
|
21 Aug 2020
DRONE DATA ATMOSPHERIC CORRECTION CONCEPT FOR MULTI- AND
HYPERSPECTRAL IMAGERY – THE DROACOR MODEL
D. Schläpfer, C. Popp, and R. Richter
Viewed
Total article views: 2,414 (including HTML, PDF, and XML)
| HTML |
PDF |
XML |
Total |
BibTeX |
EndNote |
| 1,380 |
975 |
59 |
2,414 |
141 |
214 |
- HTML: 1,380
- PDF: 975
- XML: 59
- Total: 2,414
- BibTeX: 141
- EndNote: 214
Views and downloads (calculated since 21 Aug 2020)
Cumulative views and downloads
(calculated since 21 Aug 2020)
Viewed (geographical distribution)
Total article views: 2,305 (including HTML, PDF, and XML)
Thereof 2,304 with geography defined
and 1 with unknown origin.
|
| Total: |
0 |
| HTML: |
0 |
| PDF: |
0 |
| XML: |
0 |
Cited
16 citations as recorded by crossref.
-
Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data
J. Estévez et al.
https://doi.org/10.1016/j.rse.2022.112958
-
Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study
J. Jia et al.
https://doi.org/10.1109/TGRS.2021.3096999
-
Radiometric calibration of a large-array commodity CMOS multispectral camera for UAV-borne remote sensing
X. Zhou et al.
https://doi.org/10.1016/j.jag.2022.102968
-
Hyperspectral imaging systems for corrosion detection from remotely operated vehicles
D. Thomas & M. Gündel
https://doi.org/10.1002/cepa.2132
-
Comparison of deep and shallow one-class classifiers for detecting invasive Prosopis trees in Kenya from airborne hyperspectral data
I. Vuorinne et al.
https://doi.org/10.1016/j.ecolind.2025.113465
-
How UAVs Bridge the Sea–Air–Space Gaps in Ocean Observing: Technological chains, collaborative frameworks, and system implementation
G. Zhou et al.
https://doi.org/10.1109/MGRS.2026.3685716
-
Species-specific machine learning models for UAV-based forest health monitoring: Revealing the importance of the BNDVI
S. Ecke et al.
https://doi.org/10.1016/j.jag.2024.104257
-
Forage Height and Above-Ground Biomass Estimation by Comparing UAV-Based Multispectral and RGB Imagery
H. Wang et al.
https://doi.org/10.3390/s24175794
-
High-throughput phenotyping to detect anthocyanins, chlorophylls, and carotenoids in red lettuce germplasm
A. Clemente et al.
https://doi.org/10.1016/j.jag.2021.102533
-
Machine Learning and Unmanned Aerial Vehicles in Water Quality Monitoring
B. Acharya & M. Bhandari
https://doi.org/10.1016/j.horiz.2022.100019
-
Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding
A. Herr et al.
https://doi.org/10.1002/csc2.21028
-
Assessing grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV
E. Laroche-Pinel et al.
https://doi.org/10.1007/s11119-024-10170-9
-
Calibration and Validation from Ground to Airborne and Satellite Level: Joint Application of Time-Synchronous Field Spectroscopy, Drone, Aircraft and Sentinel-2 Imaging
P. Naethe et al.
https://doi.org/10.1007/s41064-022-00231-x
-
Evaluation of the DROACOR Model for Atmospheric Correction of Drone Hyperspectral Data
Y. Son
https://doi.org/10.7780/kjrs.2024.40.6.1.31
-
UAV-based deep learning for biodiversity monitoring: Advances, applications, and future directions
S. Wang et al.
https://doi.org/10.1016/j.ecoinf.2026.103710
-
Limitations of a Multispectral UAV Sensor for Satellite Validation and Mapping Complex Vegetation
B. Cottrell et al.
https://doi.org/10.3390/rs16132463
Latest update: 20 Aug 2026