TY - GEN
T1 - Snow-covered area using machine learning techniques
AU - Gatebe, Charles
AU - Li, Wei
AU - Chen, Nan
AU - Fan, Yongzhen
AU - Poudyal, Rajesh
AU - Brucker, Ludovic
AU - Stamnes, Knut
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - In this study, we used an artificial neural network method to estimate the fractional snow cover area (fSCA), which is fast and accurate, and that can be easily adapted to different remote sensing instruments. We tested our approach using SnowEx data from NASA's Cloud Absorption Radiometer (CAR) over Grand Mesa; one of the largest flat-topped mountains in the world, which features sufficient forested stands with a range of density and height (and a variety of other forest conditions); a spread of snow depth/snow water equivalent conditions over sufficiently flat snowcovered terrain. The retrieved fractional snowcovered area from CAR compares reasonably with a Sentinel-2 image over the same location and demonstrates CAR's unique capability to improve the retrieval of snow properties using machine learning. The retrieved snow fraction parameter from our method is expected to minimize the error associated with the traditional binary snow detection scheme, and improve the retrieval quality of key parameters such as surface albedo.
AB - In this study, we used an artificial neural network method to estimate the fractional snow cover area (fSCA), which is fast and accurate, and that can be easily adapted to different remote sensing instruments. We tested our approach using SnowEx data from NASA's Cloud Absorption Radiometer (CAR) over Grand Mesa; one of the largest flat-topped mountains in the world, which features sufficient forested stands with a range of density and height (and a variety of other forest conditions); a spread of snow depth/snow water equivalent conditions over sufficiently flat snowcovered terrain. The retrieved fractional snowcovered area from CAR compares reasonably with a Sentinel-2 image over the same location and demonstrates CAR's unique capability to improve the retrieval of snow properties using machine learning. The retrieved snow fraction parameter from our method is expected to minimize the error associated with the traditional binary snow detection scheme, and improve the retrieval quality of key parameters such as surface albedo.
KW - CAR
KW - Cloud Absorption Radiometer
KW - Fractional snow cover area
KW - Multilayer Neural Network
KW - Snow grain size
UR - http://www.scopus.com/inward/record.url?scp=85064194187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064194187&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8519443
DO - 10.1109/IGARSS.2018.8519443
M3 - Conference contribution
AN - SCOPUS:85064194187
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6291
EP - 6293
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -