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Estimation of Net Radiation Flux of Antarctic Ice Sheet in East Dronning Maud Land, Antarctica, During Clear Sky Days Using Remote Sensing and Meteorological Data

  • H. S. GusainEmail author
  • Dhiraj Kumar Singh
  • V. D. Mishra
  • M. K. Arora
Original Paper
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Abstract

In the present paper, incoming shortwave radiation flux, net shortwave radiation flux, net longwave radiation flux, and net radiation flux have been estimated at 1-km spatial scale for the ice sheet in East Dronning Maud Land, Antarctica. Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) products (i.e., land and atmospheric data products) have been used to estimate net radiation flux during few clear sky days of the years 2007, 2008, 2009, and 2010. Estimated surface energy fluxes using MODIS products have been evaluated using in situ recorded values of energy fluxes. In situ data of the surface energy fluxes and meteorological parameters have been collected using automatic weather stations (AWS) on ice sheet at two locations near the Indian Research Station “Maitri.” Net radiation flux has been estimated for the study area from net shortwave radiation flux and net longwave radiation flux maps. Bias, correlation, and root mean square error (RMSE) between AWS-recorded and MODIS-derived radiation fluxes have been observed as − 23 W m−2, 0.91 and 61 W m−2 for net shortwave radiation flux and − 21.3 W m−2, 0.93 and 64 W m−2 for net radiation flux, respectively. The study highlights the validation of some of the MODIS products and MODIS-derived energy fluxes in Antarctica. Spatial and temporal variations of radiative energy fluxes have also been investigated in the study area.

Keywords

Net radiation flux Antarctic ice sheet MODIS Maitri AWS 

1 Introduction

Energy balance of the cryospheric regions influences the temperature and climate of the globe. Study of the surface energy balance in the cryospheric region includes the study of the snow-meteorological parameters and the energy exchange between snow/ice surface and atmosphere. Many studies have been conducted to study the surface energy balance of the cryospheric regions of Antarctica (e.g., [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), Arctic (e.g., [14, 15, 16]), and glaciers of different mountain ranges (e.g., [17, 18, 19, 20, 21, 22]) all over the globe using in situ recorded snow-meteorological observations.

Radiative energy fluxes are vital components of the energy balance study and small variations in these components change the energy budget of a system. However, these energy fluxes are recorded at sparse locations using in situ measurements in cryospheric regions. Surface energy fluxes estimated from sparse in situ observations may therefore not characterize spatial variation of energy fluxes over a large snow/ice-covered region. To overcome this problem, remote sensing data can be effectively used in conjunction with ground observations via suitably interpolation or modeling techniques to map energy fluxes at spatial level. Remote sensing techniques also have been used to estimate radiative (e.g., [23, 24, 25, 26, 27, 28, 29, 30, 31]) energy fluxes spatially in a variety of land covers. However, very few studies [32, 33, 34] have been reported in snow/ice-covered regions where remote sensing techniques can be very helpful as these are regarded as inaccessible and sparsely observed regions. In the present study, incoming shortwave radiation flux, net shortwave radiation flux, and net radiation flux have been estimated at 1-km spatial scale for East Antarctic ice sheet around the Indian Research Station “Maitri” using MODIS sensor images and MODIS data products. The MODIS data products and derived energy fluxes have been evaluated with in situ observed radiation fluxes from AWS data. Spatial and temporal variations of energy fluxes have also been analyzed in the study area.

2 Study Area

The Antarctic ice sheet consists of three distinct morphological zones—East Antarctica, West Antarctica, and the Antarctic Peninsula [35]. The Antarctic ice sheet varies in thickness across the continent from its periphery to high Antarctic plateau towards South Pole and depth is greatest in East Antarctica. Although Antarctica is known for its vast ice sheet, there are many local topographic and morphologic features on the surface, such as ice-free areas, also known as oases. Approximately 2% area of the Antarctic continent is in oases [36]. Indian research station “Maitri” is located in “Schirmacher Oasis” in the coastal margin of the Dronning Maud Land, East Antarctica, at 70° 45.94′ S and 11° 44.13′ E [8]. The study area considered in the present work extends from 70.2° S to 72° S and 8.6° E to 14° E around Indian Research Station “Maitri” (Fig. 1). The study area comprises ice shelves of Princess Astrid Coast, ice-free area of Schirmacher Oasis, continental ice sheet, and Wohlthat Mountains from north to south. Schirmacher Oasis (70° 44′–70° 46′ S, 11° 24′–11° 54′ E) is about 16 km long in an east-west direction with a maximum width of about 3 km. The oasis is about 70 km south from the Princess Astrid Coast in East Dronning Maud Land, Antarctica. It is a small moraine-covered rocky area having low lying hills. More than 80% area of the oasis is covered with snow during winter period, which melts off during summer months [8]. Ice shelf in the north of the Shirmacher Oasis spreads ~ 70 km up to the Antarctic sea. Continental ice sheet exists in the south and thickness of the ice sheet increases as one move towards south. Wohlthat Mountains are about 70 km south of the Shirmacher Oasis. In the study area considered, the elevation ranges from 22 m near Antarctic sea to 3003 m on high Antarctic plateau. Most part of the study area is flat ice sheet having slope less than 5°. A small terrain with slope angle between 20° and 30° also exists in Schirmacher Oasis and Wohlthat mountains.
Fig. 1

