Abstract
Active as well as passive spaceborne sensors can be used to monitor spring snowmelt on regional to continental scale. Change detection methods are used to determine dates related to the thaw period. They comprise initial thaw, primary thaw, start of diurnal thaw/refreeze period, mean date of thaw, end of thaw and start of greening-up of vegetation. Only the latter is determined by use of passive optical sensors and combines measurements of visible and infrared radiation. All other approaches use microwave data. Some instruments such as the scatterometer Seawinds on QuikScat and the radiometer AMSR-E on Aqua make several measurements per day allowing the detection of diurnal thaw and refreeze, which is characteristic of the spring snowmelt period in northern latitudes. A specific, diurnal difference approach developed for QuikScat allows the determination of the length of the final period of diurnal thaw/refreeze. This duration and the spatial dynamics are closely linked to surface hydrology and ecosystem processes.
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References
Aagaard K, Carmack EC (1989) The role of sea ice and other fresh water in the arctic circulation. J Geophys Res 94(C10):14485–14498
Abdalati W, Steffen K (1997) Snowmelt on the Greenland ice sheet as derived from passive microwave satellite data. J Climate 10:165–175
Ashcraft IS, Long DG (2005) Differentiation between melt and freeze stages of the melt cycle using SSM/I channel ratios. IEEE Trans Geosci Remote Sens 43(6):1317–1323
Ashcraft IS, Long DG (2006) Comparison of methods for melt detection over Greenland using active and passive microwave measurements. Int J Remote Sens 27:2469–2488
Aurela M, Laurilla T, Tuovinen J-P (2004) The timing of snow melt controls the annual CO2 balance in a subarctic fen. Geophys Res Lett 31:L16119
Bartsch A, Kidd R, Wagner W, Bartalis Z (2007a) Temporal and spatial variability of the beginning and end of daily spring freeze/thaw cycles derived from scatterometer data. Remote Sens Environ 1996:360–374
Bartsch A, Pathe C, Wagner W (2007c) Wetland mapping in the West Siberian Lowlands with ENVISAT ASAR global mode. Proceedings of the ENVISAT Symposium, Montreux 2007, ESA SP-636
Bartsch A, Wagner W, Rupp K, Kidd R (2007b) Application of C and Ku-band scatterometer data for catchment hydrology in northern latitudes. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain, 23–27 July 2007
Bartsch A, Wagner W, Pathe C, Scipal K, Sabel D, Wolski P (2009) Global monitoring of wetlands – the value of ENVISAT ASAR global mode. J Environ Manage 90:2226–2233
Biancamaria S, Mognard NM, Boone A, Grippa M, Josberger EG (2008) A satellite snow depth multi-year average derived from SSM/I for the high latitude regions. Remote Sens Environ 112:2557–2568
Delbart N, Kergoat L, Le Toan T, Lhermitte J, Picard G (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97:26–38
Delbart N, Le Toan T, Kergoat L, Fedotova V (2006) Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004). Remote Sens Emviron 1001:52–62
Dye GD, Tucker CJ (2003) Seasonality and trends of snow cover, vegetation index and temperature in northern Eurasia. Geophys Res Lett 30(7):1405
Friborg T, Soegaard H, Christensen TR, Lloyd CR, Panikov NS (2003) Siberian wetland: where a sink is a source. Geophys Res Lett 30:2129–2132
Gao BC (1996) NDWI – a normalized difference water index for remote sensing of liquid water from space. Remote Sens Environ 58:257–266
Goulden ML, Wofsy SC, Harden JW, Trumbore SE, Crill PM, Gower ST, Fries T, Daube BC, Fan S-M, Sutton DJ, Bazzaz A, Munger JW (1998) Sensitivity of boreal forest carbon balance to soil thaw. Science 279:214–217
