Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Juha Lemmetyinen, Chris Derksen, Helmut Rott, Giovanni Macelloni, Josh King, Martin Schneebeli, Andreas Wiesmann, Leena Leppänen, Anna Kontu, Jouni Pulliainen
2018 Remote Sensing  
Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors.
more » ... suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure. The mass of seasonal snow cover, or snow water equivalent (SWE) remains difficult to estimate on a global scale. Observational needs in terms of spatial resolution and product accuracy cannot be met by present satellite, in situ, or model-based data products at the global or regional-scale [1, 2] . Global scale EO (Earth-Observation)-based products [3,4] rely on passive microwave sensors, while watershed-scale SWE has been successfully tracked with airborne LiDAR by relating observed snow height to the snow free DEM (Digital Elevation Model), and inferring SWE from the observations by modeling snow density [5] . The cost of timely airborne LiDAR surveys, however, is prohibitive for large-scale applications beyond individual watersheds or regions, so continuing efforts are made to apply Earth Observing satellite sensors for this purpose. Applying passive microwave observations from space for snow cover detection is appealing due to the availability of a long time series of daily observations with near global coverage, extending back almost 40 years. However, estimation of SWE has proved challenging despite several decades of efforts in developing retrieval approaches [6, 7] . The main challenges hampering retrieval accuracy are related to the separation of the effect of increasing snow mass from other varying microstructural properties of the snowpack (density, layering, snow structural properties), and mitigating mixed pixel effects in the coarse scale passive microwave observations over heterogeneous landscapes. Existing active microwave sensors are unable to estimate SWE at the global scale and within user requirements because of the lack of current sensors at frequencies higher than X-band. In order to overcome these limits, a dual-band (X-and Ku) SAR mission called CoReH2O (Cold Regions Hydrology High-resolution Observatory, [8]), was proposed as a candidate for the 7th Earth Explorer mission of the European Agency (ESA), with the objective to provide SWE products at a spatial resolution of 200 m, exceeding that of current passive microwave methods. However, following phase-A CoReH2O was not selected for further development. A priori characterization of snow structural parameters determining the scattering efficiency of microwaves in snow is of primary importance in the accuracy of SWE retrieval algorithms based on radiometer measurements, as this knowledge is required to resolve the total snow mass from observed signal changes [9] [10] [11] . A key parameter defining the scattering of microwaves has conventionally been the snow grain size, an estimate of the average size of snow grains in the snowpack [12] . The snow grain size has been used to empirically define the rate of microwave extinction in snow [13] , which in turn has been applied in a radiative transfer model simulating emission from snow covered ground [14] . An effective grain size can be determined directly from passive microwave measurements, using widely available measurements from weather stations to fix the snow depth for the grain size inversion [15] ; an effective grain size determined in this fashion can be related to grain sizes observed in the field [16] . Grain size estimates for application in SWE retrievals can also be obtained by means of applying a model to estimate snow grain metamorphism during the snow season [17] . The snow structural parameter itself remains difficult to measure, and the conversion from the 3D structure to effective model grain size is not unique due to the complex nature of snow grain metamorphism [18, 19] . Theoretically-based emission and backscattering models based on the Dense Medium Radiative Transfer (DMRT) theory have assumed snow as a collection of spherical particles, introducing a stickiness parameter to emulate the sintering and clustering of snow grains [20] [21] [22] . However, it remains difficult to assign properties of snow observed in nature directly to these
doi:10.3390/rs10020170 fatcat:rl4u33mb6zahtc4rgntbmogon4