Snowmelt Detection Research Articles

High-resolution Snowmelt Detection in Mountainous Areas Based on Remote Sensing Retrieved Snow Surface Grain Size Variation

High-resolution Snowmelt Detection in Mountainous Areas Based on Remote Sensing Retrieved Snow Surface Grain Size Variation

Toward a real validation of passive microwave snowmelt detection algorithms over the Antarctic Ice sheet

Antarctic Ice Sheet (AIS) surface snowmelt can profoundly impact climatological and hydrological processes, as well as energy and mass balances. Passive microwave remote sensing has proven highly effective in detecting snowmelt, and the accuracy of the algorithms is typically assessed using air temperature (Tair). Nonetheless, the occurrence of snowmelt cannot be solely determined by Tair being above the melting point, it can only provide evaluation of the algorithms rather than a real validation. This study is the first to conduct a reliable validation of six commonly used algorithms by using the melt rate data from a surface energy balance model, including the cross-polarized gradient ratio (XPGR) algorithm, the diurnal amplitude variation (DAV) algorithm, the Microwave Emission Model of Layered Snowpacks based (MEMLS-based) algorithm, the constant threshold algorithm (CTA), the Ashcraft and Long Algorithm (ALA), and the M + 30 K (M30 hereafter) algorithm. Results suggest that the M30 algorithm and MEMLS-based algorithm have overall better performances, with high overall accuracies (with a value of 95.81 % and 95.56 %) and high kappa coefficient (with a value of 0.69 and 0.68). Though Tair is not reliable for evaluating the algorithms, a threshold of Tair = − 0.8 ℃ can serve as an alternative criterion in the absence of melt rate data. Additionally, two algorithms with the highest accuracy were selected to investigate interannual snowmelt trends in the Antarctic and the sub-regions. The results show a significant increase in melt extent and density in the Wilkes and Adelie sub-region during the period 1979–2020.

Snowmelt detection on the Antarctic ice sheet and ice Shelves based on AMSR2 89 GHz channels

Currently, the spatial resolution of snowmelt detection based on low-frequency brightness temperature data (less than or equal to 36 GHz) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is low, which inhibits the determination of accurate snowmelt changes. The spatial resolution of the AMSR2 89 GHz data is at least twice that of other microwave channels, but liquid water in clouds seriously affects the propagation of the high-frequency microwave signal. In order to apply microwave radiometer AMSR-2 89 GHz data with high spatial resolution but easily disturbed by clouds and water vapor to Antarctic ice-sheet near-surface snowmelt detection, this paper builds a screening model and correction model for 89 GHz data with large interference based on the high reliability of AMSR-2 36 GHz data and the high spatial resolution of 89 GHz data and the internal relationship between the two data. The method proposed in this paper was compared with the cross-polarised gradient ratio (XPGR) algorithm and six automatic weather stations. The average detection accuracy of the proposed method and the XPGR algorithm is 97% and 95%, respectively. The experimental results show that the snowmelt detection method proposed in this paper can more accurately detect the snowmelt on the Antarctic Ice Sheet and Ice Shelves.

Snowmelt detection in Greenland ice sheet based on AMSR2 89GHz

The snowmelt on the surface of the Greenland ice sheet can show the climate change in the Arctic. In order to obtain high spatial resolution and high precision results for snowmelt detection on the polar ice sheet surface, a method of snowmelt detection based on an Advanced Microwave Scanning Radiometer-2 (AMSR-2) 89GHz channel is presented. Firstly, the stable relationship between the polarization ratio of 89GHz data and 36GHz data under clear and cloudless weather conditions is used to screen out the 89GHz data that is not affected by external factors such as clouds and water vapor. Then, the Nobuyuki Otsu (Otsu) method is used to obtain the freeze-thaw threshold, and the snowmelt detection results of the ice sheet surface are obtained from the unaffected data by cloud conditions. The areas affected by cloud cover are replaced by the snowmelt detection results of the ice sheet surface from the gradient ratio (GR) model based on 19GHz and 37GHz horizontal polarization data. Finally, we get the snowmelt detection results of the Greenland ice sheet surface. To verify the feasibility and rationality of the proposed method, the results are compared with those of the cross-polarized gradient ratio algorithm (XPGR) and the simple physical model algorithm. The snowmelt area obtained by our method is between the XPGR and the simple physical model, and the snowmelt trend of our method and the simple physical model algorithm is closer. Then the results of the three algorithms are compared and analyzed with the temperature data from the automatic weather stations showing that the results obtained by our method are closer to the measurement data.