Grippa M, Kergoat L, Le Toan T, Mognard NM, Delbart N, L’Hermitte J, Vincente-Serrano SM (2005a) The impact of snow depth and snowmelt on the vegetation variability over Central Siberia. Geophys Res Lett 32:L21412
Grippa M, Mognard N, Le Toan T (2005b) Comparison between the interannual variability of snow parameters derived from SSM/I and the Ob river discharge. Remote Sens Environ 98:35–44
Hall DK, Riggs GA (2007) Accuracy assessment of the MODIS snow products. Hydrol Process 21:1534–1547
Hall DK, Foster JL, Verbyla DL, Klein AG, Benson CS (1998) Assessment of snow-cover mapping accuracy in a variety of vegetation-cover densities in central Alaska. Remote Sens Environ 66:129–137
Hüttich C, Herold M, Schmullius C, Egorov V, Bartalev SA (2007) Indicators of Northern Eurasia’s land-cover change trends from SPOT-VEGETATION time-series analysis 1998–2005. Int J Remote Sens 28(18):4199–4206
Keddy PA, Fraser LH (2005) Introduction: big is beautiful. In: Fraser LH, Keddy PA (eds) The world’s largest wetlands: ecology and conservation. Cambridge University Press, Cambridge, pp 1–10
Kimball J, McDonald K, Keyser A, Frolking S, Running S (2001) Application of the NASA Scatterometer (NSCAT) for Determining the Daily Frozen and Nonfrozen Landscape of Alaska. Remote Sens Environ 75(1):113–126
Kimball JS, McDonald KC, Frolking SE, Running SW (2004a) Radar remote sensing of the spring thaw transition across a boreal landscape. Remote Sens Environ 89:163–175
Kimball JS, McDonald KC, Running SW, Frolking SE (2004b) Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sens Environ 90(2):243–258
Luojus KP, Pulliainen JT, Metsamaki SJ, Hallikainen MT (2007) Snow-covered area estimation using satellite radar wide-swath images. IEEE Trans Geosci Remote Sens 45(4):978–989
McDonald KC, Kimball JS, Njoku E, Zimmermann R, Zhao M (2004) Variability in springtime thaw in the terrestrial high latitudes: monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interact 8(020):1–23
Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981-1991. Nature 386:698–702
Nagler T, Rott H (2000) Retrieval of wet snow by means of multitemporal SAR data. IEEE Trans Geosci Remote Sens 38:754–765
Nghiem SV, Steffen K, Kwok R, Tsai W-Y (2001) Detection of snow melt regions on the Greenland ice sheet using diurnal backscatter change. J Glaciol 47(159):539–547
Peterson BJ, Holmes RM, McClelland JW, Vörösmarty CJ, Lammers RB, Shiklomanov AI, Shiklomanov IA, Rahmstorf S (2002) Increasing river discharge to the Arctic ocean. Science 298:2171–2173
Pietroniro A, Töyrä J, Loconte R, Kite G (2005) Remote sensing of surface water and soil moisture. In: Duguay CR, Pietroniro A (eds) Remote sensing in northern hydrology. AGU geophysical monograph, 163, 119–142
Ramage JM, Isacks BL (2002) Determination of melt onset and refreeze timing on southeast Alaskan Icefields using SSM/I diurnal amplitude variations. Ann Glaciol 34:391–398
Ramage JM, Apgar JD, McKenney RA, Hanna W (2007) Spatial variability of snowmelt timing from AMSR-E and SSM/I passive microwave sensors, Pelly River, Yukon Territory, Canada. Hydrol Process 21:1548–1560
Rawlins MA, McDonald KC, Frolking S, Lammers RB, Fahnestock M, Kimball JS, Vörösmarty CJ (2005) Remote sensing of snow thaw at the Pan-Arctic scale using the Seawinds scatterometer. J Hydrol 312:294–311
Rawlins MA, Fahnestock M, Frolking S, Vörösmarty CJ (2007) On the evaluation of snow water equivalent estimates over the terrestrial Arctic drainage basin. Hydrol Process 21:1616–1623
