Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach
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 inaccu...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/8/2604 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850180873250406400 |
|---|---|
| author | Huasheng Sun Lei Guo Yuan Zhang |
| author_facet | Huasheng Sun Lei Guo Yuan Zhang |
| author_sort | Huasheng Sun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2e05cbc1eafd4ce1920e342b6f4f9cbf |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2e05cbc1eafd4ce1920e342b6f4f9cbf2025-08-20T02:18:01ZengMDPI AGSensors1424-82202025-04-01258260410.3390/s25082604Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation ApproachHuasheng Sun0Lei Guo1Yuan Zhang2Shandong Provincial Key Laboratory of Soil and Water Conservation and Environmental Protection, School of Resources and Environment, Linyi University, Linyi 276000, ChinaShandong Provincial Key Laboratory of Soil and Water Conservation and Environmental Protection, School of Resources and Environment, Linyi University, Linyi 276000, ChinaSchool of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaLand 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.https://www.mdpi.com/1424-8220/25/8/2604drone-based remote sensingmultispectral imagesland surface reflectancedirect and scattering radiationsolar radiation component separation |
| spellingShingle | Huasheng Sun Lei Guo Yuan Zhang Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach Sensors drone-based remote sensing multispectral images land surface reflectance direct and scattering radiation solar radiation component separation |
| title | Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach |
| title_full | Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach |
| title_fullStr | Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach |
| title_full_unstemmed | Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach |
| title_short | Accurate Conversion of Land Surface Reflectance for Drone-Based Multispectral Remote Sensing Images Using a Solar Radiation Component Separation Approach |
| title_sort | accurate conversion of land surface reflectance for drone based multispectral remote sensing images using a solar radiation component separation approach |
| topic | drone-based remote sensing multispectral images land surface reflectance direct and scattering radiation solar radiation component separation |
| url | https://www.mdpi.com/1424-8220/25/8/2604 |
| work_keys_str_mv | AT huashengsun accurateconversionoflandsurfacereflectancefordronebasedmultispectralremotesensingimagesusingasolarradiationcomponentseparationapproach AT leiguo accurateconversionoflandsurfacereflectancefordronebasedmultispectralremotesensingimagesusingasolarradiationcomponentseparationapproach AT yuanzhang accurateconversionoflandsurfacereflectancefordronebasedmultispectralremotesensingimagesusingasolarradiationcomponentseparationapproach |