Normalized Difference Snow Index Research Articles

A snow cover mapping algorithm based on a multitemporal dataset for GK-2A imagery

ABSTRACT Snow plays a crucial role in the climate. In particular, snow controls the surface temperature and stabilizes the energy budget due to its spectral characteristics. However, snow can be affected by climate change. If the snow cover extent (SCE) diminishes due to global warming, the rate of introducing radiance to the surface increases because non-snow areas can absorb more solar radiance. Therefore, it is highly important to accurately detect snow-covered areas. In this paper, we develop an algorithm for deriving a daily snow cover map of East Asia using Geostationary-Korean Multi-Purpose SATellite-2A (GEO-KOMPSAT-2A, GK-2A) imagery. After processes for cloud masking and misclassification removal are conducted, a threshold with consideration for the characteristics of a geostationary satellite is applied to the normalized difference snow index (NDSI), and a daily snow cover map is generated utilizing a stacking process. In the case of the cloud mask, the angle-time variation and displacement by clouds proposed in this study were used to enhance the accuracy of cloud detection. For the quantitative validation, the F1 score was 0.89 for Landsat-8 and 0.79 for the interactive multisensor snow and ice mapping system (IMS). In addition, when the snow cover extent calculated from the IMS was compared, the correlation reached 0.91 from December 2020 to January 2021.

STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China

Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (NX), Northeast China (NC), and the Qinghai–Tibet Plateau (QTP). Spatiotemporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, the integrated normalized difference snow index (NDSI) maps containing additional details on snow cover extent are still extremely scarce. In this study, a recent 20-year stretch seamless Terra–Aqua MODIS NDSI collection in China is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the cloud-free STAR NDSI collection is superior to the two baseline datasets. The omission error decreased by 10 % in NX compared to the snow cover extent product, and the average correlation coefficient increased by 0.11 compared to the global cloud-gap-filled MODIS NDSI product. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues in China. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).

Impact of illumination gradients on the raw, atmospherically and topographically corrected snow and vegetation areas of Jhelum basin, Western Himalayas

Mountainous terrains severely affect the sun-target-sensor geometry due to surface dispersion of solar radiation, resulting to variation in the observed radiance. The effect of topographic shadow on raw, atmospherically and topographically processed Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) values was investigated for Jhelum basin, Kashmir Himalaya. The results indicate that NDSI and NDVI derived from raw images show less area of snow and vegetation, due to underestimation of corresponding pixels in the shadow regions, in comparison to atmospherically and topographically processed datasets. Among the three datasets, raw images exhibited low values of minimum and maximum NDSI and NDVI for both shadow and sunlit slopes. All the three datasets show significantly higher values of minimum and maximum NDSI, NDVI and surface temperature in the sunlit slopes, relative to shadow slopes. This research demonstrates that topographic shadow has significant impact on the raw, DOS atmospheric corrected and C topographic processed Landsat-8 Operational Land Imager (OLI) datasets as well as on Thermal Infrared Sensor (TIRS) sensor.

Perception of transhumant herders on climate change and their adaptation strategies

Climate change is becoming one of the greatest challenges of our times questioning the survival of humanity and the coexistence of biodiversity. Its impact is realized globally in aspects of climate-induced disasters, water stresses, rangeland, agricultural, and food production. This case study confers the extent and impact of climate change on transhumant herders along with their coping strategies in Lho and Sirdibas of Chumnurbi Rural Municipality, Gorkha. Focus group discussion, key informant interview, and questionnaire survey were carried out in two wards covering herder's perception towards various climatic variability, impacts on transhumance system, and adaptation strategies undertaken for resilience. Data were analyzed using descriptive, inferential statistics and GIS such as percentage, frequency, weighted mean, index of relative ranking, Chi-square test, Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), etc. were presented in figures and charts. This study showed that temperature is rising and precipitation is falling in the rural mountainous areas along with a decrease in snow cover and an increase in vegetation cover over a period of time. Seasonal movement of livestock is mainly guided by the search of forage resources and for adjusting the temperature. The impacts such as the decrease in pasture resources, increase in drought, decrease in agricultural production, etc. were perceived by the herders. Adaptation strategies such as the seasonal movement of livestock, diversification of income sources, and grazing resources were most noticed. Most of them were autonomous based on indigenous knowledge and innovation. The study recommends that the perceived changes and possible impacts of climate change to the transhumance systems need to be monitored scientifically over time. Further, more programs and policies should be developed for the resilience of the community.

