Remote Sensing of Snow

Preparing the slides for my hydrological class, and aiming to overview (just a little indeed) measurements methods, I discovered prof Hongjie Xie page, from which I withdraw some information. This was for me the excuse to copy and download some reference on passive microwaves and optical sensor dedicated to snow. Below it is a report of the page (with the bibliography a little edited to cope with available papers).

"Since the middle of the 1960’s, a number of satellite-derived snow products have been available, with a few available in near-real time through Internet (Bitner et al, 2002).
Space-board passive microwave radiometer, such as SMMR (Scanning Multichannel Microwave Radiometer), SSM/I (Special Sensor Microwave/Imager), and AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System), can penetrate clouds to detect microwave energy emitted by snow and ice and provide information on SWE or snow depth and thus estimating runoff (Pulliainen, 2006; Wulder et al., 2007). Since the 1970s, SWE retrieval from space-borne passive microwave has been investigated.  Space-borne passive microwave data are well suited to snow cover monitoring because of characteristics such as all weather imaging, a wide swath width with frequent overpass times, and a long available time series (Derksen et al., 2004). But the coarse spatial resolution (25 km of AMSR-E is the best available now) hinders their application in operational hydrological modeling and snow-caused disasters monitoring (Foster et al., 2003; Dressler, et al. 2006; Pulliainen，2006). Optical sensors such as AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectraradiometer), SPOT and Landsat have been well developed to produce snow cover maps with high spatial resolution (Salonmonson & Appel, 2004; Brown et al., 2007; Dozier&Painter, 2004). But due to the inherent limitation, optical sensors cannot see the earth surface when cloud is present. High cloud blockage becomes the biggest problem in applying snow products from optical sensor (Klein & Barnett, 2003; Zhou et al., 2005; Tekeli et al., 2005; Ault et al., 2006; Liang et al. 2008 a, b; Wang et al., 2008a, b; Wang and Xie 2009)"

Bibliography

Ault T.W.,⁎, Czajkowski K.P., Benko T., Coss J., Struble J., Spongberg A., Templin M.,  Gross C., Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region, Remote Sensing of Environment 105 (2006) 341–353

Bitner D., T. Carroll, D. Cline and P. Romanov, 2002: An assessment of the differences between 
three satellite snow cover mapping techniques, Hydrological Processes 16:3723–3733.

Brown R., Derksen C., Wang L, Assessment of spring snow cover duration variability over northern Canada from satellite datasets,  Remote Sensing of Environment 111 (2007) 367–381

C. Derksen C.,Brown, R., Walker A., Merging Conventional (1915–92) and Passive Microwave (1978–2002) Estimates of Snow Extent and Water Equivalent over Central North America, Journal of Hydromet, 5,  2004, 850-861

Dozier J,  Painter T.H, Multispectral and hyperspectral remote sensing of alpine snow, Annu. Rev. Earth Planet. Sci. 2004. 32:465–94 doi: 10.1146/annurev.earth.32.101802.120404

Dressler,K. A., Leavesley,G. H.,  Bales R. C. and Fassnacht S. R., Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model, Hydrol. Process. 20, 673–688 (2006)

Foster, J.L.,  Sunb C., Walkerd J.P.,  Kelly R., Changa A., Dong J.,  Powell U, Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sensing of Environment 94 (2005) 187–203

Klein A, Barnett A.C., Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000–2001 snow year, Remote Sensing of Environment 86 (2003) 162–176

Liang T., Zhang X., Xie X, Wu C., Feng Q, Huang X, Chen Q., Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements, Remote Sensing of Environment xxx (2008) xxx-xxx

Pulliainen J., Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations, Remote Sensing of Environment, Volume 101, Issue 2, 30 March 2006, Pages 257-269, ISSN 0034-4257, 10.1016/j.rse.2006.01.002.

Salomonson V.V, Appel, I., Estimating fractional snow cover from MODIS using the normalized difference snow index, Remote Sensing of Environment 89 (2004) 351 – 360

Tekelia A.E., Akyurek Z., Sorman A., Sensoy A, Sorman U., Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey, Remote Sensing of Environment 97 (2005) 216 – 230

Wang X., Xie H., Liang T., and  Huang X., Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China, Hydrol. Process. 23, 419–429 (2009) DOI: 10.1002/hyp.7151

Wulder, M.A., T. A. Nelson,  Derksen C, Seemann D,  Snow cover variability across central Canada (1978–2002) derived from satellite passive microwave data,  Climatic Change (2007) 82:113–130 DOI 10.1007/s10584-006-9148-9

Zhou X, Xieb H.,  Hendrickx J.M.H., Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements, Remote Sensing of Environment 94 (2005) 214–231

Experimental Hydrology Wiki

Today, while looking for snow pillow images I discovered this Experimental Hydrology wiki  with some interesting information. About the same subject, obviously the CUAHSI site is also a source of many resources: but since the many documents present there, is sometimes more difficult to find the stuff you are looking for.

This is what the wiki authors say:

"It will help us to learn about, recommend, question and discuss new / established / basic / advanced methods of experimental hydrology.

It will help us to avoid reinventing the wheel each time we start out measuring something we haven't measured before.

It will help us not to make the same mistakes others have made before us.

It will help us to share new ideas and concepts.

It will help us to find the methodology and the equipment suitable for our investigation.

All experimental hydrologists are welcome to contribute with their knowledge and experience!"

A paper in Nature on Scientific Software

The news was brought to me by Martin Davis who had from Stefan Steiner.  The paper is:  The case for open computer programs and was published in Nature.