(a) Study area of East Dronning Maud Land, Antarctica. (b) Study area using LIMA satellite image. (c) Field photograph and AWS locations. (d) Field photograph of AWS

The annual mean of temperature, precipitation, and wind at “Maitri” in Schirmacher Oasis were observed around − 9.6 °C, 18 cm, and 15.4 m s−1, respectively. The study area observed high katabatic winds round the year. These winds are the results of cold dense air rolling down the continental slope from the high Antarctic plateau [37, 38]. Severe blizzards in the study area are produced when these winds interact with the warmer air from the ocean [36].

3 Data

Snow-meteorological data recorded from AWS, Digital Elevation Model (DEM), Terra-MODIS sensor data, and Terra-MODIS satellite products form the dataset for the present study. Snow-meteorological data was collected using two automatic weather stations (AWS) in the study area for the years 2007, 2008, 2009, and 2010. The AWSs have been installed on the ice sheet at a distance of 1 km and 8 km from “Maitri” research station towards south, respectively. Both the AWS record air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, reflected solar radiation, atmospheric pressure, and surface temperature and are equipped with calibrated sensors. The detail specifications of the sensors are given in Gusain et al. [8].

Shuttle Radar Topographic Mission (SRTM) DEM at spatial resolution of 90 m has been used in the study. SRTM DEM is freely available and can be downloaded from the Internet (http://srtm.csi.cgiar.org/). SRTM DEM has an error of approximately 16 m in elevation. DEM has been used to generate slope, aspect, and elevation maps of the study area. These maps have been used as input for the estimation of solar zenith angle and incoming shortwave radiation flux.

Terra-MODIS sensor images were downloaded from http://ladsweb.nascom.nasa.gov. Images of clear sky days have been acquired. Terra and Aqua have a sun-synchronous, near polar, circular orbit. Aqua crosses the equator daily at 0800 UTC and Terra crosses the equator daily at 0530 UTC. Salient specifications of MODIS sensor are given in detail by Salomonson et al. [39]. It has 36 bands in the wavelength range 0.4–14.4 μm. The senor has spatial resolution of 250 m in band 1–2, 500 m in band 3–7, and 1000 m in band 8–36. The radiometric resolution of the sensor is 12-bit. These images along with MODIS data products have been used for estimation of surface temperature, net shortwave radiation flux, net longwave radiation flux, and net radiation flux. Dates of acquisition of MODIS images of Antarctica for the present study are given in Table 1. Mostly cloud-free images (images with less than 15% cloud cover in the study area) of MODIS have been considered for the study.
Table 1

Date of acquisition of MODIS sensor images

Sr. no.

MODIS images

Sr. no.

MODIS images

1

02 October 2007

14

07 October 2008

2

11 October 2007

15

11 November 2008

3

20 October 2007

16

20 November 2008

4

27 October 2007

17

24 November 2008

5

01 January 2008

18

13 February 2009

6

03 January 2008

19

19 February 2009

7

05 January 2008

20

05 March 2009

8

07 January 2008

21

19 March 2009

9

02 February 2008

22

24 January 2010

10

13 February 2008

23

25 January 2010

11

17 February 2008

24

28 January 2010

12

21 February 2008

25

21 February 2010

13

02 October 2008

  

MODIS have 44 products categorized into five sections: (a) calibration, (b) atmosphere, (c) land, (d) cryosphere, and (e) ocean. In the present paper, atmospheric and land data products have been used. These products can be downloaded from https://modis.gsfc.nasa.gov/data/dataprod/ and a brief about these products are given as follows:

3.1 MODIS Air and Dew Point Temperature Data Product

For the present study, air and dew point temperature profile data have been retrieved from MOD07_L2 (MOD07 Level-2), MODIS atmospheric profile data product. The temporal resolution of data is 1 day with spatial resolution of 5 × 5 km, at 20 vertical atmospheric pressure levels. These products were generated using statistical regression between collected radiances values and atmospheric profiles data in the International TOVS Processing Package [40]. Air temperature and dew point temperature were retrieved at 1000 hPa vertical pressure level, as the Indian Research Station “Maitri” is close to sea level. It is assumed that the temperatures remain homogenous for entire 5 × 5 km of grid.