STUDY ON SNOW COVER CHANGE BASED ON THE FUSION OF SENTINEL-2 AND MODIS IMAGES

Abstract. Snow cover is one of the features that change rapidly on the surface, and remote sensing data with high spatial and temporal resolution is an important means to monitor the dynamic changes of snow. However, due to the limitation of satellite conditions, it is difficult to acquire remote sensing images with both high temporal and spatial resolution at the same time. The classical Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) breaks through the limitations of a single sensor and effectively brings into play the complementary advantages of different observation platforms. In this paper, we take the Babao River Basin within the Qinghai-Tibet Plateau as the study area to investigate the effect of fusion of Sentinel-2 and MODIS (Moderate-resolution Imaging Spectroradiometer) based on the ESTARFM and analyze the fusion-supplemented snow accumulation period data. The results show that the fusion effect of Sentinel-2 and MODIS is good in terms of visual and multiple quantitative indicators. The snow accumulation area of the fused image is also close to the real image. By fusing to supplement the missing Sentinel-2 data, a variation map of snow NDSI (Normalized Difference Snow Index) in the study area was obtained. It was found that the snowfall process in the watershed appeared first in the surrounding uplands and then in the flat areas, and the maximum rate of snow cover area change was 116.17 km2. Therefore, the spatiotemporal data fusion of Sentinel-2 and MODIS based on ESTARFM algorithm can provide reliable satellite remote sensing data with a higher spatiotemporal resolution for snow accumulation change monitoring.

A Geospatial Analysis of Shishper Glacier Surge-Upper Hunza Gilgit Baltistan

Glaciers can be an important indicator of climate change. In Pakistan, glaciers are found in the Northern Part of the country. In this study, we have discussed the occurrence of surge velocity, lake formation, and outburst of Shisper Glacier, Hunza, Gilgit, and Baltistan. To conduct this study satellite remote sensing techniques have been used. For this purpose, Sentinel-2 and Landsat 8 have been downloaded for 2015-2019. While to examine the elevation difference ASTER 2000 and SRTM 2014 Digital Elevation Model (DEM) have been downloaded, along with this other remote sensing analyses were applied including temporal change, land cover, and morphometric analysis including indices Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI). It has been analyzed that Ice-Dammed Lake formed in November 2018, which is the greatest threat to the study area as it can cause glacial lake outbursts and the volume of the lake is showing a continuous increase from 2018- to 2020. The land cover change indicated that snow area has increased about 35% from 2015 to 2018 along with an increase in debris glacier by about 20%. Whereas, a decrease in vegetation was observed up to 2% in 3 years. This study concludes that snow and clear glacier are enlarged over time. Taking into account our research, our suggestion is obligatory stride should be taken to keep away from the disastrous occurrence in the Shisper glacier.

Mapping Three Decades of Changes in the Tropical Andean Glaciers Using Landsat Data Processed in the Earth Engine

