SECTION HYPERLINKS
BACKGROUND
The Cryosphere constitutes everything on the earth's surface that is frozen, be it glaciers, sea ice, polar caps or snow. While the inhospitable temperatures deter economic activity in these regions, they still are of vital importance to mankind as they maintain the planet's climate systems by reflecting away solar radiation. Reduction in ice composition in the polar regions is often treated as a leading indicator of widespread Global Warming. Therefore, monitoring change in the Cryosphere is of much scientific interest to researchers working in the Earth Observation sector.
The topic of this post is to monitor Snow Cover in Himachal Pradesh, India from 2018-2021. Himachal Pradesh is a mountainous State in the country which receives precipitation in the form of Snow at higher altitudes which are present right from its North-East to its South-East.
What drove me to this topic was a report published last month (August 2021) that Snow cover in Himachal Pradesh reduced by 18% between October 2020 and May 2021 - the researchers at HIMCOSTE and SAC Ahmedabad had analyzed Satellite Imagery acquired by AWIFS (Advanced Wide Field Sensor jointly developed by ISRO and USGS) onboard ISRO's ResourceSat-2 satellite. For my study, I have analysed Satellite Imagery captured by Ocean and Land Color Instrument (OLCI) onboard ESA Copernicus' Sentinel-3 satellite.
Much thanks to RUS Copernicus for the training material
Snowmelt is the primary source of water for some of the major rivers of India. Himachal Pradesh's Snow, in particular, is the source of water for Beas, Chenab, Ravi and Sutlej and Yamuna river basins. The linkage is straightforward: Less Snowfall → Less Snow Cover → Less Snowmelt → Less Water Inflow for these vital livelihood-supporting rivers.
I will perform analysis on two sets of Sentinel-3 Satellite Imagery datasets-
a) 1 Imagery dataset each from four days (Winter-Summer) of 2021 and
b) 1 Imagery dataset each from a day (Summer) in 2018-2021
For the former, I have acquired the dataset from February (end of Winter) and finished in June (prime Summer). By doing so, I'll be able to observe the extent of Snow Melt vividly.
For the latter, I have acquired the dataset from a prime Summer day (May or June). Here, the objective will be to see how the extent of Snow Cover is changing on a year-on-year basis.
Depicting the acquisition timeline in a calendar-view below-
METHODOLOGY
For starters, this is how the Sentinel-3 OLCI Multispectral Satellite Imagery looks like in RGB mode-
The methodology that I have used to derive the extent of Snow Cover using Remotely-sensed data is called Normalized Difference Snow Index (NDSI). Clouds can be easily differentiated from Snow in Short Wave Infrared (SWIR) wavelength of the Electromagnetic Spectrum. However, Sentinel-3 Ocean & Land Color Instrument doesn't acquire data in SWIR - therefore, I have used an NDSI adaptation suited for OLCI developed by Kokhanovsky et. al, (2019) instead (it utilizes the Near Infrared wavelength).
Depicted in Figure 4 below is the processing chain to convert input Satellite Imagery (Read) to output Snow Cover Map (Write) on SNAP software. The adapted NDSI formula is plugged in the 'Band Maths' function of the processing chain.
Sharing a short description of the other processing steps below-
Rayleigh Correction - OLCI in Sentinel-3 captures Multispectral information (21 spectral bands) in Top of Atmosphere (TOA) mode and is Passive Sensor i.e. the source of energy is not onboard the satellite - it is Sunlight. I believe you may have heard the term 'Rayleigh Scattering' before - fine particles in the atmosphere scattering sunlight particularly in the shorter, blue wavelength which is what causes the sky to appear blue. We have to subtract this effect from the OLCI dataset in order to get an accurate representation of the solar reflectances at a Surface-level i.e. remove the signal distortion due to scattering. Converting TOA to rBRR (Bottom of Rayleigh Reflectances) is the function of this Rayleigh Correction algorithm.
Subset - Subsetting an Imagery Dataset can involve a) clipping the Geographic Extent of the Imagery to the Study Area and/or b) removing those Spectral Bands of information which are not useful for the study and/or c) removing the Metadata included in the Imagery package. Performing a subset helps to reduce the size of the Imagery dataset considerably, making the processing quicker. For this Snow Mapping workflow, I've used a) Extent Subset.
Reproject - The purpose of applying Reprojection is to set a defined Coordinate Reference System (CRS) to the output. To understand Reprojection, one must be aware about Projection which involves the transfer of information captured from a 3-dimensional surface (LWH) onto the 2-dimensions of a Map (XY). The question is where does this XY geometry belong to? By default, it belongs to nowhere - just a coordinate in an arbitrary space. The purpose of Reprojection is therefore to make the XY coordinates correspond to real locations on Earth i.e. to set an Earth Reference System.
OUTPUT AND INTERPRETATION
Upon processing a Satellite Imagery dataset on SNAP using the adapted NDSI for OLCI, the output generated is depicted in Figure 5 below-
Depicted in Figure 6 below are the Snow Cover outputs for Himachal Pradesh across the four Imagery acquisitions from 2021, stacked side-by-side.