3.2 16-Day MODIS Albedo Product

Schaaf et al. [41] has described 16-day MODIS albedo product and Stroeve et al. [42] has validated this product over the Greenland ice sheet. MODIS 16-day albedo product has been categorized into MOD43B3 (Terra) and MCD43B3 (combined Terra and Aqua) albedo products. It consist of both “black-sky albedo” (directional hemispherical reflectance) and “white-sky albedo” (bihemispherical reflectance) [42]. In this paper, Terra-MODIS 16-day albedo product MOD43B3 has been used. It consists of seven spectral bands (band 1–7) and three broad bands (0.3–0.7 μm, 0.7–3.0 μm, and 0.3–5.0 μm). To compute shortwave albedo from narrowband albedos, a linear combination of 7 MODIS spectral bands were carried out. The coefficients were obtained from Liang et al. [43] to estimate snow/ice albedo.

4 Methodology

4.1 Estimation of Net Shortwave Radiation Flux

Net shortwave radiation flux has been estimated using incoming shortwave radiation flux (SWF↓) and albedo (α) maps. Incoming shortwave radiation flux has been estimated using Zillman [44] parameterization scheme [25, 45], given as
$$ SWF\downarrow ={S}_0{\cos}^2\theta /\left[1.085\cos \theta +{e}_{\mathrm{a}}\left(2.7+\cos\ \theta \right)0.001+0.1\right] $$
(1)
where S0 is solar constant (1367 W m−2), θ is solar zenith angle, and ea is vapor pressure. Vapor pressure (ea) [46] has been estimated using Clausius-Clapeyron equation [25] as
$$ {e}_{\mathrm{a}}=6.11\ \mathrm{Exp}\left[{L}_{\mathrm{v}}/{R}_{\mathrm{v}}\left(1/{T}_0-1/{T}_{\mathrm{d}}\right)\right] $$
(2)
where Lv is the latent heat of vaporization (2.5 × 106 J kg−1), Rv is the gas constant for water vapor (461 J kg−1 K−1), and T0 = 273 K.
For mountainous topography, solar zenith angle θ is replaced with angle of incidence i and given as [47].
$$ \cos\ i=\sin \varphi\ \cos a\ \cos \left({\alpha}_a-{\alpha}_{\mathrm{s}}\right)+\cos \varphi\ \sin a $$
(3)
where φ is the slope angle, a is the elevation angle of the sun, αa is azimuth angle of the sun, and αs is the aspect of the surface.
Air temperature and dew point temperature maps for the study area have been generated using Terra-MODIS-L2 product and retrieved at 1000 hPa. Incoming shortwave radiation flux maps have been generated using Eqs. (1) and (2). Albedo of the snow/ice surface and incoming shortwave radiation flux have been used to estimate net shortwave radiation flux. Albedo of the study area has been estimated using MODIS 16-day albedo data product (MOD43B3) [48]. Net shortwave radiation flux (SWFnet) has been estimated using
$$ SW{F}_{\mathrm{net}}= SWF\downarrow \left(\ 1-\alpha \right) $$
(4)

4.2 Estimation of Net Longwave Radiation Flux and Net Radiation Flux

Incoming longwave radiation flux (LW↓) has been estimated using model proposed by Prata [49] and is given aswhere єm is emissivity of the atmosphere, σ is Stephan Boltzmann constant (5.67 × 10−8 W m−2 K−4), and Ta is air temperature in kelvin retrieved from MODIS Level-2 product. єm is estimated aswhere wp is precipitable water content
$$ {w}_{\mathrm{p}}=46.5\ \left({e}_{\mathrm{a}}/{T}_{\mathrm{a}}\right) $$
(7)
where wp is precipitable water content and ea is vapor pressure (Pa).
Outgoing longwave radiation flux (LW↑) has been estimated using Stephan Boltzmann equation and is given aswhere σ is the Stephan Boltzmann constant, Ts is the surface temperature in kelvin, and єs is the surface emissivity. Surface temperature maps for the study area have been generated using thermal bands of MODIS data by the method proposed in Gusain et al. [50] and given as follows:
$$ {T}_{\mathrm{s}}=-260.0967412+0.959826974{T}_{31}-1.034104696\left({T}_{31}\hbox{--} {T}_{32}\right) $$
(9)
where T31 and T32 are brightness temperature of the MODIS thermal bands 31 and 32, respectively. Brightness temperature for the thermal bands 31 and 32 of MODIS has been estimated by the method proposed by Wan [51] and Qin and Karnieli [52]. Surface emissivity of the snow/ice surface has been assumed unity [2, 53].
Net longwave radiation flux (LWnet) has been estimated asand net radiation flux (Rnet) for clear sky days has been estimated as

Using above methodology, surface energy fluxes have been estimated at 1-km spatial scale in Antarctica. MODIS satellite products have been used to generate the spatial maps and validated with in situ recorded AWS data.