The fast retreat of the tropical Andean glaciers (TAGs) is considered an important indicator of climate change impact on the tropics, since the TAGs provide resources to highly vulnerable mountain populations. This study aims to reconstruct the glacier coverage of the TAGs, using Landsat time-series images from 1985 to 2020, by digitally processing and classifying satellite images in the Google Earth Engine platform. We used annual reductions of the Normalized Difference Snow Index (NDSI) and spectral bands to capture the pixels with minimum snow cover. We also implemented temporal and spatial filters to have comparable maps at a multitemporal level and reduce noise and temporal inconsistencies. The results of the multitemporal analysis of this study confirm the recent and dramatic recession of the TAGs in the last three decades, in base to physical and statistical significance. The TAGs reduced from 2429.38 km2 to 1409.11 km2 between 1990 and 2020, representing a loss of 42% of the total glacier area. In addition, the time-series analysis showed more significant losses at altitudes below 5000 masl, and differentiated changes by slope, latitude, and longitude. We found a more significant percentage loss of glacier areas in countries with less coverage. The multiannual validation showed accuracy values of 92.81%, 96.32%, 90.32%, 97.56%, and 88.54% for the metrics F1 score, accuracy, kappa, precision, and recall, respectively. The results are an essential contribution to understanding the TAGs and guiding policies to mitigate climate change and the potential negative impact of freshwater shortage on the inhabitants and food production in the Andean region.

Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

The present study was undertaken in the Chenab basin, western Himalayas to explore the effect of shadow on atmospherically and topographically corrected normalized difference snow index (NDSI) values. The research was conducted using the Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) datasets of two time periods i.e., February 2015 and February 2018. Our results suggested that total snow cover area (SCA) computed using NDSI from raw satellite images, under estimated SCA (viz. 10,938.66 and 7739.97 Km2); relative to atmospherically corrected (AC) (14,819.01 and 11,199.7 Km2) and topographically corrected (TC) (14,882.07 and 11,188.18 Km2) images during both the time periods (i.e., 2015 and 2018, respectively). This is mainly due to the non-identification of snow pixels in the shadow regions of raw imageries. Maximum SCA lies in the higher NDSI range in case of AC and TC images, unlike raw image where maximum SCA lies in lower NDSI range for both sunlit and shadow test sites. In the sunlit and shadow terrains, minimum NDSI values were found to be higher for TC images as compared to AC images, except few observations which exhibited similar values for both AC and TC images. In case of maximum NDSI values, most of the observations exhibited similar values for both AC and TC images, except few observations which depicted higher maximum NDSI value in AC images as compared to TC images in both the terrains. This observation ascertains the narrow range of NDSI values for TC images as compared to AC images, which could be attributed to reduced background reflection and low atmospheric scattering in the TC images. Comparison among shadow and sunlit snow terrains, reveals low values of minimum and maximum NDSI in the shadow site, which is ascertained by significant (p < 0.001) t-values of means difference between shadow and sunlit terrains. Moreover, snow surface temperature (SST) values computed for both the time periods, reveal low SST values for shadow sites (t-values −5.1 and − 10.57 for minimum SST, and − 9.1 and − 12.61 for maximum SST for the years 2015 and 2018, respectively) as compared to sunlit sites, and this observation validates that minimum amount of solar radiation reaches the shadow sites. Thus, it is concluded that AC and TC are helpful for delineation of snow pixels under shadow, but shadow has a significant impact on the spectral reflectance of snow, even in the AC and TC images. The influence of shadow should be taken into account for accurate estimation of physical properties and broadband albedo of the snow.

Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities

Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the sensitivity of the Synthetic Aperture Radar (SAR) backscatter to vegetation growth and soil moisture in an arctic environment. A combined approach using time series data from active remote sensing sensors such as SAR and passive optical sensors is a known technique in snow monitoring, while there is little knowledge of the radar C-band’s response pattern to vegetation dynamics in the arctic. First, we created multi-sensor masks using the HV backscatter coefficients from Sentinel-1 and the Normalized Difference Snow Index (NDSI) time series from Sentinel-2, monitoring the snow dynamics in Adventdalen (Svalbard) for the season from 2017 to 2018. Second, radar sensitivity analysis was performed using the HV polarized channel responses to vegetation growth and soil moisture dynamics. (1) Our results showed that the C-band radar data are capable of monitoring the seasonal variability in timing of snow melting in Adventdalen, revealing an earlier start by approximately 20 days in 2018 compared to 2017. (2) From the sensitivity analyses, the HV channel showed a major response to the vegetation component in areas with drier graminoid dominated vegetation without water-saturated soil (R = 0.69). However, the temperature was strongly correlated with the HV channel (R = 0.74) during the years with delayed snow melting. Areas of frozen tundra with drier vegetation dominated by graminoids had delayed soil thawing processes and therefore this may limit the ability of the radar to follow the vegetation growth pattern and soil moisture.

Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions

Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product is one of the prevailing datasets for global snow monitoring, but cloud obscuration leads to the discontinuity of ground coverage information in spatial and temporal. To solve this problem, a novel spatial-temporal missing information reconstruction model based on U-Net with partial convolutions (PU-Net) is proposed to recover the cloud gaps in the MODIS Normalized Difference Snow Index (NDSI) products. Taking the Yellow River Source Region as a study case, in which the snow cover is characterized by shallow, fast-changing and complex heterogeneity, the MODIS NDSI product in the 2018–2019 snow season is reconstructed, and the reconstruction accuracy is validated with simulated cloud mask and in situ snow depth (SD) observations. The results show that under the simulated cloud mask scenario, the mean absolute error (MAE) of the reconstructed missing pixels is from 4.22% to 18.81% under different scenarios of the mean NDSI of the patch and the mask ratio of the applied mask, and the coefficient of determination (R2) ranges from 0.76 to 0.94. The validation based on in situ SD observations at 10 sites shows good consistency, the overall accuracy is increased by 25.66% to 49.25% compared with the Aqua-Terra combined MODIS NDSI product, and its value exceeds 90% at 60% of observation stations.

Mapping snow cover in forests using optical remote sensing, machine learning and time-lapse photography

The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1–7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground-truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8–14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales.

Detection of Snow Cover from Historical and Recent AVHHR Data—A Thematic TIMELINE Processor

Global snow cover forms the largest and most transient part of the cryosphere in terms of area. On the local and regional scale, small changes can have drastic effects such as floods and droughts, and on the global scale is the planetary albedo. Daily imagery of snow cover forms the basis of long-term observation and analysis, and only optical sensors offer the necessary spatial and temporal resolution to address decadal developments and the impact of climate change on snow availability. The MODIS sensors have been providing this daily information since 2000; before that, only the Advanced Very High-Resolution Radiometer (AVHRR) from the National Oceanographic and Atmospheric Administration (NOAA) was suitable. In the TIMELINE project of the German Aerospace Center, the historic AVHRR archive in HRPT (High Resolution Picture Transmission) format is processed for the European area and, among other processors, one output is the thematic product ‘snow cover’ that will be made available in 1 km resolution since 1981. The snow detection is based on the Normalized Difference Snow Index (NDSI), which enables a direct comparison with the MODIS snow product. In addition to the NDSI, ERA5 re-analysis data on the skin temperature and other level 2 TIMELINE products are included in the generation of the binary snow mask. The AVHRR orbit segments are projected from the swath projection into LAEA Europe, aggregated into daily coverages, and from this, the 10-day and monthly snow covers are finally calculated. In this publication, the snow cover algorithm is presented, as well as the results of the first validations and possible applications of the final product.

