Analysis of the Directional Characteristics of the Clumping Index (CI) Based on RAMI-V Canopy Scenes

Author:

Xie Jinke1,Xie Donghui1ORCID,Zhou Kun1,Yan Guangjian1,Mu Xihan1

Affiliation:

1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

Abstract

The vegetation canopy clumping index (CI) is an important indicator for understanding radiative transport processes, radiation interception, and the photosynthesis of vegetation canopies. However, most studies consider CI only in the nadir or specific direction. In this study, we analyze the directional characteristics of the CI based on RAMI-V (radiation transfer model intercomparison) activity, which represents most typical canopies. The directional gap fraction and CI of these scenes are accurately calculated based on the LESS (large-scale remote sensing data and image simulation framework) model. According to our results, the directional characteristics of the CI are affected by many factors, such as vegetation type, season, and canopy structure. Generally, the CI of a coniferous forest varies little with zenith angle, while the CI of a broad-leaf forest demonstrates the different trend. In winter, the CI is smaller than that in summer, and the variation in the CI at the zenith angle is less. The row structure scenes exhibit different directional characteristics along and perpendicular to the row direction, and their CIs tend to increase with zenith angle. To accurately model the directional CI, we propose a modified Gompertz function model. Compared with other directional CI models, this model has the advantages of high precision and strong applicability ( R 2  = 0.975). By studying the directional characteristics of CI, we can enhance the usability of radiative transfer modeling and the accuracy of canopy biophysical parameter retrieval for vegetation with different structures.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

American Association for the Advancement of Science (AAAS)

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