5 Results and Discussion

Incoming shortwave radiation flux, net shortwave radiation flux, net longwave radiation flux, and net radiation flux have been estimated at 1-km spatial scale for the study area in Antarctica. Air and dew point temperature maps have been generated using MOD07_L2 data products at 1000 hPa level. Figure 2 shows the comparison of MODIS Level-2 product derived air temperature and AWS-recorded in situ air temperature during different clear sky days of the years 2007 to 2010.
Fig. 2

Comparison of observed and estimated air temperature using MODIS data product (MOD07) at 1000 hPa level for clear sky days during 2007–2010

Bias, correlation coefficient, and root mean square error (RMSE) in MODIS air temperature products and in situ recorded air temperature have been observed as 0.6 K, 0.71 K, and 4.4 K, respectively. MODIS air temperature product has shown better results for the present study area compared with other studies reported earlier, e.g., Bisht et al. [25] for Southern Great Plains covering parts of Oklahoma and Kansas in USA, and they reported RMSE to be 5.01 K.

Air temperature maps were used to estimate incoming shortwave radiation flux maps in the study area. MODIS-derived incoming shortwave radiation flux maps were evaluated using in situ recorded incoming shortwave radiation flux from an upward-looking pyranometer of make Kipp & Zonen and mounted on AWS. Incoming shortwave radiation flux has been estimated using Eq. (1) and flux map at 0900 UTC on 21 February 2010 is shown in Fig. 3. Incoming shortwave radiation flux varies from 42 to 1130 W m−2 with mean values of 372 ± 57 W m−2 in the study region. Large spatial variation has been observed in mountainous region of the study area as compared to flat ice sheet, as shown in Fig. 3. This may be due to large variation in incidence angle of solar radiation on mountain slopes. Incoming shortwave radiation flux has also been estimated for other clear sky days, as mentioned in Table 1.
Fig. 3

Incoming shortwave radiation flux map (in W m−2) for 21 February 2010

Figure 4 shows a plot between MODIS-derived and in situ recorded incoming shortwave radiation flux during different days of the years 2007 to 2010. Absolute errors of 10 to 101 W m−2 were observed. Bias of − 28.3 W m−2, RMSE of 51.6 W m−2, and correlation coefficient of 0.95 have been observed between MODIS-derived and AWS-recorded incoming shortwave radiation flux. RMSE obtained is about ~ 14% of the mean values of the incoming shortwave radiation flux and is in correspondence with the RMSE values of 14–25% reported by Wang and Pinker [34] and 14–21% reported by Niu et al. [33] in different cryospheric regions.
Fig. 4

Satellite-estimated vs in situ observed incoming shortwave radiation flux (in W m −2)

Albedo of the ice sheet has been estimated from Terra 16-day MODIS (MOD43B3) albedo data product using methodology described by Stroevea et al. [48]. Figure 5 shows the comparison of albedo using MODIS product and AWS-recorded albedo during different days. Correlation coefficient of 0.86, bias of − 0.1, and RMSE of 0.09 have been observed between observed and satellite-estimated albedo. Stroevea et al. [48] estimated snow albedo over the Greenland ice sheet using 16-day MODIS albedo product and reported correlation of ~ 0.80 with in situ albedo values. Results of the present study in Antarctica are comparable with the results of Stroevea et al. [48] for Greenland ice sheet.
Fig. 5

Comparison of AWS-recorded and satellite-estimated albedo using Terra 16-day MODIS data product (MOD43B3) during 2007–2010

Figure 6 shows net shortwave radiation flux map at 0900 h on 21 February 2010. Net shortwave radiation flux varies from 25 to 795 W m−2 in the study area with mean values of 148 ± 42 W m−2. Large variation in net shortwave radiation flux values has been seen at small distances in mountainous region. This is due to large variation in slope and aspect of the mountainous region which cause a large variation in local incidence angle of the incoming shortwave radiation. Spatial variation in net shortwave radiation flux in the study area has been observed mostly due to spatial variation in albedo and incoming shortwave radiation flux. Spatial variation in albedo occurs due to variable melting of the ice sheet during austral summer months December, January, and February. The regions with high melting are having high water content and low albedo values resulting into high values of net shortwave radiation flux. Regions having net shortwave radiation flux in the range of 150–250 W m−2 and higher, are the regions with high melting compare to other regions having net shortwave radiation flux lower than 150 W m−2.
Fig. 6

Net shortwave radiation flux map (in W m−2) for 21 February 2010

Figure 7 shows a comparison of the satellite-estimated and AWS-recorded values of net shortwave radiation flux during different days. Absolute errors of 4 to 186 W m−2 were observed during different days. Bias of − 23 W m−2, RMSE of 61 W m−2, and correlation coefficient of 0.91 have been observed between satellite-estimated and AWS-recorded net shortwave radiation flux. RMSE obtained is ~ 19% of the mean values of the net shortwave radiation flux. Gusain et al. [32] reported an error of ~ 85 W m−2 in estimation of net shortwave radiation flux in Western Himalaya for some clear sky days of the winter season. They reported an error of about 14–27% of the mean values during different days. There is no other data available from any other source on estimation of net shortwave radiation flux for snow/ice-covered region to compare the results.
Fig. 7

Satellite-estimated vs in situ recorded net shortwave radiation flux (W m −2)