Cloud–Snow Confusion with MODIS Snow Products in Boreal Forest Regions

Reliable cloud masks in Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have a high potential to improve the retrieval of snow properties. However, cloud–snow confusion is a popular problem in MODIS snow cover products, especially in boreal forest areas. A large amount of forest snow is misclassified as clouds because of the low normalized difference snow index (NDSI), and excessive cloud masks limit the application of snow products. In addition, ice clouds are easily misclassified as snow due to their similar spectral characteristics, which leads to snow commission errors. In this paper, we quantitatively evaluated the cloud–snow confusion in Northeast China and found that snow-covered forests and transition zones from snow-covered to snow-free areas are prone to being misclassified as clouds, while clouds are less likely to be misclassified as snow. A temporal-sequence cloud–snow-distinguishing algorithm based on the high-frequency observation characteristics of the Himawarri-8 geostationary meteorological satellite is proposed. In the temporal-sequence images acquired from that satellite, the NDSI variance in cloud pixels should be greater than that of snow because clouds vary over time, while snow is relatively stable. In the MODIS snow cover products, the cloud pixels with NDSI variance lower than a threshold are identified as cloud-free areas and attributed their raw NDSI value, while the snow pixels with NDSI variance greater than the threshold are marked as clouds. We applied this method to MOD10A1 C6 in Northeast China. The results showed that the excessive cloud masks were greatly eliminated, and the new cloud mask was in good agreement with the real cloud distribution. At the same time, some possible ice clouds which had been misclassified as snow for their spectral characteristics similar to those of snow were identified correctly.

Characterization of NDSI Variation: Implications for Snow Cover Mapping

The normalized difference snow index (NDSI) plays an important role in mapping snow cover with spaceborne visible and shortwave-infrared imagery. The NDSI variation depends on illuminating-viewing geometry and snow physical properties, including equivalent grain size (EGS), snow depth (SD) and impurity concentration, as well as fractional snow cover (FSC) within a mixed pixel; however, it is still not fully understood. To quantifiably characterize the pattern of snow NDSI variation, we use a light scattering model of snow to calculate bidirectional reflectance and consequent NDSI values for a wide range of illuminating-viewing geometries, SD, and EGS values. In the model designated bicontinuous snow model using Geometric Optics theory and Radiative Transfer Equation (bicontinuous-GO/RTE), snowpack is represented by a bicontinuous microstructure, and bidirectional reflectance is simulated based on geometric optics and vector radiative transfer equation. The discrete ordinates radiative transfer (DISORT) algorithm is used to simulate the soot concentration effect on snow NDSI. A soil spectral reflectance model (SOILSPECT) is utilized with the assumption of the linear spectral mixture of snow and soil to quantify the effect of FSC on NDSI. As for discontinuous forests, an analytical hybrid geometric-optical and radiative transfer (GORT) model in conjunction with the bicontinuous-GO/RTE model and the PROpriétésSPECTrales (PROSPECT) model is used to examine the effect of canopy cover, which is related to the maximal FSC viewable to satellites. Modeling results indicate that: 1) snow NDSI is comparably low at off-nadir viewing angles, and this effect is exacerbated by the decline in solar elevation but limited by large EGS; 2) snow NDSI increases with EGS yet becomes saturated at EGS of 500 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> ; 3) the effect of SD that is as low as 1 cm on NDSI is rarely noticeable; 4) the concentration of internally mixed soot up to 1 ppm has little reducing effect on snow NDSI (<0.05); 5) the angular effects on the NDSI thresholding method to identify binary snow cover for soil background are nonsignificantly pronounced in the cross plane (CP); 6) NDSI increases linearly with FSC for soil background at nadir as well as in the CP; 7) the SNOw-MAPping algorithm (SNOMAP) method could omit snow cover at large zenith angles for solar zenith angles <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt; 20^{\circ }$ </tex-math></inline-formula> in forests; and 8) forests complicate the nonlinear relationship between NDSI and canopy cover with fully snow-covered ground beneath canopies. These findings imply important uncertainty sources of binary and FSC mapping with NDSI.

