Estimation of snow depth and snow water equivalent distribution using airborne microwave radiometry in the Binggou Watershed, the upper reaches of the Heihe River basin

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Abstract

We estimated the spatial distribution of snow depth/snow water equivalent (SD/SWE) in a mountainous watershed (Binggou, which is in the upper reaches of the Heihe River basin) by an airborne microwave radiometry observational experiment. Two microwave radiometers measuring at K band (18.7 GHz) and Ka band (36 GHz) were used to estimate the volume scatter from snowpacks and infer SD and SWE. Simultaneously, the snow physical properties (such as snow depth, density, grain size and temperature) over four sites were measured, and a simple multi-layer sample scheme was adopted to obtain the stratigraphic information. The microwave emission model of layered snowpacks (MEMLS) was used to simulate the brightness temperatures of snow cover for each measurement point. By comparing TB data that were simulated by MEMLS and observed by radiometers on the aircraft over the four sites, we obtained the retrieval algorithms of SD and SWE based on brightness temperature differences (TBD) at the K- and Ka-bands that are suitable to the local snow properties. The validation shows that the mean absolute and relative errors of SD estimates are approximately 3.5 cm and 14.8%, respectively. SWE from airborne microwave radiometers show that blowing snow and sun radiation are two main factors that determine the spatial distribution of SWE in Binggou Watershed.

The local angle of incidence of the microwave radiometer observation can be influenced by mountainous topography, and a sensitivity analysis suggests that changes in the local angle of incidence (e.g., the nominal angle of incidence) will not significantly influence the estimation of SD/SWE. The snow's stratigraphic condition is not an important factor for estimating SD/SWE in this study because the snow was not very deep in the Binggou Watershed. However, the field sampling scheme should be given more attention to obtain the spatial variations of snow properties and to support pixel-by-pixel validation in next field campaign.

Highlights

► We estimate SWE by airborne microwave radiometers in a cold region watershed. ► SWE was retrieved by differences of brightness temperature (TB) at K- and Ka-bands. ► Blowing snow and sun radiation are two factors for the distribution of snow. ► Change of angles of incidence from terrain is important to research of TB. ► Change of angles of incidence from terrain is not important to retrieval of snow.

Introduction

Snow cover plays an important role in the water and energy cycles at both a global and a regional scale. An accurate estimation of the snow water equivalent (SWE) can improve the understanding of the climate, land surface processes and, in particular, the cold regional hydrological model.

Spaceborne passive microwave (PM) radiometers have provided an opportunity to estimate SWE at both a regional and global scale (Chang et al., 1987, Foster et al., 1997, Che et al., 2003, Kelly et al., 2003). The core of these SWE algorithms is generally the brightness temperature differences (TBD) on two frequencies (generally using K- and Ka-bands), which are based on the volume scattering variations of snowpacks between different wavelengths, and the rule that a larger snow mass results in a larger volume scattering.

However, the spatial resolution from PM satellites is too coarse to introduce or assimilate the SWE into the hydrological process model at the basin scale (Li and Cheng, 2008). The observational data for snow in mountainous stations cannot represent the snow conditions within the PM footprint due to the spatial heterogeneity of the snow distribution. For example, snow data located in mountainous terrain have been removed for the development of the global AMSR-E SWE algorithm (Kelly et al., 2003). The relationship between TBD and in situ SWE in high elevation regions (>2000 m) is quite different from the low elevation regions (Che et al., 2003). It is not surprising to find large errors in the global SWE product algorithm in mountainous regions (Andreadis and Lettenmaier, 2006). Therefore, the estimation of SWE in mountain regions requires further improvements because of (1) the coarse spatial resolution of PM on the satellite and (2) the complexity of the topography. Airborne microwave systems can provide data at higher resolutions, and thus, they were considered as a bridge between the ground and spaceborne observations.

