scholarly journals EVALUATION OF A BLENDED SATELLITE IN-SITU SNOW DEPTH ANALYSIS OVER MOUNTAIN TERRAIN

2020 ◽  
Vol 10 (4) ◽  
pp. 583-586
Author(s):  
Cezar Kongoli ◽  
◽  
Keyword(s):  
Snow Depth ◽  
Mountain Terrain ◽  
Depth Analysis

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Validation of MODIS snow cover images over Austria

2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Classification Accuracy ◽  
Snow Depth ◽  
Seasonal Pattern ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Temporal Shift ◽  
Early Afternoon ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

Comparison of seasonal evapotranspiration of temperate coniferous forests with Copernicus Sentinel-1 time series

2021 ◽  
Author(s):  
Marlin Markus Mueller ◽  
Clémence Dubois ◽  
Thomas Jagdhuber ◽  
Carsten Pathe ◽  
Christiane Schmullius
Keyword(s):  
Time Series ◽  
Extreme Weather Events ◽  
Coniferous Forests ◽  
Weather Data ◽  
Complex Nature ◽  
Southeastern Part ◽  
Backscatter Signal ◽  
Multiple Observations ◽  
Depth Analysis

<p>A changing climate accompanied by an increasing number of extreme weather events puts pressure on ecosystems around the globe. Evapotranspiration is one of the key metrics for understanding vegetation dynamics and changes in an ecosystem. Due to its complex nature, evapotranspiration is difficult to determine on a larger scale.<br>Existing approaches to correlate evapotranspiration measurements and radar backscatter signals were completed in boreal forests using ground-based scatterometers for short time series (several months) with much higher temporal resolution (multiple observations per hour) for small test sites. Our goal is to build upon this research to establish a broader understanding on the influences of evapotranspiration on the signal of the widely used Copernicus Sentinel-1 C-Band SAR for managed temperate coniferous forests. Variations of the observed backscatter signals (VV, VH) over several growing seasons and years (2016-2020) are investigated.<br>Besides wind, temperature or precipitation as some of the influencing parameters on the C-band SAR signal, we focus our analyses on the influence of evapotranspiration on the Sentinel-1 C-band signal. Therefore, we recorded long time series of Sentinel-1 data to investigate and estimate the correlation between forest evapotranspiration dynamics and SAR signal variations. For this purpose, Sentinel-1 and weather data from July 2016 to December 2020 were obtained for forested areas in the southeastern part of the Free State of Thuringia, central Germany.<br>We use four different weather station datasets with daily measurements to calculate evapotranspiration values following the Penman-Monteith approach and apply regression analyses to gain a better understanding about the influence on the SAR signal. To obtain regions with speckle-suppressed backscatter for in situ comparison, forest areas in a radius of five kilometers around the four weather stations are considered. For the analysis, radar datasets are differentiated in co- and cross-polarized data as well as descending and ascending flight directions. It seems also important to distinguish between frozen and no-frozen conditions as we discover strong changes in the C-band SAR signal but only minor changes in evapotranspiration values for temperatures below freezing level. Excluding frozen conditions, in situ evapotranspiration measurements and the SAR backscatter variations over four years directly correlate with R2-values up to 0.48 without any parameterization or calibration on both sides (SAR & in situ). Currently we are investigating statistical methods for in-depth analysis of the correlation between the two datasets. As the SAR backscatter signal at C-band is not a direct and sole function of evapotranspiration, future work will combine the modelling of the different influence parameters of the environment on the SAR backscatter signal and aim at quantifying their respective influence on the signal to better understand the seasonal signal variations.</p>

scholarly journals In-Situ LED-Based Observation of Snow Surface and Depth Transects

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2292
Author(s):  
Celeste Barnes ◽  
Chris Hopkinson ◽  
Thomas Porter ◽  
Zhouxin Xi
Keyword(s):  
Snow Depth ◽  
Surface Albedo ◽  
Surface Condition ◽  
Field Of View ◽  
Snow Surface ◽  
Depth Measurement ◽  
Individual Distance ◽  
Strong Linear Relationship ◽  
Better Than

As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).

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