Open AccessLand surface reflectance retrieval from optical hyperspectral data collected with an unmanned aerial vehicle platform
Author(s) -
Yaokai Liu,
Chuanrong Li,
Lingling Ma,
Ning Wang,
Caixia Gao,
Lingli Tang
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.007174
Subject(s) - remote sensing , modtran , hyperspectral imaging , mean squared error , environmental science , radiative transfer , atmospheric correction , satellite , atmospheric radiative transfer codes , reflectivity , optics , physics , geology , mathematics , radiance , statistics , astronomy
We present a physical-based atmospheric correction algorithm for land surface reflectance retrieval based on radiative transfer model MODTRAN 5, with which the aerosol optical thickness @550 nm (AOT@550nm), columnar water vapor (CWV) could also be estimated from the hyperspectral data collected over UAV platform. Then, the method was tested on both the synthetic and field campaign-collected hyperspectral data by an UAV-VNIRIS (UAV visible/near-infrared imaging hyperspectrometer) with the spectral range covering from 400 to 1000 nm. The retrieval results were validated with theoretical values from synthetic data and truth values from field campaign measurements. The results show that the averaged MAE (mean absolute error) and RMSE (root mean squared error) of measured and retrieved surface reflectance based on estimated AOT@550nm and CWV is 0.0134 and 0.0130. Meanwhile, the averaged MAE and RMSE of measured and retrieved surface reflectance based on ground measured AOT@550nm and CWV is 0.0101 and 0.0112. The results show that our introduced method has good agreement with the method based on ground-measured AOT@550nm and CWV. These encouraging results also indicate that the introduced physical-based atmospheric approach provides a quick and reliable way to acquire the land surface reflectance from UAV platform-observed hyperspectral data for further quantitative remote sensing applications.