Advances in topographic correction methods for optical remote sensing imageries

Abstract

Steep terrain produces serious topographic effects on remote sensing satellite imageries. Serious topographic effects cause difficulty in classifying vegetation species and retrieving key essential climate variables (such as albedo, Leaf Area Index, and fraction of absorbed photo synthetically active radiation). These effects also bring complexity in distinguishing the unhealthy change in land covers over rugged terrains. Topographic correction is necessary for remote sensing applications over mountainous areas. Researchers have attempted to remove or at least reduce topographic effects in remote sensing imageries by using various standard methodological algorithms. The background and the topographic correction model have been reviewed in former studies. However, several effective topographic correction models, which have high quality and have been newly developed during these years, have not been mentioned and recommended comprehensively. Therefore, topographic correction models and evaluation methods for optical remote sensing imageries from the presented research chain were reviewed comprehensively in this paper. The aim was to determine the potential effective solutions for topographic effect correction over rugged terrains. This study is important for quantitative remote sensing applications in mountainous areas. Topographic correction models have been explored for more than 30 years (Fig. 1). These models can be divided into three categories, namely, the regression model, the Lambertian-based model, and the non-Lambertian-based model. The regression model generally has obvious advantages of simplified formulation and easy operation. However, this model has the shortage of lack of physical meaning for empirical parameters. The Lambertian-based model has rigorous mathematical formulas, which have clear physical meaning for parameters and are easy to operate for reducing complex topographic effects. Meanwhile, the Lambertian models are built on the basis of several assumptions and have the obvious shortage of ignoring the non-Lambertian surface reflectance. This negligence may result in overcorrection, especially over shady surfaces. The non-Lambertian model can improve the performance of the Lambertian-based model, especially over heterogeneous land surfaces. On the basis of this study, we summarized the problems in existing topographic correction model and provided several possible suggestions for the development of a topographic correction model in optical remote sensing. These suggestions can provide important guidance to the research on topographic correction and its practical application.