At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping
Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/24/8224 |
| _version_ | 1797500859034107904 |
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| author | Cuizhen Wang |
| author_facet | Cuizhen Wang |
| author_sort | Cuizhen Wang |
| collection | DOAJ |
| description | Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site. |
| first_indexed | 2024-03-10T03:09:55Z |
| format | Article |
| id | doaj.art-a7c4b3eb3a3f4bab8a122ac8006842cb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:09:55Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a7c4b3eb3a3f4bab8a122ac8006842cb2023-11-23T10:28:28ZengMDPI AGSensors1424-82202021-12-012124822410.3390/s21248224At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation MappingCuizhen Wang0Department of Geography, University of South Carolina, Columba, SC 29208, USARapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site.https://www.mdpi.com/1424-8220/21/24/8224sUASRedEdge cameraradiometric correctionsurface reflectance orthoimagered edge vegetation index |
| spellingShingle | Cuizhen Wang At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping Sensors sUAS RedEdge camera radiometric correction surface reflectance orthoimage red edge vegetation index |
| title | At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping |
| title_full | At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping |
| title_fullStr | At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping |
| title_full_unstemmed | At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping |
| title_short | At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping |
| title_sort | at sensor radiometric correction of a multispectral camera rededge for suas vegetation mapping |
| topic | sUAS RedEdge camera radiometric correction surface reflectance orthoimage red edge vegetation index |
| url | https://www.mdpi.com/1424-8220/21/24/8224 |
| work_keys_str_mv | AT cuizhenwang atsensorradiometriccorrectionofamultispectralcamerarededgeforsuasvegetationmapping |