Estimation of snow depth from MWRI and AMSR-E data

in forest regions of Northeast China

Tao CHE and Liyun DAICold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences

Xingming ZHENG, Xiaofeng Li, Kai ZHAONortheast Institute of Geography and Agroecology, Chinese Academy of Sciences

7th EARSeL LISSIG Workshop, February 3-6, 2014Bern, Switzerland

Remote Sensing of the Earth’s Cryosphere:Monitoring for operational applications and climate studies

Content

ObjectivesStudy area and snow measurementMethodologyResults and validationsConclusions

To develop a new algorithm of snow retrieval from passive microwave brightness temperature data in forest regions

To evaluate the use of MWRI to estimate snow depth and SWE

To quantify the forest influences on estimation of snow depth

Objectives

Study area

In Northeast China, the forest cover, which represents 40% of the total area, is the most abundant type of land cover: the farmland and grassland cover 30% and 20% of the total area, respectively, and rivers, lakes, residential areas, and other areas cover the remaining 10%.

For each snow layer: Snow thickness, density, grain size, temperature measurements in 76 snow pits

Snow depth measurements in 401 points.

(a) 4-7 January 2012

(b) 9-14 January 2012

(c) 6-9 March 2012

(d) 9-14 January 2013

Courses length: 4,800 km

Snow measurements

Sensor MWRI AMSR-E

Satellite FY3B EOS-Aqua

Time series 11/2010- present 06/2002 – 10/2011

Orbit altitude (km) 836 705

Frequency: Footprint

(GHz): (km x km)

10.65: 51 x 85

18.7: 30 x 50

23.8: 27 x 45

36.5: 18 x 30

89: 9 x 15

10.65: 29 x 51

18.7: 16 x 27

23.8: 18 x 32

36.5: 8.8 x 14.4

89:4 x 4.5

Polarization V & H V & H

Sampling interval

(km x km)10 x10 25 x 25

Incidence angle (degrees) 45 55

Data acquisition daily daily

Swath width (km) 1,400 1,445

Ascending equator crossing local time

(hh:mm)13:50 13:30

Instrument specifications of MWRI on FY3 and AMSR-E on EOS-Aqua

Content

ObjectivesStudy area and snow measurementMethodologyResults and validationsConclusions

(1 )b b snow b forestT f T f T

(1 ) (1 ) (1 ) (1 ) (1 )b forest b snow snow forest forestT t T t t e T t T

b snowT simulated by the microwave emission model of layered snowpacks (MEMLS)

土壤

雪

Microwave radiative transfer models

ω is set up to 0.05.

Snow coverSoil

( )b SAT b a a b atm b atmT T t r t T T (1)

(2)

(3)

Air temperature (Ta)

Forest cover fraction (f)

Forest temperature (Tv)

Single scattering albedo

(ω = 0.05)

Transmissivity of forest (t)t18: 0.4 - 1.0t36: 0.4 - 1.0

Snow properties from snow pits

Equation (3) MEMLS

Frequencies, incidence angle of MWRI

Equation (2)

TPW from MODISEquation (1)

Simulations of brightness temperature at 18 and 36 GHz

Observations of brightness temperature at 18 and 36 GHz

Equation (7)

Optimal t18 and t36

Determination of forest transmissivity

2 2 218 36 18 36

1 ( )3 Tb Tb Tb Tberror

error

Establishment of look-up table between snow properties and brightness temperature

Content

ObjectivesStudy area and snow measurementMethodologyResults and validationsConclusions

When the error reaches its minimum, the transmissivities are 0.656 and 0.895 at 36 and 18 GHz, respectively.

Transmissivities retrieved

Snow depth retrieved along the snow courses

Snow course

Number of samples Mean(cm) Bias (cm) RMSE(cm) Correlation

coefficient(a) 119 14.03 0.49 2.06 0.85(b) 88 13.59 0.78 3.49 0.83(c) 119 21.92 -0.72 7.02 0.77(d) 75 27.92 -1.5 7.37 0.58

Summary 401 18.64 -0.14 5.3 0.83

4-7 January 2012 9-14 January 2012

6-9 March 2012 9-14 January 2013

Snow depth retrieved at meteorological stations

Comparisons with snow products from NSIDC, ESA and WESTDC

the daily global SWE data from the NSIDC (Kelly, 2009; Tedesco and Narvekar, 2010) the GlobSnow SWE dataset from the ESA (Pulliainen, 2006; Takala et al., 2011) a snow depth product for China from the WESTDC (Che et al, 2008; Li et al., 2011)

 Mean SWE (mm)

Bias (mm) RMSE (mm)

New WESTDC ESA NSIDC New WESTDC ESA NSIDC

f>15% 6.8 0.7 13.1 -0.4 13 6.3 23.3 8.6 27.119.1 -0.5 21 -3 30.1 9.1 33.8 14.1 41.2

f<15% 2.4 0 0.5 2.1 5.7 3.9 4.6 6.8 20.79.1 -0.4 0 5.7 13.2 6.5 7.4 11.7 23.7

Snow depths were converted to SWE by multiplying the snow density that was measured in the meteorological stations every five days (pentad).

For forest regions: NEW -> ESA -> WESTDC -> NSIDCFor non-forest regions: NEW -> WESTDC -> ESA -> NSIDC

Content

ObjectivesStudy area and snow measurementMethodologyResults and validationsConclusions

The accuracy of the snow depth retrieved can be improved if the snow properties (primarily snow grain size and density) are known in advance.

The influence of forest on the retrieval of snow depth is important, and the single scattering albedo and transmissivity of forest should be considered when estimating snow depth from passive microwave remote sensing data.

The MWRI has similar specifications to AMSR-E, and the latter has not worked since 2010. Therefore, the MWRI can succeed AMSR-E for retrieval of the global SWE data before AMSR2 data are available, which can maintain temporal consistency of the daily global SWE products.

Conclusions

Thank you for your attention!