An Improved Algorithm For BRDF Modeling By Kernel Inversion Method-A Descriptive Modeling Technique

Vegetation is a diverse and complex entity that is distributed across the planet’s surface,ranging from dense forests to arid grasslands.It is a fundamental component of the Earth’s ecosystem and plays a critical role in supporting life on the planet.Vegetation provides numerous ecological services such as carbon sequestration,energy balance regulation,and nutrient cycling,and it plays a vital role in climate change mitigation and adaptation.Vegetation modeling involves the use of various remote sensing techniques to estimate the physical and biochemical properties of vegetation.These properties include canopy structure,biomass,leaf area index,chlorophyll content,and photosynthetic capacity.The development of accurate vegetation models is essential for understanding the complex interactions between vegetation,the atmosphere,and the Earth’s climate system.Existing BRDF modeling techniques are not accurate and efficient to produce low RMSE for estimating vegetation properties.There is a need for the analysis of the angular spread of BRDF scattering for backward and forward directions.Therefore,BRDF needs improvements to provide high accuracy for large datasets with varying illumination conditions by analyzing and then selecting one of the three modified Walthall,Ross-Roujean,or Ross-Li Dense models to provide the most accurate and precise results for modeling vegetation reflectance.The existing technique did not accomplish to development of a single algorithm for multiple scattering of BRF data measured under different illumination conditions and all azimuth directions.In this study,we propose a new approach for modeling vegetation properties using remote sensing data and physical models.Our proposed approach has significant implications for various fields,including agriculture,forestry,and climate change research.By accurately modeling vegetation properties by using kernel inversion method,we can better understand the carbon and energy balance of ecosystems and predict the impacts of climate change on vegetation.Moreover,the proposed approach can be used for monitoring vegetation health and growth,which is crucial for sustainable agriculture and forestry management.The proposed approach focuses on addressing the challenges associated with modeling vegetation properties,particularly the effects of specular reflectance,which is often neglected in current models.Specular reflectance refers to the reflection of light from a surface in a particular direction,and it can significantly affect the observed reflectance of vegetation.The proposed approach includes three models: the Modified Walthall Ross-Roujean Model,the Ross-Li Dense model with and without specular reflectance.The proposed approach utilizes four different models to accurately model vegetation properties.The Modified Walthall model is based on the spectral vegetation index and incorporates the vegetation cover fraction to improve its accuracy.The Ross-Roujean model is a radiative transfer model that uses canopy structure parameters to estimate vegetation properties.The Ross-Li Dense model is an extension of the Ross-Li model that accounts for the effects of leaf area density on vegetation reflectance.Finally,the Ross-Li Dense model is further modified to include the effects of specular reflectance,which is often neglected in current models.These models are combined with a specular reflectance model to account for the effects of specular reflectance on vegetation reflectance.This approach provides a more comprehensive understanding of vegetation properties and improves the accuracy of vegetation modeling.To simulate the vegetation properties,we use a radiative transfer model that simulates the transfer of electromagnetic radiation through the vegetation canopy.The simulation parameters include the leaf area index,the vegetation moisture content,and the vegetation chlorophyll content.We also include parameters related to the instrument and atmospheric conditions,such as the solar zenith angle,the viewing angle,and the atmospheric transmittance.The simulation outcomes demonstrate that the inclusion of specular reflectance in the models significantly improves their performance.In the case of the Modified Walthall Ross-Roujean Model,the inclusion of specular reflectance reduces the mean absolute error(MAE)by up to 50% for Canopy A and Canopy C.Similarly,in the case of the Ross-Li Dense model,the inclusion of specular reflectance reduces the MAE by up to 45% for Canopy A and Canopy C.These results highlight the importance of accounting for the effects of specular reflectance in vegetation modeling.This study proposes a new approach for modeling vegetation properties using remote sensing data and physical models.The approach includes the Modified Walthall Ross-Roujean Model,the Ross-Li Dense model with and without specular reflectance.The specular models are developed for achieving the reduction of the inverted parameter,k0 kernel in the BRDF regression model in order to fulfill the limitation of previous study.The simulation outcomes demonstrate that the inclusion of specular reflectance significantly improves the performance of the models.The proposed approach has the potential to advance our understanding and management of vegetation properties,particularly in the context of global environmental change.Future studies could explore the potential of this approach for monitoring vegetation properties at different scales and for different types of vegetation.