High-resolution spatio-temporal analysis of snowmelt over Antarctic Peninsula ice shelves from 2015 to 2021 using SAR images

ABSTRACT Ice shelves play an essential role in the dynamics of the Antarctic ice sheet. The surface meltwater is important, as it can irreversibly weaken ice shelves by exerting additional hydrostatic pressure. Therefore, high-resolution snowmelt products are urgently needed to accurately analyze melting patterns of ice shelves and further estimate mass loss. In this study, a new high-resolution (40 m) snowmelt dataset over all of the Antarctic Peninsula ice shelves larger than 100 km was developed based on the modified snowmelt detection framework by using a co-orbit normalization method. The dataset provides detailed snowmelt information on each ice shelf, including the image coverage, melting area and melting ratio every 5 days. The melting patterns of three typical ice shelves (George VI, Wilkins and Larsen C Ice Shelves) and the spatio-temporal melting distribution of the Antarctic Peninsula (AP) were further analyzed. The snowmelt information indicates that both the extent and duration of snowmelt have been increasing in the Antarctic Peninsula from 2015 to 2021, and we found that the snowmelt on the Antarctic Peninsula showed a spatial pattern of significantly intense snowmelt on the western side. We believe this study will provide essential data for ice shelf investigation to support other fields of polar research.

Snowmelt detection on the Antarctic ice sheet surface based on XPGR with improved ant colony algorithm

ABSTRACT This study presents an XPGR coupled with an improved ant colony algorithm to automatically detect the Antarctic ice sheet surface snowmelt and acquire high-precision snowmelt information. This approach first enlarges the difference between dry snow and wet snow using the XPGR algorithm and then utilizes an enhanced ant colony algorithm to adaptively find the best threshold for segmenting dry snow and wet snow. The dissimilarity matrix, which determines the initial clustering centre and utilizes Levy flight to modify the clustering radius dynamically, is a major advancement to the ant colony algorithm. The method proposed in this study was compared to the standard XPGR algorithm from October 2017 to February 2018 and from October 2019 to February 2020 to evaluate the practicality and rationale of the proposed method. The further verification of six automatic weather stations (AWS) shows that the method proposed in this study has higher accuracy.

Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does

Abstract. Climate warming will cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Quantifying how sensitive streamflow timing is to climate change and where it is most sensitive remain key questions. Physically based hydrological models are often used for this purpose; however, they have embedded assumptions that translate into uncertain hydrological projections that need to be quantified and constrained to provide reliable inferences. The purpose of this study is to evaluate differences in projected end-of-century changes to streamflow timing between a new empirical model based on diel (daily) streamflow cycles and regional land surface simulations across the mountainous western USA. We develop an observational technique for detecting streamflow responses to snowmelt using diel cycles of incoming solar radiation and streamflow to detect when snowmelt occurs. We measure the date of the 20th percentile of snowmelt days (DOS20) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2=0.85), suggesting that a 1 d earlier DOS20 corresponds with a 1 d earlier DOQ25 and 0.7 d earlier DOQ50. Empirical projections of future DOS20 based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS20 will occur on average 11±4 d earlier per 1 ∘C of warming. However, DOS20 in colder watersheds (mean November–February air temperature, TNDJF<-8 ∘C) is on average 70 % more sensitive to climate change than in warmer watersheds (TNDJF>0 ∘C). Moreover, empirical projections of DOQ25 and DOQ50 based on DOS20 are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS20 based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.

Intercalibration of Brightness Temperatures From FY-3 MWRI for Surface Snowmelt Detection Over Polar Ice Sheets