Net longwave radiation flux has been estimated using air temperature and surface temperature maps of the study area using Eq. (10). Surface temperature map has been generated using thermal bands data of MODIS sensor images and detailed methodology is given in Gusain et al. [50]. Figure 8 shows net longwave radiation flux map at 0900 h on 21 February 2010. Net longwave radiation flux varies from − 140 to − 30 W m−2 in the study area with mean values of − 71 ± 12 W m−2. Regions with ice-free areas and high melting zones of ice sheet have shown high losses due to longwave radiation. Mean absolute error varies from 1.5 to 30 W m−2 during different days of the years 2007 to 2010. Bias of 8 W m−2 and RMSE of 14.5 W m−2 have been obtained which is about 22% of the mean values of the net longwave radiation flux.
Fig. 8

Net longwave radiation flux map (in W m−2) for 21 February 2010

Net radiation flux has been estimated using Eq. (11). Figure 9 shows net radiation flux map at 0900 h on 21 February 2010. Net radiation flux varied from − 98 to 872 W m−2 in the study area with mean values of 77 ± 42 W m−2. Generally, regions with high net shortwave radiation flux have also shown high values of net radiation flux. These are generally areas with high ice melt or ice-free areas. Figure 10 shows a comparison of the satellite-estimated and in situ net radiation flux values during different days. Absolute errors of 3 to 151 W m−2 were observed. Bias of − 21.3 W m−2, RMSE of 64 W m−2, and correlation coefficient of 0.93 have been observed between satellite-estimated and in situ observed net radiation flux. RMSE obtained is about 29% of the mean values of net radiation flux. RMSE obtained in estimation of net shortwave radiation and net longwave radiation fluxes contribute to higher value of RMSE in estimation of net radiation flux.
Fig. 9

Net radiation flux map (in W m−2) for 20 February 2010 in Antarctica

Fig. 10

Satellite-estimated vs in situ observed net radiation flux (W m −2) for 21 February 2010

In Antarctica, November, December, January, and February are the summer months and melting of ice sheet has been observed during these months. Temporal variation in the surface energy fluxes of the ice sheet has also been analyzed during few days of the summer months 2010. Table 2 shows mean values of solar zenith angle, surface temperature, incoming shortwave radiation flux, net shortwave radiation flux, net longwave radiation flux, and net radiation flux for 24 January 2010, 28 January 2010, and 21 February 2010. Incoming shortwave radiation flux in the study area has been found to decrease from 24 January 2010 to 21 February 2010 and this may be attributed to increasing solar zenith angle during this period. Net shortwave radiation flux decreases from 24 January 2010 to 21 February 2010 and has been observed in correspondence with the values of incoming shortwave radiation flux on these dates. A decrease in the net longwave radiation flux from 24 January 2010 to 21 February 2010 has been observed and may be attributed to decrease in surface temperature during this period. It has also been observed from AWS data that net longwave radiation flux values are higher during summer period compared with winter period [8]. Net radiation flux decreases from 12 January 2010 to 21 February 2010 due to decrease in net shortwave radiation flux and net longwave radiation flux. The decrease in net radiation flux indicates the decrease in amount of energy available for melting of the ice sheet during the stated period.
Table 2

Mean values of net radiation components in the study area

Date

Solar zenith angle (°) (mean ± SD)

Surface temperature (°C) (mean ± SD)

Incoming shortwave radiation flux (W m−2) (mean ± SD)

Net shortwave radiation flux (W m−2) (mean ± SD)

Net longwave radiation flux (W m−2) (mean ± SD)

Net radiation flux (W m−2) (mean ± SD)

24 Jan 2010

55.2 ± 0.21

− 8.3 ± 5.2

563 ± 57

200 ± 52

− 83 ± 11

117 ± 51

28 Jan 2010

57.8 ± 0.36

− 9.5 ± 4.6

482 ± 62

179 ± 50

− 76 ± 12

102 ± 47

21 Feb 2010

68.7 ± 0.26

− 18.6 ± 4.9

372 ± 58

148 ± 42

− 71 ± 12

77 ± 42

6 Conclusion

Most of the studies on surface energy fluxes of snow/ice surfaces in the past are limited to point observations using AWS or micro-meteorological towers. However, these studies do not account for the spatial variability in energy fluxes over a large study area. In this paper, incoming shortwave radiation flux, net shortwave radiation flux, net longwave radiation flux, and net radiation flux have been estimated at 1-km spatial scale in Antarctica using MODIS sensor images and MODIS products. MODIS products and derived energy fluxes have been evaluated using in situ recorded energy fluxes at AWS locations. MODIS Level-2 product of air temperature has shown good correlation with in situ air temperature data with an RMSE of 4.4 K and correlation coefficient of 0.71. Terra-MODIS 16-day albedo product MOD43B3 has also shown a good correlation with in situ recorded albedo values. Correlation coefficient of 0.86 and RMSE of 0.09 have been obtained between MODIS-derived albedo and AWS-recorded albedo. Incoming shortwave radiation flux has been estimated with an accuracy of ~ 14% of the mean values. The accuracy is comparable to that reported by Wang and Pinker [34] and Niu et al. [33] for snow/ice surfaces.

