Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments

Abstract

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites.

Remote sensing of earth’s energy budget: synthesis and review

“…One challenging issue is that most global satellite products have not taken into account the effects of surface topography yet. It has been demonstrated that surface topography can significantly affect the estimation of incident short wave radiation (Wang, Yan, et al 2018;Wu et al 2018) and surface albedo (Wen et al 2018). Another challenging issue is that existing global satellite products have pixel size much larger than the footprints of ground-based flux measurements, making it difficult to assess the accuracy of satellite radiation products over heterogeneous landscapes (Cescatti et al 2012).…”

The Depths of Cast Shadow

“…With these tests, the authors examine the hypothesis that a normalised spectral signature of shadow colour is invariant to brightness effects associated with sun-object-sensor geometry and can be used to quantify shadow depth. To overcome the complexities of geometry and the resulting variance would be valuable for high-resolution image analysis methods because it removes the need for commensurately scaled data such as terrain and BRDF (bi-direction reflectance distribution function) models [16,17]. A simplified and less scene-dependent approach to quantifying shadow depth and extent has vast potential to benefit many remote sensing applications because it would provide the data for de-shadowing that achieves improved classification and analysis of high spatial resolution imagery.…”

Diffuse Skylight as a Surrogate for Shadow Detection in High-Resolution Imagery Acquired Under Clear Sky Conditions

“…Quantifying the illumination in image pixels provides the metric to normalise pixels to full sun and skylight and that is advantageous for shadow detection [6]. Characterizing illumination is complex due to variations in atmospheric conditions, sun-object-sensor geometry, scene topography, and surface material properties [7][8][9][10]. All shadow detection methods must consider illumination so further examination of these effects follows.…”

“…Surface material properties are modelled using Bidirectional Reflectance Distribution (BRD) functions [7][8][9][10]. A BRDF is material specific, so methods to reduce illumination effects using BRDF require a priori reflectance characteristics of all scene materials.…”