Surface snowmelt is a vital environmental parameter that affects energy exchanges between polar ice sheets and the atmosphere. Due to the difficulties of continuous in-situ measurements, passive microwave remote sensing technology has become a major method for obtaining ice sheet surface snowmelt states over large areas. Feng Yun-3 (FY-3) series satellites, the second generation of Chinese polar-orbiting meteorological satellite missions, have great potential for providing long-term polar ice sheet surface snowmelt state products. In this study, we establish a monthly inter-calibration model to synergize brightness temperatures from the Microwave Radiation Imager (MWRI) aboard different FY-3 satellites. Based on the calibrated continuous brightness temperature record, an improved snowmelt algorithm is proposed by using an adaptive thresholding method, which does not rely on in-situ observation data. After inter-calibration, the consistency of the melt extent obtained by different sensors is considerably better than before, with the bias decreasing from 85 pixels to 3 pixels in the Greenland Ice Sheet (GrIS) and from 16 pixels to 6 pixels in the Antarctic Ice Sheet (AIS). Evaluation of the snowmelt result is conducted with the automatic weather station (AWS) air temperature, and a promising accuracy is found with an overall accuracy above 92% in the AIS and approximately 86% in the GrIS. This study provides new possibilities for a long-term continuous snowmelt product by connecting FY-3B, FY-3C, FY-3D, and its successors FY-3F and FY-3G. The inter-calibration coefficients and FY-3 crossing times are available at https://doi.org/10.6084/m9.figshare.20657712.v1.

Delineation of Radar Glacier Zones in the Antarctic Peninsula Using Polarimetric SAR

Climate change is a cause of the expansion of snowmelt phenomena in the Antarctic, and shifts in position of wet and dry snow lines have been considered as good indicators of climate changes. The impacts of climate change are observable by the delineation of significant position change of glacier zones. The principal limitation of current glacier zone classification methods by synthetic aperture radar (SAR) image is that it is difficult to discriminate dry-snow and wet-snow zones using only single-polarimetric radar backscattering intensity. This study tried to solve the problem using polarimetric SAR (PolSAR). Analysis indicates that polarimetric decomposition elements could be efficient characteristics to delineate radar glacier zones by recognition of principal backscatter patterns. Further, two radar glacier zone classification processes for polarimetric SAR are proposed: a supervised support vector machine (SVM) classification process and a simple decision-tree classification method. These methods enable reliable delineation of radar glacier zones in the Antarctic Peninsula. Polarimetric SAR, which provides more information about the scattering processes and target structure, proves to be an efficient tool for delineating radar glacier zones and snowmelt detection.

Enhanced winter snowmelt in the Antarctic Peninsula: Automatic snowmelt identification from radar scatterometer

Surface snowmelt affects the energy balance through melt-albedo feedback and may endanger the ice shelves in the Antarctic Peninsula (AP) through hydrofracture. Here, we introduce an automatic snowmelt identification algorithm based on Quick Scatterometer and Advanced Scatterometer. The proposed method can provide self-adaptive thresholds for snowmelt detection based on Rosin thresholding, and is able to detect weak melt signals with a wavelet denoising procedure. Results suggest the AP surface snowmelt has slightly declined during 1999–2018 in the context of recent cooling. However, winter melt index (i.e., melt days times the area of melting) has significantly increased (above the 99% confidence level) with a rate of 83% decade−1. Unprecedented winter snowmelt was found in 2015/2016 when about one-third of the AP experienced snowmelt in late May. This may be attributed to the intrusions of marine air and the enhanced föhn-driven warming resulted from the abnormal northwesterly flow driven by a significant deepening of the Amundsen Sea Low.

Snowmelt Detection with Calibrated, Enhanced‐Resolution Brightness Temperatures (CETB) in Colorado Watersheds

AbstractUnderstanding the timing of snowmelt is critical for water resources management in snow‐dominated watersheds. Passive microwave remote sensing can be used to estimate snowmelt events through brightness temperature satellite observations. Previous studies were limited to lower resolution (~25 km or coarser) datasets, making it difficult to quantify snowpack variability in heterogeneous, high‐relief areas. This study investigates the use of recently available Calibrated, Enhanced‐Resolution Passive Microwave Daily EASE‐Grid 2.0 Brightness Temperature ESDR (CETB) to estimate snowmelt timing at much higher spatial resolution (~3‐6 km) than has been previously available. This study investigates the effectiveness of the CETB product for snowmelt detection in several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002‐2003), along with data for the Senator Beck Basin from the Center for Snow and Avalanche Studies (CSAS). We compare melt variability with nearby air temperature and stream discharge to show that the new CETB product allows detection of hydrologic processes in mountainous watersheds. We show that the higher resolution CETB product can detect snowmelt in heterogeneous terrain more accurately than the coarser resolution product in terms of the number of winter melt events and seasonal melt onset date. This work lays the foundation for the utilization of higher resolution reprocessed CETB data for snowpack evolution more broadly in a range of environments.