A marked reduction in Snow Cover can be clearly observed in the fourth image from June 2021 compared to the previous months. Himachal Pradesh has had un-seasonal snowfall in April 2021 (onset of Summer) - the third Snow Cover map is from this month and evidently, it shows a marked increase from the previous months. An aspect to understand is that this type of Snowfall is less dense and is prone to quicker melting causing Avalanches.
Depicted in Figure 7 below are the Snow Cover outputs for Himachal Pradesh across the four Imagery acquisitions from a summer day in each of 2018-2021, stacked side-by-side.
It is evident that the extent of Snow Cover in the fourth image (from a day in June 2021) is much less than the the second and third image from the preceding two years respectively (from a day in May 2019 & May 2020 respectively). The first image (from a day in May 2018) appears to have the least Snow Cover of them all - which is backed by the findings of a comprehensive scientific study.
In Figure 8 below, I have combined all the processed outputs from 2021 (Figure 6) into a single Map-
Similarly, in Figure 9 below, I have combined all the processed outputs from 2018-21 (Figure 7) into a single Map-
While I have visually interpreted the output thus far, it is also possible to do quantitative analysis - from within SNAP Software itself. Figure 10 below depicts the Snow Extent in terms of Area for the four acquisitions from 2021-
A ~36% decrease in Snow Cover has been observed between February 2021 and June 2021. To draw a meaningful interpretation isn't straightforward as February is a Winter month while May is a Summer month i.e. it is normal for a portion of the Snow Cover to melt away. Just how much is normal is not something that I'm familiar with. However, if you recollect the finding of the scientific study I had referred to at the beginning of this post - an 18% decrease in Snow Cover was observed between October 2020 and May 2021 which was deemed to be unusual and was attributed to Climate Change - so I presume the normal Melt percentage is well below 18%. Hence, the 36% decrease in Snow Cover observed from my study is certainly alarming!
Interestingly, I would definitely like to ask the researchers, should an opportunity arise, why they chose October 2020 as the base month to compare May 2021 Snow Cover output to. October marks the end of monsoon - the Snow Cover is expected to be at its lowest during this time in a normal scenario and comparing it to peak Summer output where again the Snow Cover is less due to extensive melting doesn't seem like a natural comparison to me. While I would still give the researchers the benefit of doubt as they are an authority on this matter, nonetheless, as informed citizens we must not take the Research Findings, especially those which appear in news portals, at face value - the headline and content usually focuses on an eye-popping finding but the methodology and rationale behind the selected parameters are generally hidden-from-view and must be studied before being absorbed.
That being said, as I was unfamiliar about the ideal Melt percentage was the precise reason why I chose to compare Snow Coverage year-on-year during roughly the same time period (Peak Summer). The output would complement my 2021 month-on-month output - it would validate whether the Snow Coverage is reducing which would point in the direction of Climate Change.
Figure 11 below depicts the Snow Extent in terms of Area for the four acquisitions from 2018-2021-
A 42% decrease in Snow Cover can be observed during the comparison of June 2021 output to that of May 2019 and a 34% decrease can be observed during the comparison of June 2021 output to that of May 2020. These observations indicate a severe impact from Climate Change and, in a way, validates the 2021 month-on-month results discussed prior.
That being said, a 25% increase in Snow Cover was also observed during the comparison of June 2021 output to that of May 2018 which is a contrast to the Climate Change narrative - some may argue otherwise, and rightfully so, as Climate Change doesn't go in one direction all the time, rather it represents an increase in volatility of weather conditions. Nonetheless, two potential reasons that I could think of are-
a) a small section of Himachal Pradesh is not present in the Satellite Imagery from 2018 - as can be observed on the right and on the bottom of Figure 12. This is because I was late to realize that the Satellite Image I had selected did not cover the study area completely. Himachal Pradesh receives precipitation in the form of Snow at higher altitudes so I am certain that the section missing on the right distorts the exact Snow Cover, however, I don't think it would make much of an impact - presumably a single digit % increase in 2018 Snow Cover.
b) More importantly, May 2018 (9096 sq. km. of Snow Cover) could very well be an anomaly, a very warm month. There is a reason to believe so - from Figure 10 & 11, you will find that in all the remaining 7 outputs from different time periods, the Snow Coverage is above 10,000 sq. km for the state of Himachal Pradesh. News reports validate this further - this region was under the spell of a record Heatwave during that time.
PARTING THOUGHTS
Himachal Pradesh isn't alone. Anthropogenic Global Warming-induced Climate Change is impacting the Cryosphere at various locations across the globe - the rates of melting Snow have increased drastically. In the Sierra Nevada mountain range in USA, I've observed that the Snow Cover in May 2021 was 90% lower than that of February 2021!
Remote Sensing is highly valuable for Earth Observation Workflows such as this. It helps researchers to monitor phenomena at a wide scale, with frequent observations and at sufficiently-high resolutions enabling them to suggest proactive and reactive measures to governments and institutions in a bid to combat and counter Nature's fury.
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