Detection and validation of spatiotemporal snow cover variability in the Himalayas using Ku-band (13.5 GHz) SCATSAT-1 data

A Scatterometer is a microwave radar instrument designed specifically for ocean Applications. Although due to strong sensitivity to wetness in snow, it has been extensively used for the cryosphere applications such as extraction of snow parameters, With Scatterometers, the accuracy and complexities of snow detection algorithms are the major concerns as compared to optical data (multispectral) based algorithms since snow is more separable using visible wavelengths as compared to microwave wavelengths. But optical data are limited to cloud-free days and this is an important advantage of microwave data as compared to optical measurements where practically any cloud limits the exact characterization of the land surface state. 

Some glimpse of study

The present study evaluates the potential of Ku-band Scatterometer Satellite-1 (SCATSAT-1) for quantification of spatiotemporal variability in snow cover area (SCA) over Himalayas(Himachal Pradesh) India. The SCA has been measured using dual-polarized (HH and VV) backscattered SCATSAT-1 data. Two classification approaches, i.e., Linear Mixer Model (LMM) and Artificial Neural Network (ANN) model have been used for the present study. Both available backscatter coefficients sigma-naught σ0 and gamma-naught γ0 have been considered for the estimation of SCA. To compute the seasonal snow cover trends for winter (2016‒2017 and 2017‒2018), a post-classification comparison (PCC) based change detection approach has been demonstrated on the classified dataset (LMM and ANN). The SCA maps have been validated using reference snow cover maps generated from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor. The final change-category maps have effectively mapped the snow cover variations with accuracy in between 83.01% and 95.33%. The results indicate the suitability of SCATSAT-1 for estimating the magnitude of snow extent over the Himalayas.

Reference: S. Singh, R.K. Tiwari,. V. Sood, H. S. Gusain, Detection and validation of spatiotemporal snow cover variability in the Himalayas using Ku-band (13.5 GHz) SCATSAT-1 data, International Journal of Remote Sensing, Taylor and Francis, 2021.

Link for full study:

https://doi.org/10.1080/2150704X.2020.1825866

Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008–19

The Himalayas is an essential component of the cryosphere due to the large extent of snow or ice cover. The mapping and monitoring of snow cover variability over the Himalayas is the focus of many scientific studies due to the major source of water for Asian countries and equally important for climate change studies. This study describes the analysis of snow cover variability over North Indian Himalayas (NIH) covering Western Himalayas and Karakoram mountain ranges. The snow cover area (SCA) has been analyzed in three different climate zones such as the upper Himalayan zone (UHZ) (Ladakh and Karakoram range), middle Himalayan zone (MHZ) (Great Himalaya and Zanskar), and lower Himalayan zone (LHZ) (Pir Panjal and Shamshbari range) at various elevation levels as well as aspect levels during the past decade (2008–2019). The snow cover maps have been generated for NIH and its climate zones from Moderate Resolution Imaging Spectroradiometer (MODIS) data. 

Glimpse of Study

The global climate change is directly or indirectly impacting the regional climate of the Himalayas and thus, requires more attention to seasonal or inter-annual snow cover variations over the mountainous region. In the present work, the focus is on analyzing the seasonal snow cover variability at different elevations (LHZ, MHZ, and UHZ) and aspect levels (N, NE, SE, S, SW, W, NW) during the last decade (2008–2019). The NIH experienced global warming effects especially at lower and middle Himalayan zones which can be observed as shifting of snowmelt runoff and snow accumulation periods. The experimental outcomes suggest that shifting of this period are generally due to an increase in temperature over the Himalayan mountain range. The seasonal and annual trend analysis has shown that seasonal snow cover is varying across different elevations and geographical extent. The results computed in the present study have some limitations such as lack of incorporating precipitation data and in-situ or field observations. Therefore, the comparative analysis of different model outputs delivers important information regarding the impact of climate change over the snow cover area. This study also provides effective guidance in the prediction of natural hazard analysis and the protection of water resources.

For detailed Study:

Sood, V., Singh, S., Taloor, A.K., Prashar, S. and Kaur, R., 2020. Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008‒19. Applied Computing and Geosciences. https://doi.org/10.1016/j.acags.2020.100040

Evaluation of SCATSAT-1 data for snow cover area mapping over a part of Western Himalayas

Regular monitoring and mapping of the Snow Cover Area (SCA) is important to manage the natural resources and to assess the impact of climate change on SCA. But, over inaccessible Western Himalayas, the estimation of the SCA is one of the challenging tasks due to its complex and rugged topography. SCA was mapped so far with optical sensors. Generally, the optical sensor data is affected by the presence of the cloud cover and is more sensitive towards interference from environmental effects. Alternatively, several developments were made by various authors to map the SCA using active or passive microwave data all over the globe.  It is proven that scatterometer data has the potential to retrieve the snow cover information. But the mapping of SCA using active microwave satellite data is still in its initial phase due to lower accuracy as compared to optical sensors data. 

Due to the recent advancements in classification algorithms such as Sub-Pixel Classification (SPC) and Super-Resolution Mapping (SRM), there is a clear requirement of extensive exploration of different classification algorithms for SCA estimation especially over complex undulating Western Himalayas using SCATSAT‒1 data. The integration and evaluation of advanced classification algorithms with scatterometer data are some of the primary issues in the remote sensing field. Therefore, with an investigation on such critical issues, it may be possible to extend the applicability of SCATSAT‒1 data in different applications. It is also expected that such the analysis will improve the performance of a scatterometer in the precise mapping of SCA over rugged terrain surface.