Comparisons of snowmelt detected by microwave sensors on the Shackleton Ice Shelf, East Antarctica

ABSTRACTSurface snowmelt is of great importance to the ice sheet’s mass and energy balance. Microwave sensors, including radiometer and scatterometer can be used to map snowmelt. Two new microwave sensors, including the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Scatterometer (ASCAT), were compared in terms of their behaviours in snowmelt detection on the Shackleton Ice Shelf (SIS) in East Antarctica. Melt signals were determined by identifying the sharp changes in AMSR2 brightness temperature and ASCAT backscatter. The results suggest that the SIS began to melt in December, melt area shrank quickly in February after reaching the peak in January. Melt area mapped by the two sensors agreed with each other on the SIS, however, also shows local discrepancies in the places with complex terrains. ASCAT failed to recognized melt signals in the regions with blue ice and rock outcrops where extensive melt ponds were observed based on Landsat 8 images. Snowmelt detected by radiometer and scatterometer shows complementary nature, the combination of multisource remote sensing images is expected to provide a better view of the ice sheet surface melting conditions.

Spatio-Temporal Analysis of Ice Sheet Snowmelt in Antarctica and Greenland Using Microwave Radiometer Data

The surface snowmelt on ice sheets in polar areas (ice sheets of Greenland and Antarctica) is not only an important sensitive factor of global climate change, but also a key factor that controls the global climate. Spaceborne earth observation provides an efficient means of measuring snowmelt dynamics. Based on an improved ice sheet snowmelt detection algorithm and several new proposed parameters for detecting change, polar ice sheet snowmelt dynamics were monitored and analyzed by using spaceborne microwave radiometer datasets from 1978 to 2014. Our results show that the change in intensity of Greenland and Antarctica snowmelt generally tended to increase and decrease, respectively. Moreover, we show that the de-trended snowmelt change in ice sheets of Greenland and Antarctica vary in anti-correlation patterns. Furthermore, analysis in Atlantic Multi-decadal Oscillation, North Atlantic Oscillation, and the Southern Annular Mode suggests that the Atlantic Ocean and atmosphere could be a possible link between the snowmelt variability of the ice sheets of Greenland and Antarctica.

Snowmelt detection from QuikSCAT and ASCAT satellite radar scatterometer data across the Alaskan North Slope

The timing of seasonal snowmelt in high-latitude tundra has implications ranging from local biological productivity to global atmospheric circulation, yet remains difficult to quantify, particularly at large spatial scales. Snowmelt detection in such remote polar environments is possible using satellite-based microwave scatterometers, such as NASA’s QuikSCAT. QuikSCAT measured scattering in Ku-band, which is sensitive to snowmelt signals, from 1999 until the antenna failed in 2009. The Advanced Scatterometer (ASCAT) (2006–2021 (projected) operational), which operates at C-band, may be able to extend the QuikSCAT record, but existing techniques fail to adequately monitor tundra environments. Here, we designed a departure threshold algorithm to produce a consistent 15-year time series of melt onset for the tundra of the Alaskan North Slope, using the overlap period for the enhanced resolution datasets to calibrate the ASCAT melt detection record against QuikSCAT. We produced a time series of day of year of melt onset for 4.45 km x 4.45 km grid cells on the Alaskan North Slope from 2000–2014. Time series validation with in situ mean daily air temperature produced mean R2 values of 0.75 (QuikSCAT) and 0.72 (ASCAT). We qualitatively observed a difference between early-season melt, which occurred rapidly and was driven by strong wind events, and more typical melt, which occurred gradually along a latitudinal gradient. We speculate that future melt timing will have greater frequency of early-season onset as climate change destabilizes the high-latitude atmosphere.

Near-surface snowmelt detection on the Greenland ice sheet from FengYun-3 MWRI data

The melt extent on the Greenland ice sheet plays an important role in energy balance, and the Arctic and global climates. The micro-wave radiation imager (MWRI) is one of the major payloads of Chinese second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the special sensor microwave/image (SSM/I). The cross-polarized gradient ratio (XPGR) is mainly applied in the scanning multichannel microwave radiometer (SMMR) (18 GHz horizontal polarization (18 H) and 37 GHz vertical polarization (37 V)), the advanced microwave scanning radiometer-earth observing system (AMSR-E) (18.7 GHz horizontal polarization (18 H) and 36.5 GHz vertical polarization (36 V)) and SSM/I (19.3 GHz horizontal polarization (19 H) and 37 GHz vertical polarization (37 V)), which increases the differences between dry and wet snow. The hyperplane of support vector machine (SVM) is used to detect the melt information based on the XPGR data on the Greenland ice sheet, which has higher detection accuracy comparing with the existing threshold methods in theory. The results were compared with the SSM/I data (threshold = − 0.0154), and the results show that the proposed method (That is XPGR combining with SVM) for MWRI data is feasible for the detection of the near-surface snowmelt information on the Greenland ice sheet.

