caused by shadowing in different spectral bands are proportional, and the topographic effects can be removed using band ratio; it lacked physical meaning [15]

Methods

Results

Discussion

Conclusion