An Improved Spatiotemporal Fusion Algorithm for Monitoring Daily Snow Cover Changes With High Spatial Resolution

Considering the tradeoff between spatial resolution and temporal resolution, spatiotemporal fusion has become a promising technique to monitor snow cover dynamics with both high spatial and temporal resolutions. The representative spatiotemporal fusion methods, e.g. Spatial Temporal Data Fusion Approach (STDFA), usually exist obvious phenomenon of spectral distortion when the surface reflectance changes nonlinearly, which affects the quality of the spatiotemporal fusion image. To address this issue, an effective STDFA-Matching-Pix2pix-Generative Adversarial Network (SMPG) algorithm combining the unmixing-based method, deep learning method, pre-matching and post-matching module is proposed to reduce the spectral distortion of STDFA fusion image. The high-temporal-low-spatial (HTLS) resolution MOD09GA data and high-spatial-low-temporal resolution (HSLT) Landsat 8 data are selected in this study. SMPG algorithm is firstly employed to obtain daily high-spatial-high-temporal (HSHT) images, and then daily snow cover results with a spatial resolution of 30 m are obtained by calculating the normalized difference snow index (NDSI). SMPG algorithm is further compared with STDFA, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Swin SpatioTemporal Fusion Model (SwinSTFM), and Generative Adversarial Network-based SpatioTemporal Fusion Model (GAN-STFM). The experimental results indicate that the proposed algorithm yields better overall performance in daily spatiotemporal fusion image and snow cover result with a spatial resolution of 30 m. The mean correlation coefficient (CC) of SMPG can achieve 0.962, which is 0.06-0.36 higher than that of other spatiotemporal fusion methods. The error between the percentage of snow cover area obtained through SMPG and validation data is within 0.84%.

Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.

Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data

It is of great significance to monitor sea ice for relieving and preventing sea ice disasters. In this paper, the growth and development of sea ice in Liaodong Bay of Bohai Sea in China were monitored using Sentinel-2 remote sensing data during the freezing period from January to March in 2018. Based on the comprehensive analysis of the spectral characteristics of seawater and sea ice in visible bands, supplemented by the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI), we proposed a new method based on decision tree classification for extracting sea ice types in Liaodong Bay of Bohai Sea. Using the remote sensing data of eight satellite overpasses acquired from Sentinel-2A/B satellites, the distribution and area of the different sea ice types in Liaodong Bay during the freezing period of 2017/2018 were obtained. Compared with the maximum likelihood (ML) classification method and the support vector machine (SVM) classification method, the proposed method has higher accuracy when discriminating the sea ice types, which proved the new method proposed in this paper is suitable for extracting sea ice types from Sentinel-2 optical remote sensing data in Liaodong Bay. And its classification accuracy reaches 88.05%. The whole process of evolution such as the growth and development of sea ice in Liaodong Bay during the freezing period from January to March in 2018 was monitored. The maximum area of sea ice was detected on 27 January 2018, about 10,187 km2. At last, the quantitative relationship model between the sea ice area and the mean near-surface temperature derived by MODIS data in Liaodong Bay was established. Through research, we found that the mean near-surface temperature was the most important factor for affecting the formation and melt of sea ice in Liaodong Bay.

Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)

Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.

MODIS NDVI Multi-Temporal Analysis Confirms Farmer Perceptions on Seasonality Variations Affecting Apple Orchards in Kinnaur, Himachal Pradesh

Apple cultivation in the Kinnaur district of the northern Indian State of Himachal Pradesh faces challenges from climatic changes and developmental activities. Farmers in the neighboring districts have already faced a major loss of livelihood due to seasonal changes. Therefore, it is important to study the extent of seasonal variations in the apple growing locations of this region. This study makes that attempt by assessing seasonality variations during a 15-year period from 2004 to 2018 when maximum construction activities occurred in this region. The study uses geospatial and statistical techniques in addition to farmer perceptions obtained during a field visit in November 2019. A temporal pattern using a normalized difference vegetation index (NDVI) based on Moderate Resolution Imaging Spectroradiometer (MODIS) was studied for seven apple-growing locations in the district. The results show high seasonal variations and reduced snowfall at lower elevations, resulting in less chilling hours, which are necessary for the healthy growth of apples. The normalized difference snow index (NDSI) and rainfall show a high correlation with apple growth. Local farmers are unprepared for future seasonal disturbances, as they lack early warning systems, insurance for apple crops, and alternative livelihood options.

Non-Binary Snow Index for Multi-Component Surfaces

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.