Net shortwave radiation flux has been estimated with RMSE of ~ 19% which is comparable to the errors reported by Gusain et al. about 14–27% of the mean values in Western Himalaya. Net longwave radiation and net radiation fluxes have been estimated up to RMSE of ~ 22% and ~ 29% of the mean values, respectively. As there is no data from any other source on spatial estimation of net longwave radiation flux and net radiation flux using remote sensing data for snow/ice-covered region, the results of this study cannot be compared. RMSE obtained in estimation of net shortwave radiation and net longwave radiation fluxes contribute to higher value of RMSE obtained in estimation of net radiation flux.

MODIS air temperature and albedo data products proved to be valuable and evaluation with in situ recorded data has shown high correlation coefficient. MODIS-derived geospatial maps of radiative energy fluxes may be useful in many studies related to snow/ice, hydrology, climatology, ecology, etc. in Antarctica, particularly in the absence of in situ recorded data.

Notes

Acknowledgements

Authors are thankful to the anonymous reviewers for improving the quality of the manuscript. We would like to thank all the scientists/technical officers/technical staff of Snow and Avalanche Study Establishment (SASE), who collected valuable data for the study in Antarctica. Logistic support provided by National Centre of Antarctic and Ocean Research (NCAOR), Goa, is duly acknowledged. NASA’s LAADS web (http://ladsweb.nascom.nasa.gov) and https://modis.gsfc.nasa.gov/data/dataprod/ is duly acknowledged for providing MODIS data, MODIS data products, and SRTM DEM. LIMA USGS website is duly acknowledged for providing Antarctica Landsat Mosaic.