Scherer D, Hall DK, Hochschild V, König M, Winther J-G, Duguay CR, Pivot F, Mätzler C, Rau F, Seidel K, Solberg R, Walker AE (2005) Remote sensing of snow cover. In: Duguay CR, Pietroniro A (ed.) Remote sensing in northern hydrology. AGU Geophysical Monograph, 163, 7–38
Schmullius C, Hese S, Knorr D (2003) Siberia-II – a multi sensor approach for greenhouse gas accounting in northern Eurasia. Petermanns Geogr Mitt 147(6):4–5
Sheng Y, Smith LC, MacDonald GM, Kremenetski KV, Frey KE, Velichko AA, Lee M, Beilman DW, Dubinin P (2004) A high-resolution GIS-based inventory of the West Siberian peat carbon pool. Global Biogeochem Cycle 108:GB3004
Shibistova O, Lloyd J, Evgrafova S, Savushkina N, Zrazhevskaya G, Arneth A, Knohl A, Kolle O, Schulze ED (2002a) Seasonal and spatial variability in soil CO2 efflux rates for a central Siberian Pinus sylvestris forest. Tellus B 54(5):552–567
Shibistova O, Lloyd J, Zrazhevskaya G, Arneth A, Kolle O, Knohl A, Asrekhantceva N, Shijneva I, Schmerler J (2002b) Annual ecosystem respiration budget for a Pinus sylvestris stand in central Siberia. Tellus B 54(5):568–589
Shiklomanov AI, Yakovleva TI, Lammers RB, Karasev IP, Vörösmarty CJ, Linder E (2006) Cold region river discharge uncertainty – estimates from large Russian rivers. J Hydrol 326:231–256
Shvidenko AZ, Nilsson S, Stolbovoi VS, Gluck M, Shchepashchenko DG, Rozhkov VA (2000) Aggregated estimation of the basic parameters of biological production and the carbon budget of Russian terrestrial ecosystems: 1. Stocks of plant organic mass. Russ J Ecol 31(6):371–378
Smith NV, Saatchi SS, Randerson JT (2004) Trends in high northern latitude soil freeze thaw cycles from 1988 to 2002. J Geophys Res 109:D12101
Sokol J, Pultz TJ, Walker AE (2003) Passive and active airborne microwave remote sensing of snow cover. Int J Remote Sens 24:5327–5344
Solomeshch AI (2005) The West Siberian lowland. In: Fraser LH, Keddy PA (eds) The world’s largest wetlands: ecology and conservation. Cambridge University Press, Cambridge, pp 11–62
Stolbovoi V, McCallum I (2002) CD-ROM “Land Resources of Russia”. International Institute for Applied Systems Analysis and the Russian Academy of Science. Laxenburg, Austria
Tedesco M (2007) Snowmelt detection over the Greenland ice sheet from SSM/I brightness temperature daily variations. Geophys Res Lett 34:L02504
Temimi M, Leconte R, Brissette F, Chaouch N (2007) Flood and soil wetness monitoring over the Mackenzie river basin using AMSR-E 37 GHz brightness temperature. J Hydrol 333:317–328
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150
Ulaby FT, Moore RK, Fung AK (1986) Microwave remote sensing: active and passive, vol III. From theory to applications. Artech House, Norwood
Wang L, Sharp MJ, Rivard B, Marshall S, Burgess D (2005) Melt season duration on Canadian Arctic ice caps, 2000–2004. Geophys Res Lett 32:L19502
White MA, Nemani RR (2006) Real-time monitoring and short-term forecasting of land surface phenology. Remote Sens Environ 104:43–49
Wismann V (2000) Monitoring of seasonal thawing in Siberia with ERS scatterometer data. IEEE Trans Geosci Remote Sens 38(4):1804–1809
Yang D, Ye B, Kane DL (2004) Streamflow changes over Siberian Yenisei river basin. J Hydrol 296:59–80
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Bartsch, A., Wagner, W., Kidd, R. (2010). Remote Sensing of Spring Snowmelt in Siberia. In: Balzter, H. (eds) Environmental Change in Siberia. Advances in Global Change Research, vol 40. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8641-9_9
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