Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes

To improve the simulation accuracy of vegetation canopy reflectance in optical bands, the Radiosity Applicable to Porous IndiviDual objects (RAPID) model has been upgraded to better deal with branches in the latest RAPID4. Previous versions of RAPID (RAPID1 and RAPID3) neglected branches in porous o...

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Main Authors: Huaguo Huang, Jianbo Qi, Linyuan Li
Format: Article
Language:English
Published: American Association for the Advancement of Science (AAAS) 2023-01-01
Series:Journal of Remote Sensing
Online Access:https://spj.science.org/doi/10.34133/remotesensing.0039
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author Huaguo Huang
Jianbo Qi
Linyuan Li
author_facet Huaguo Huang
Jianbo Qi
Linyuan Li
author_sort Huaguo Huang
collection DOAJ
description To improve the simulation accuracy of vegetation canopy reflectance in optical bands, the Radiosity Applicable to Porous IndiviDual objects (RAPID) model has been upgraded to better deal with branches in the latest RAPID4. Previous versions of RAPID (RAPID1 and RAPID3) neglected branches in porous objects in optical bands, while RAPID2 emphasized them in microwave bands. This inconsistency needed to be addressed to establish a unified radiosity-based simulation framework. By incorporating branches in RAPID4, we have improved several aspects of the model, including the random dynamic projection process, the equivalent reflectance or transmittance, the single scattering estimation, the multiple scattering solution, and the bidirectional reflectance factor (BRF) calculation. Three-dimensional trees from the fifth RAdiation transfer Model Intercomparison (RAMI-V) have been used to test the contribution of branches on BRF. Comparisons with a ray-tracing-based LESS model (the LargE-Scale remote sensing data and image Simulation framework) on RAMI-V scenes show a general agreement on BRF (R2 ≥ 0.96 and root mean square error ranging from 0.014 to 0.054). The major biases occur in a realistic scene (i.e., HET51_WWO_TLS) created from terrestrial laser scanning data. Sensitivity analysis has been conducted to show the branch contribution on BRF in optical domain. Without considering dense branches, the BRF error can exceed 0.1.
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spelling doaj.art-415c831dc3564de784cea51087d96c3a2023-04-28T16:06:44ZengAmerican Association for the Advancement of Science (AAAS)Journal of Remote Sensing2694-15892023-01-01310.34133/remotesensing.0039Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI ScenesHuaguo Huang0Jianbo Qi1Linyuan Li2State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, People’s Republic of China.State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, People’s Republic of China.State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, People’s Republic of China.To improve the simulation accuracy of vegetation canopy reflectance in optical bands, the Radiosity Applicable to Porous IndiviDual objects (RAPID) model has been upgraded to better deal with branches in the latest RAPID4. Previous versions of RAPID (RAPID1 and RAPID3) neglected branches in porous objects in optical bands, while RAPID2 emphasized them in microwave bands. This inconsistency needed to be addressed to establish a unified radiosity-based simulation framework. By incorporating branches in RAPID4, we have improved several aspects of the model, including the random dynamic projection process, the equivalent reflectance or transmittance, the single scattering estimation, the multiple scattering solution, and the bidirectional reflectance factor (BRF) calculation. Three-dimensional trees from the fifth RAdiation transfer Model Intercomparison (RAMI-V) have been used to test the contribution of branches on BRF. Comparisons with a ray-tracing-based LESS model (the LargE-Scale remote sensing data and image Simulation framework) on RAMI-V scenes show a general agreement on BRF (R2 ≥ 0.96 and root mean square error ranging from 0.014 to 0.054). The major biases occur in a realistic scene (i.e., HET51_WWO_TLS) created from terrestrial laser scanning data. Sensitivity analysis has been conducted to show the branch contribution on BRF in optical domain. Without considering dense branches, the BRF error can exceed 0.1.https://spj.science.org/doi/10.34133/remotesensing.0039
spellingShingle Huaguo Huang
Jianbo Qi
Linyuan Li
Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes
Journal of Remote Sensing
title Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes
title_full Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes
title_fullStr Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes
title_full_unstemmed Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes
title_short Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes
title_sort enhanced branch simulation to improve rapid in optical region using rami scenes
url https://spj.science.org/doi/10.34133/remotesensing.0039
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