References

  1. 1.
    Bintanja R, Reijmer CH (2001) Meteorological conditions over Antarctic blue-ice areas and their influence on the local surface mass balance. J Glaciol 17:37–50CrossRefGoogle Scholar
  2. 2.
    Bintanja R, van den Broeke MR (1994) Local climate, circulation and surface-energy balance of an Antarctic blue-ice area. Ann Glaciol 20:160–168CrossRefGoogle Scholar
  3. 3.
    Bintanja R, van den Broeke MR (1995) The surface energy balance of Antarctic snow and blue ice. J Appl Meteorol 34:902–926CrossRefGoogle Scholar
  4. 4.
    Bintanja R (1999) On the glaciological, meteorological, and climatological significance of Antarctic blue ice areas. Rev Geophys 37:337–359CrossRefGoogle Scholar
  5. 5.
    Bliss AK, Cuffey KM, Kavanaugh JL (2011) Sublimation and surface energy budget of Taylor Glacier, Antarctica. J Glaciol 57:684–696CrossRefGoogle Scholar
  6. 6.
    Fountain AG, Nylen TH, MacClune KL, Dana GL (2006) Glacier mass balances (1993–2001), Taylor Valley, McMurdo Dry Valleys, Antarctica. J Glaciol 52:451–462CrossRefGoogle Scholar
  7. 7.
    Genthon C, Lardeux P, Krinner G (2007) The surface accumulation and ablation of a coastal blue-ice area near Cap Prudhomme, Terre Adelie, Antarctica. J Glaciol 53:635–645CrossRefGoogle Scholar
  8. 8.
    Gusain HS, Mishra VD, Arora MK (2014a) A four-year record of the meteorological parameters, radiative and turbulent energy fluxes at the edge of the East Antarctic ice sheet, close to Schirmacher Oasis. Antarct Sci 26(1):93–103CrossRefGoogle Scholar
  9. 9.
    Hoffman MJ, Fountain AG, Liston GE (2008) Surface energy balance and melt thresholds over 11 years at Taylor Glacier, Antarctica. J Geophys Res 113:1–12.  https://doi.org/10.1029/2008JF001029 CrossRefGoogle Scholar
  10. 10.
    Kuipers Munneke P, van den Broeke MR, King JC, Gray T, Reijmer CH (2012) Near-surface climate and surface energy budget of Larsen C ice shelf, Antarctic Peninsula. Cryosphere 6:353–363CrossRefGoogle Scholar
  11. 11.
    Lewis KJ, Fountain AG, Dana GL (1998) Surface energy balance and melt water production for a Dry Valley glacier, Taylor Valley, Antarctica. Ann Glaciol 27:603–609CrossRefGoogle Scholar
  12. 12.
    Schneider C (1999) Energy balance estimates during the summer season of glaciers of the Antarctic Peninsula. Glob Planet Chang 22:117–130CrossRefGoogle Scholar
  13. 13.
    van den Broeke M, Reijmer C, van As D, Boot W (2006) Daily cycle of the surface energy balance in Antarctica and the influence of clouds. Int J Climatol 26:1587–1605CrossRefGoogle Scholar
  14. 14.
    Ohmura A (1982) Climate and energy balance on the arctic tundra. J Climatol 2(1):65–84CrossRefGoogle Scholar
  15. 15.
    Lindsay RW (1998) Temporal variability of the energy balance of thick Arctic pack ice. J Clim 11:313–333CrossRefGoogle Scholar
  16. 16.
    Lynch AH, Chapin FS, Hinzman LD, Wu W, Lilly E, Vourlitis G, Kim E (1999) Surface energy balance on the arctic tundra : measurements and models. J Clim 12:2585–2606CrossRefGoogle Scholar
  17. 17.
    Azam MF, Wagnon P, Vincent C, Ramanathan AL, Mandal A, Pottakkal JG (2014) Processes governing the mass balance of Chhota Shigri Glacier (Western Himalaya, India) assessed by point-scale surface energy balance measurements. Cryosphere 8:2195–2217CrossRefGoogle Scholar
  18. 18.
    Brock BW, Willis IC, Sharp MJ (2006) Measurement and parameterization of aerodynamic roughness length variations at Haut Glacier d’Arolla, Switzerland. J Glaciol 52:281–297CrossRefGoogle Scholar
  19. 19.
    Giesen RH, van den Broeke MR, Oerlemans J, Andreassen LM (2008) Surface energy balance in the ablation zone of Midtdalsbreen, a glacier in southern Norway : interannual variability and the effect of clouds. J Geophys Res 113, D21111:1–17.  https://doi.org/10.1029/2008JD010390 CrossRefGoogle Scholar
  20. 20.
    Klok EJ, Nolan M, van den Broeke MR (2005) Analysis of meteorological data and the surface energy balance of McCall Glacier, Alaska, USA. J Glaciol 51(174):451–461CrossRefGoogle Scholar
  21. 21.
    Sicart JE, Hock R, Six D (2008) Glacier melt, air temperature and energy balance in different climates: the Bolivian Tropics, the French Alps, and northern Sweden. J Geophys Res 113, D24113:1–11.  https://doi.org/10.1029/2008JD010406 CrossRefGoogle Scholar
  22. 22.
    Wagnon P, Sicart JE, Berthier E, Chazarin JP (2003) Wintertime high-altitude surface energy balance of a Bolivian glacier, Illimani, 6340 m above sea level. J Geophys Res 108, D6:1–14.  https://doi.org/10.1029/2002JD002088 CrossRefGoogle Scholar
  23. 23.
    Bisht G, Bras RL (2010b) Estimation of net radiation from the Moderate Resolution Imaging Spectroradiometer over the continental United States. IEEE Trans Geosci Remote Sens 49(6):2448–2462CrossRefGoogle Scholar
  24. 24.
    Bisht G, Bras RL (2010a) Estimation of the net radiation from MODIS data under all sky conditions: Southern Great Plains case study. Remote Sens Environ 114:1522–1534CrossRefGoogle Scholar
  25. 25.
    Bisht G, Venturini V, Islam S, Jiang L (2005) Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days. Remote Sens Environ 97:52–67CrossRefGoogle Scholar
  26. 26.
    Hetrick WA, Rich PM, Barnes FJ, Weiss SB (1993) GIS-based solar radiation flux models, American Society of Photogrammetry and Remote Sensing Technical papers (GIS Photogrammetry and Modeling) 3:132–143Google Scholar
  27. 27.
    Jiang L, Islam S (2010) An intercomparison of regional latent heat flux estimation using remote sensing data. Int J Remote Sens 24:2221–2236CrossRefGoogle Scholar
  28. 28.
