Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Abstract

Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge.A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath > 1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn-dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger "hole at the pole". Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

“…The snow depth over sea ice was estimated with an RMSE of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 GHz on vertical channels. Braakmann-Folgmann and Donlon [28] proposed an artificial neural network using both AMSR2 and Soil Moisture and Ocean Salinity (SMOS) data as input to retrieve snow depths in the Arctic in 2019. Trained using seven years of OIB snow depth data, the algorithm is suitable for FYI and MYI, and the bias and the RMSE relative to OIB snow depth data were 1.1 and 4 cm.…”

Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

“…The topographical information such as snow cover area is one of the most important variables for the application of hydrological model [18][19][20], and can dominate local and regional climate, and hydrology. Snow and glacier strongly influence the regional radiation budget with its high albedo and acts as an insulation, controlling snow accumulation and melt [21]. In this regard, accurate representation of snow cover at micro-scale of spatial variation is required to provide accurate and timely information on water availability and its management.…”

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

“…We plan to apply our ensemble-based deep neural network to the AMSR-E and AMSR2 to examine to what extent the snow depth retrieval can be improved by considering lower frequency brightness temperatures. More recently, Braakmann-Folgmann and Donlon [53] attempted to retrieve snow depth over sea ice using a multi-source of satellite microwave radiometer measurements with a neural network. They showed that the inclusion of the L-band of SMOS as the additional input could lead to better estimation of snow depth.…”

“…Each hidden layer contains a number of neurons that allow the network to learn more complicated, non-linear relationships between multi-frequencies (channels) microwave emissions from within a snow layer and snow depth, i.e., the neurons in the first hidden layer make simple decisions, and then the neurons in the second hidden layer make more complicated decisions than those in the first hidden layer. More recently, Braakmann-Folgmann and Donlon [53] attempted to retrieve snow depth over sea ice a deep neural network using a gradient ratio of selected frequencies of brightness temperatures as the input and showed promising results. However, a specific network shows certain sensitive dependence on initially assigned weight and bias to the neurons.…”