The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 893–898, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-893-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 893–898, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-893-2020

21 Aug 2020

21 Aug 2020

# STUDY ON ARCTIC MELT POND FRACTION RETRIEVAL ALGORITHM USING MODIS DATA

J. Su1,2, P. Yu1,3, Y. Qin1,2, G. Zhang1, and M. Wang1,4 J. Su et al.
• 1Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
• 2Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
• 3Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Finland
• 4Earth Observation and Modelling, Department of Geography, Kiel University, Kiel, Germany

Keywords: Arctic, melt pond fraction, retrieval algorithm, MODIS

Abstract. During spring and summer, melt ponds appear on the sea ice surface in the Arctic and play an important role in sea ice-albedo feedback effect. The melt pond fraction (MPF) can be retrieved using multi-band linear equations, but the calculation is complicated by the ill-conditioned reflectance matrix. In this paper, we calculated the condition numbers which represent the degree of the ill-conditioned reflectance matrix in the results of the MPF from a MODIS-based unmixing algorithm. The condition number is introduced here as a criterion for the sensitivity of the solution in the system to the error in the input value. By combining 3 bands among 5 visible and near-infrared bands of MODIS data, the results show that the three-band combination with the lowest sensitivity to the error of input is B245. To improve the algorithm, we introduce pre-processing to remove open water from the four surface types and then remove one reflectance equation from the original set. The best two-band combination algorithm is B15. Compared with the discrimination results from Landsat5-TM, the RMS is 0.14. This algorithm is applied in pan-Arctic scale, the MPF results are larger than that from University of Hamburg, especially in the Pacific sector.