Methodology to generate Snow Cover Maps 

This study is capable to evaluate the Indian Satellite SCATSAT-1 data for snow cover mapping. Three classifiers KMC, SVM, and LSM were used for the generation of binary snow cover maps from SCATSAT-1 backscattered values. For better accuracy of the maps super-resolution mapping technique in conjunction with NDSI data has also been used. The results of classifiers and SRM have been compared with NDSI images generated from Landsat‒8 and MODIS data on clear sky days, respectively.  This means that LSM-SRM on SCATSAT-1 is a good candidate for classifying the SCMs at Ku-band but it requires an additional dataset. Further, such outcomes will be beneficial in the study of the cryosphere, especially on cloudy days. LSM-SRM results show that regular snow cover mapping for the generation of a time series maps in high‒altitude Himalayas and other regions of extreme climate sensitivity, where cloud cover plays an important role during the winter season, maybe possible by fusion of scatterometer data with optical sensor data. Currently, the application range of SCATSAT-1 may be limited due to some difficulties such as a) incidence angle variations affect the backscatter values, and b) difficult to identify pure endmember properties due to low spatial resolution. Further studies to overcome the limitations in examining SCA from scatterometer data may be useful for trend analysis of early snowmelts and seasonal snow cover variations.

Sood et al. (2020) address the potential of SCATSAT‒1 data for snow cover mapping over a part of Western Indian Himalayas. Three classifiers KMC, SVM, and LSM were used for the generation of binary snow cover maps from SCATSAT-1 backscattered values. For better accuracy of the maps super-resolution mapping technique in conjunction with NDSI data has also been used.

  

Reference: Sood V., Gusain H.S., Gupta S., Singh S., Kaur S. 2020. Evaluation of SCATSAT-1 data for snow cover area mapping over a part of Western Himalayas, Advances in Space Research, Elsevier.

Link for full study: https://doi.org/10.1016/j.asr.2020.08.017

Nearest-Neighbor Diffusion-based pan-sharpening using multispectral MODIS and AWiFS

Remote sensing plays a significant role in the monitoring of the undulating the Himalayas. With continuous monitoring, the preservation of natural resources and mitigation of natural hazards is possible. Currently, satellite sensors are not capable enough to deliver the earth's surface image at a very high temporal, spectral, and spatial resolution, simultaneously. Therefore, it is essential to perform the pan-sharpening of spatially high-resolution (HR) panchromatic (PAN) spectral band with low-resolution (LR) multispectral (MS) imagery which must be acquired on the same temporal date from multiple sensors. On the other hand, due to the rugged topography of the Himalayas, topographic effects are generally induced in the form of shadow and affect the spatial information or spectral information. 

Process of Pan-sharpening (Fusion)

For regional or global scale studies, the LR satellite dataset is more preferable and can be merged with the HR dataset with nearest-neighbor diffusion (NND) -based pan-sharpening algorithm. With visual interpretation, it is apparent that NND pan-sharpening with topographic correction offers more reliable information by effectively removing the shadow effects as compared with NND pan-sharpening without topographic correction.

Singh et al. (2020) address the topographic correction is required to be implemented with NND-based pan-sharpening and other classification models. For experimental purposes, AWiFS as HR-PAN data and MODIS as LR-MS data have been used.

  

Reference: Singh, S., Sood, V., Prashar, S. and Kaur, R., 2020. Response of topographic control on nearest-neighbor diffusion-based pan-sharpening using multispectral MODIS and AWiFS satellite dataset. Arabian Journal of Geosciences, 13(14), pp.1-9. 

Link for full study: https://rdcu.be/b5DMK

Potential Applications of SCATSAT-1 Satellite Sensor: A systematic review

The Ku-band (13.5 GHz) based scatterometer is the main sensor onboard Scatterometer Satellite (SCATSAT-1) launched on 26th September 2016 by Indian Space Research Organization (ISRO). The SCATSAT-1 satellite sensor provides daily updates on the conditions of atmospheric, oceanographic, agriculture and cryospheric parameters. Moreover, it delivers data products (Level 1‒4) in form of different parameters (Sigma-naught σ0, Gamma-naught γ0, brightness temperature BT, wind vectors and velocity) at two different polarization modes (HH and VV). 

SCATSAT-1 Products & their Applications 

Since launch, several studies have been carried out to explore the potential of SCATSAT-1 satellite sensor for remote observation of the ocean as well as the land surface at the global level. Besides the conventional applications in weather and oceanic domains which are based on wind vector data, emerging applications over land use and land cover are also introduce. 

Singh et al. (2020) address the current status of SCATSAT-1 applications in different scientific domains such as oceanographic, cryospheric, agriculture and land hydrology. It is expected that such an extensive exploration of the applications of SCATSAT-1 satellite sensor will provide important insights for future utilization of scatterometer data.

  

Reference: S. Singh, R. K. Tiwari, H. S. Gusain and V. Sood, "Potential Applications of SCATSAT-1 Satellite Sensor: A systematic review," in IEEE Sensors Journal. 

Doi: https://doi.org/10.1109/JSEN.2020.3002720

Link for detailed study