TY - GEN
T1 - A neural network method to correct bidirectional effects in water-leaving radiance
AU - Fan, Yongzhen
AU - Li, Wei
AU - Voss, Kenneth J.
AU - Gatebe, Charles K.
AU - Stamnes, Knut
N1 - Publisher Copyright:
© 2017 Author(s).
PY - 2017/2/22
Y1 - 2017/2/22
N2 - The standard method to convert the measured water-leaving radiances from the observation direction to the nadir direction developed by Morel and coworkers requires knowledge of the chlorophyll concentration (CHL). Also, the standard method was developed for open ocean water, which makes it unsuitable for turbid coastal waters. We introduce a neural network method to convert the water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method does not require any prior knowledge of the water constituents or the inherent optical properties (IOPs). This method is fast, accurate and can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both open ocean and coastal waters. In open ocean or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In turbid coastal waters, especially sediment-dominated waters, a significant improvement was obtained compared to the standard method.
AB - The standard method to convert the measured water-leaving radiances from the observation direction to the nadir direction developed by Morel and coworkers requires knowledge of the chlorophyll concentration (CHL). Also, the standard method was developed for open ocean water, which makes it unsuitable for turbid coastal waters. We introduce a neural network method to convert the water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method does not require any prior knowledge of the water constituents or the inherent optical properties (IOPs). This method is fast, accurate and can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both open ocean and coastal waters. In open ocean or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In turbid coastal waters, especially sediment-dominated waters, a significant improvement was obtained compared to the standard method.
UR - http://www.scopus.com/inward/record.url?scp=85015870249&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015870249&partnerID=8YFLogxK
U2 - 10.1063/1.4975575
DO - 10.1063/1.4975575
M3 - Conference contribution
AN - SCOPUS:85015870249
T3 - AIP Conference Proceedings
BT - Radiation Processes in the Atmosphere and Ocean, IRS 2016
A2 - Schmutz, Werner
A2 - Davies, Roger
A2 - Egli, Luca
T2 - International Radiation Symposium 2016: Radiation Processes in the Atmosphere and Ocean, IRS 2016
Y2 - 16 April 2016 through 22 April 2016
ER -