# Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach

## TL;DR

This paper introduces a new method to accurately convert drone-based multispectral images into land surface reflectance using a solar radiation separation approach.

## Contribution

A novel method for separating direct and scattering radiation to improve reflectance conversion accuracy in drone-based remote sensing.

## Key findings

- The proposed method achieves high accuracy in surface reflectance conversion under complex lighting conditions.
- Green grass shows the highest mean absolute error of 1.59% among selected land cover types.
- The red band has the highest overall mean absolute error of 1.01% across all land cover types.

## Abstract

Land surface reflectance is a basic physical parameter in many quantitative remote sensing models. However, the existing reflectance conversion techniques for drone-based (or UAV-based) remote sensing need further improvement and optimization due to either cumbersome operational procedures or inaccurate results. To tackle this problem, this study proposes a novel method to mathematically implement the separation of direct and scattering radiation using a self-developed multi-angle light intensity device. The verification results from practical experiments demonstrate that the proposed method has strong adaptability, as it can obtain accurate surface reflectance even under complicated conditions where both illumination intensity and component change simultaneously. Among the six selected typical land cover types (i.e., lake water, slab stone, shrub, green grass, red grass, and dry grass), green grass has the highest error among the five multispectral bands with a mean absolute error (MAE) of 1.59%. For all land cover types, the highest MAE of 1.01% is found in the red band. The above validation results indicate that the proposed land surface reflectance conversion method has considerably high accuracy. Therefore, the study results may provide valuable references for quantitative remote sensing applications of drone-based multispectral data, as well as the design of future multispectral drones.