    Journée M, Bertrand C (2010) Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements. Remote Sens Environ 114:2692–2704CrossRefGoogle Scholar
  29. 29.
    Oliphant A, Susan C, Grimmond B, Schmid HP, Wayson CA (2006) Local-scale heterogeneity of photosynthetically active radiation (PAR), absorbed PAR and net radiation as a function of topography, sky conditions and leaf area index. Remote Sens Environ 103:324–337CrossRefGoogle Scholar
  30. 30.
    Samani Z, Bawazir AS, Bleiweiss M, Skaggs R, Tran VD (2007) Estimating daily net radiation over vegetation canopy through remote sensing and climatic data. J Irrig Drain Eng 133:291–297CrossRefGoogle Scholar
  31. 31.
    Wang W, Liang S (2009) Estimation of high-spatial resolution clear-sky longwave downward and net radiation over land surfaces from MODIS data. Remote Sens Environ 113:745–754CrossRefGoogle Scholar
  32. 32.
    Gusain HS, Mishra VD, Arora MK (2014b) Estimation of net shortwave radiation flux of western Himalayan snow cover during clear sky days using remote sensing and meteorological data. Remote Sens Lett 5(1):83–92CrossRefGoogle Scholar
  33. 33.
    Niu X, Pinker RT, Cronin MF (2010) Radiative fluxes at high latitude. Geophys Res Lett 37(L20811):1–5Google Scholar
  34. 34.
    Wang H, Pinker RT (2009) Shortwave radiative fluxes from MODIS: model development and implementation. J Geophys Res 114, D20201:1–17.  https://doi.org/10.1029/2008JD010442 CrossRefGoogle Scholar
  35. 35.
    King JC, Turner J (1997) Antarctic meteorology and climatology. Cambridge University Press, Cambridge, p 409CrossRefGoogle Scholar
  36. 36.
    Tyagi A, Singh UP, Mohapatra M (2011) Weather & weather systems at Schirmacher Oasis (Maitri) during recent two decades – a review. MAUSAM 62:513–534Google Scholar
  37. 37.
    van den Broeke M, Reijmer C, van de Wal R (2004) Surface radiation balance in Antarctica as measured with automatic weather stations. J Geophys Res.  https://doi.org/10.1029/2003JD004394
  38. 38.
    Zhou M, Zhang Z, Zhong S, Lenschow D, Hsu HM, Sun B, Gao Z, Li S, Bian X, Yu L (2009) Observations of near-surface wind and temperature structures and their variations with topography and latitude in East Antarctica. J Geophys Res.  https://doi.org/10.1029/2008JD011611
  39. 39.
    Salomonson VV, Barnes WL, Maymon PW, Montgomery HE, Ostrow H (1989) MODIS: advanced facility instrument for studies of the earth as a system. IEEE Trans Geosci Remote Sens 27:145–153CrossRefGoogle Scholar
  40. 40.
    Menzel WP, Seemann SW, Li J, Gumley LE (2002) MODIS atmospheric profile retrieval algorithm theoretical basis document,Version 6, Reference Number: ATBD-MOD-07. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod07.pdf. Accessed on 12/04/2003
  41. 41.
    Schaaf CB, Gao F, Strahler AH, Lucht W, Li X, Tsang T et al (2003) First operational BRDF, albedo nadir reflectance product from MODIS. Remote Sens Environ 83:135–148CrossRefGoogle Scholar
  42. 42.
    Stroeve J, Box J, Gao F, Liang S, Nolin A, Schaaf C (2005) Accuracy assessment of the MODIS 16-day snow albedo product: comparisons with Greenland in situ measurements. Remote Sens Environ 94:46–60CrossRefGoogle Scholar
  43. 43.
    Liang S, Strahler AH, Walthall CW (1999) Retrieval of land surface albedo from satellite observations: a simulation study. J Appl Meteorol 38:712–725CrossRefGoogle Scholar
  44. 44.
    Zillman JW (1972) A study of some aspects of the radiation and heat budgets of the Southern Hemisphere oceans, Meteorological study, vol 26. Commonwealth Bureau of Meteorology, Canberra, Australia, p 562Google Scholar
  45. 45.
    Niemelä S, Räisänen P, Savijärvi H (2001) Comparison of surface radiative flux parameterizations part II. Shortwave radiation. Atmos Res 58:141–154CrossRefGoogle Scholar
  46. 46.
    Buck AL (1981) New equations for computing vapor pressure and enhancement factor. J Appl Meteorol 20:1527–1532CrossRefGoogle Scholar
  47. 47.
    Gates DM (1980) Biophysical ecology. Springer, New YorkCrossRefGoogle Scholar
  48. 48.
    Stroevea JC, Box JE, Haran T (2006) Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet. Remote Sens Environ 105:155–171CrossRefGoogle Scholar
  49. 49.
    Prata AJ (1996) A new longwave formula for estimating downward clear-sky radiation at the surface. Q J R Meteorol Soc 122:1127–1151CrossRefGoogle Scholar
  50. 50.
    Gusain HS, Mishra VD, Brar GS, Ganju A (2015) A simple model for estimation of snow/ice surface temperature of Antarctic ice sheet using remotely sensed thermal band data. Indian Journal of Radio & Space Physics 44:51–55Google Scholar
  51. 51.
    Wan Z (1999) MODIS land-surface temperature algorithm theoretical basis document (LST ATBD), version 3.3. Institute for Computational Earth System Science, University of California, Santa Barbara, pp 1–77Google Scholar
  52. 52.
    Qin Z, Karnieli A (1999) Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data. Int J Remote Sens 20:2367–2393CrossRefGoogle Scholar
  53. 53.
    Gusain HS, Singh KK, Mishra VD, Srivastava PK, Ganju A (2009) Study of surface energy and mass balance at the edge of the Antarctic ice sheet during summer in Dronning Maudland, East Antarctica. Antarct Sci 21(4):401–409CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • H. S. Gusain
    • 1
    Email author
  • Dhiraj Kumar Singh
    • 1
    • 2
  • V. D. Mishra
    • 1
  • M. K. Arora
    • 2
  1. 1.Snow and Avalanche Study Establishment, DRDOChandigarhIndia
  2. 2.PEC (Deemed to be University)ChandigarhIndia

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