A multiscale subpixel mixture analysis applied for melt detection using passive microwave and radar scatterometer image time series of the Antarctic Peninsula (1999–2009)

ABSTRACTThis paper reports a comparative analysis performed on a fraction-image time series of the Antarctic Peninsula from the period 1999–2009 generated by multiresolution remote-sensing images (SSM/I and SSMI/S with 25 km and QuikSCAT with 2.225 km spatial resolutions) for snow-melt detection. Our method is based on the (a) preprocessing of multitemporal remote-sensing data, (b) subpixel mixture analysis of SSMI and QuikSCAT image time series, and (c) evaluation of subpixel analysis, including an assessment of fraction images of wet snow using an independent ASAR dataset and sensitivity analysis on the melt metrics measured by these images. The temporal dynamics of the melt indices derived from the wet-snow fraction images presented a more realistic pattern than the traditional melt metrics measured by Boolean snow-melt detection approaches. Because the snow melt actually occurs at the pixel fractions, the multiscale analysis that was performed suggests an overestimation of the melt metrics calculated using Boolean approaches (which assume that the entire area of the detected pixel shows snow melt). The melt metrics measurements show an overestimation according to the decrease in spatial resolution related to the multiplicative effect of a larger pixel area.

Antarctic ice-sheet near-surface snowmelt detection based on the synergy of SSM/I data and QuikSCAT data

Microwave radiometer SSM/I data and scatterometer QuikSCAT data have been widely used for the ice-sheet near-surface snowmelt detection based on their sensitivity to liquid water present in snow. In order to improve the Antarctic ice-sheet near-surface snowmelt detection accuracy, a new Antarctic ice-sheet near-surface snowmelt synergistic detection method was proposed based on the principle of complementary advantages of SSM/I data (high reliability) and QuikSCAT data (high sensitivity) by the use of edge detection model to automatically extract the edge information to get the distribution of Antarctic snowmelt onset date, snowmelt duration and snowmelt end date. The verification result shows that the proposed snowmelt synergistic detection method improves the detection accuracy from about 75% to 86% based on AWS (Automatic Weather Stations) Butler Island and Larsen Ice Shelf. The algorithm can also be applied to other regions, which provides methodological support and supplement for the global snowmelt detection.

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

Abstract. High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.

Antarctic snow melt detection based on the synergy of SSM/I and QuikSCAT

AbstractMicrowave radiometer SSM/I (Special Sensor Microwave Imager) data and scatterometer QuikSCAT (Quick Scatterometer) data have been widely used for near-surface snow melt detection based on their sensitivity to liquid water present in snow. The SSM/I data have high reliability and the QuikSCAT data have high spatial resolution. In order to improve the accuracy of Antarctic near-surface snow melt detection, we propose a new method based on the synergy of SSM/I and QuikSCAT data, i.e. the snow melt physical model incorporates the complementary advantages of both datasets. Based on comparisons with temperature data from three automatic weather stations, the proposed algorithm improved the accuracy of snow melt detection. The algorithm could also be applied to other regions, which would provide further evidence to support its use and additional data to document changes in the Antarctic due to global climate change.

Snowmelt detection on the Greenland ice sheet using microwave scatterometer measurements

ABSTRACTMicrowave scatterometer measurements have been used in monitoring the snowmelt characteristics of the Greenland ice sheet. The melt signals and the appropriate threshold scheme are critical to the detection of ice-sheet snowmelt. We propose a scatterometer ice-sheet snowmelt detecting method based on diurnal variation and polarization ratio (DVPR) time series. We implement the algorithm to detect the snowmelt of the Greenland ice sheet using Quick Scatterometer (QuikSCAT) data. Moreover, the detection results of the DVPR method are compared with two other scatterometer snowmelts and validated by the automatic weather station (AWS) data in Greenland. The results show that the method based on the DVPR time series has better detection accuracy and this method is more precise for detecting the melt onset and melt end of ice sheets.