Here it is what Martin says:

"The paper raises the argument for open source software to a higher plane, that of being a necessary component of scientific proof. It points out that the increasing use of computational science as a basis for scientific discovery implies that open source must become a standard requirement for documentation. Apparently some journals such as Science already require source code to be supplied along with submissions of articles. Amongst other advantages, access to source code is an essential element of peer review.
An interesting example they mention is the infamous HadCRUT and CRUTEM3 meteorological datasets. One of the (few) salient criticisms levelled at this information during Climategate was the inability to reproduce the results by re-running the software. (Mind you, the software was probably a pile of crufty old Fortran programs mashed up by Perl scripts, so maybe it's just as well)"

This clearly reflect what I already wrote in some of my posts:

http://abouthydrology.blogspot.it/2011/03/going-beyond-present-stato-of-art-in.html
http://abouthydrology.blogspot.it/2012/02/reproducible-research-and-papers.html

Utilizing Online Resources for Hydrological Research

I got from LinkedIn this website where Mamhud illustrates several on-line resources by means of videos.

The blog and the site itself are full of interesting information and useful for any hydrologist.

Hydro-geological hazard in Italy as Mapped by the local authorities

A friend gave pointed out to me this morning this map of hydrogeological hazards in Italy, as produced by the local authorities and collected by the Italian Ministry of Italy.  The full map at higher resolution (1.4 Mb) can be retrieved here (I think it is public and can be found somewhere in the Environmental Ministry of Italy). "Carta delle aree ad alta criticita' idrogeologica" means: maps of the area with the highest potential hazards. Red are landslides, blue are flooding, green snow avalanches hazards.

Well, some parts of Italy (as my Province of Trento) are very red and full of hazard. Some others (same hydrological and geological conditions, as the Province of Bolzano and Province of Belluno) are white: almost no hazard at all. (I tend to support the idea, since I was involved in part of its construction, that the hazard map of Trentino was produced with state of art techniques. But I also know that officiers of the other Provinces are also rigorous people. Then ?). The case of Emilia Romagna is also perfect example of this partition.  The Apennines in Emilia (under the Authority of River Po ?) are really red.  The Apennines in Romagna (under the river Authority of Reno River ?) are almost white.
Why these differences ? Different techniques of mapping ? Different "political choices" ?  Different stage of production of the maps (did all really made it) ?

Who knows can give me a clue ?

The microscopic^1 thermodynamics of snow

Listening to Michi Lehning talk, my curiosity was raised by a couple of passages. One of these was the use of mean field theories for describing the snow metamorphism.  Therefore I asked him for a little of literature on the subject. Here below his suggested readings:

I.M. Lifshitz and V.V. Slyozov. The kinetics of precipitation from supersaturated solid solutions. 

J. Phys. Chem. Solids, 19(1-2):35–50, 1961. 

[C. Wagner. Theorie der Alterung von Niederschlagen durch umlÅNosen (Ostwald-Reifung). Z. 

Elektrochem., 65(7-8):581–591, 1961. 

Probably easier in language and style: 

C.W.J. Beenakker and J. Ross. Theory of Ostwald ripening for open systems. J. Chem. Phys., 

83:4710–4714, 1985. 

L. Ratke and C. Beckermann. Concurrent growth and coarsening of spheres. Acta mater, 

49:4041–4054, 2001. 

S.P. Marsh and M.E. Glicksman. Kinetics of phase coarsening in dense systems. Acta Materialia, 

44(9):3761–3771, 1996. 

and with respect to snow: 

L. Legagneux and F. Domine. A mean field model of the decrease of the specific surface area of dry snow during isothermal metamorphism. J. Geophys. Res. Earth, 110(F4), NOV 18 2005.

Looking at the citations chain one can more or less recover most of the literature on the subject. You can also give a look to this more recent post.

^1 microscopic thermodynamics is, obviously, a sort of oxymoron. However, these thermodynamics work at a scale which is much smaller than what we want usually to treat (at field or catchment scale).

Modelling Environments for Biophysical Modelling in Hydrology and Agriculture

Modelling Environments for Biophysical Modelling in Hydrology and Agriculture: Object Modeling System 3 (OMS3) and Biophysical Model Applications (BioMA)

July 9-13, 2012

Joint Research Centre, European Commission, Ispra, Italy

The need for integrated analysis, and the multiplicity of possible goals in analysis which require biophysical modelling, necessitates more than ever the capability of composing modelling solutions of known quality which are transparent to users and consist of reusable model components.

There is a variety of modelling platforms and the number of model components and tools is growing; however, there is little interaction among developers of modelling platforms and the potential community of researchers who may benefit from new techniques and technologies in their work. This workshop is meant to provide this opportunity by illustrating the concepts at the base of two modelling frameworks, and by having a hands-on approach to both. The two modelling environments which will be presented are the Object Modeling System (OMS3) and the Biophysical Model Application (BioMA), briefly described below. Both are operational and allow running complex analyses in the biophysical domains of hydrology and agricultural production. The closing session will include a critical discussion on the concepts and realizations experienced during the week.

More Information on the website of the course.  OMS3 as you can verify from many post:

• uDig got a spatial toolbox
• Going Beyond the Present Stato-of-Art in hydrological modeling
• The JGrass-NewAge system for forecasting and managing the hydrological budgets at the basin scale: the models of flow generation, propagation, and aggregation

is at the core of my modeling strategy. I believe is one of the best way to have a modern approach to do models. So the school will be the occasion to learn about it (for who is already a good programmer)