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Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest

Takumi Fujiwara · Wataru Takeuchi

Remote Sensing · 21 Aug 2022 · 10.3390/rs14164088

Abstract

Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.

Code and data availability

The paper's CSRS reflectance/shadow simulation code is explicitly stated to be publicly available on the authors' GitHub repository. The ECOSTRESS Spectral Library is a generic external spectral database, not a paper-specific asset.

Codepublic

ng—review and editing, W.T.; visualization, T.F.; supervision, W.T.; project administration, W.T.; funding acquisition, W.T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The simulation code of CSRS is available from https://github.com/Takumi-Fuji6936/CSRS.git (accessed on 29 June 2022). Conflicts of Interest: The authors declare no conflict of interest. References 1. FAO. Assessment, Global Forest Resources 2020. Available online: https://www.fao.org/3/CA8753EN/CA8753EN.pdf (accessed on 10 November 2021). 2. Beer, C.; Reichstein, M.; Tomelleri, E.; Ciais, P.; Jung, M.; Carvalhais

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