Two airborne passive microwave remote sensing experiments were implemented on the BOREAS campaign to estimate the SWE in February of 1994 and 2003, respectively (Chang et al., 1997, Parde et al., 2007). The results from the experiment in 1994 revealed that the boreal forest was one of the most important factors in estimating SWE (Chang et al., 1997), and thus, a forest modification algorithm was proposed (Foster et al., 1997). Spatial consistency between ground snow measurements and airborne pixel locations was difficult, so the mean measurement on each airborne segment was used to validate the results (Parde et al., 2007). The SWE retrieval algorithm was based on a snow emission model (HUT) that was initialized using standard meteorological measurements (snow depth and air temperature), and this algorithm can estimate the SWE and snow grain size simultaneously.

The Cold Land Processes Experiment (CLPX) in 2003 and 2004 used the airborne polarimetric scanning radiometer to obtain multi-frequency brightness temperature data of different snow conditions (dry, wet, and refrozen). A series of empirical SWE retrieval algorithms, which were similar to the Chang algorithm but with more frequencies and polarizations, were analyzed according to the relationships between emissivity and snow properties (Stankov et al., 2008). Their results also suggested that a new algorithm should be developed to account for local conditions, such as snow properties and land cover.

Recently, two experiments between Canada and Finland were carried out to compare airborne microwave brightness temperature and snow properties in these two regions in 2005 and 2006, respectively (Lemmetyinen et al., 2009). The comparison showed that larger snow grain sizes lead to a lower brightness temperature, which indicates that grain size is an important factor in the volume scatter of snowpack. These studies suggested that future studies should measure the snow stratigraphic condition in the field.

In this study, to obtain the SWE at a high spatial resolution, the airborne microwave radiometers were used to observe the TB at a K-band (18.7 GHz) and Ka-band (36.0 GHz). Furthermore, we adopted a nadir observation to reduce the influences of topography, particularly the local incidence angle changes, on TB. A simple snow sampling scheme was implemented to obtain the snow's stratigraphic condition by simultaneous ground observations. The microwave emission model of layered snowpacks (MEMLS) was used to simulate the relationship between TB and SD/SWE. This study is one of many airborne remote sensing experiments within the watershed allied telemetry experimental research (WATER) (Li et al., 2009). The aim of this airborne mission was to retrieve SD/SWE at a watershed scale in cold and mountain regions.

Section snippets

Study area

The Binggou Watershed, with an area of 30.48 km2, is located in the boundary regions of the Qinghai–Tibet plateau (see Fig. 1). In hydrology, this watershed belongs to the upstream of the Heihe River basin and its elevation varies from 3450 m to 4400 m (Li et al., 2009). Melted snow water is one of the most important water resources in the Heihe River basin, which supplies agriculture in the middle reaches and the ecosystem in the lower reaches.

Airborne microwave radiometry observations

In the Binggou Watershed, the airborne microwave

TB comparison of simulation and observation

The brightness temperature difference (TBD) at 18/19 and 36 GHz has been successfully used to derive volume scattering signatures of snowpacks (Chang et al., 1987, Foster et al., 1997, Kelly et al., 2003, Che et al., 2008), which is the kernel of retrieval algorithms of snow depth or SWE. In this study, we simulated the TB at 18.7 and 36 GHz by MEMLS and analyzed the relationships between TBD and SD/SWE.

The AIEM was used to calculate the reflectivity (s0) between the snow and ground. First, we

MEMLS model and retrieval algorithm

The MEMLS model was developed according to many field measurements that determines several basic relationships between snow physical variables and microwave properties, such as the permittivity (Mätzler, 1996), scattering and absorption coefficients (Wiesmann et al., 1998). Several snow-pit data with ground-based microwave radiometer measurements have proved the MEMLS can simulate brightness temperature of snow (Wiesmann and Mätzler, 1999, Mätzler and Wiesmann, 1999, Tedesco and Kim, 2006,

Acknowledgments

We wish to thank two anonymous reviewers for their helpful comments and suggestions which significantly improved this paper. The work was funded by the Chinese Academy of Sciences Action Plan for West Development Project ’Watershed Allied Telemetry Experimental Research (WATER)’ (KZCX2-XB2–09), the China State Key Basic Research Project (2007CB714400), and the National Natural Science Foundation of China (40971188&40701030). Authors thank every participant in cold region experiment of WATER.

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