scholarly journals Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1989 ◽  
Author(s):  
Liyun Dai ◽  
Tao Che ◽  
Hongjie Xie ◽  
Xuejiao Wu
Keyword(s):  
Tibetan Plateau ◽  
Brightness Temperature ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Depth ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
New Method ◽  
Retrieval Algorithm ◽  
Mountainous Areas

Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.

scholarly journals CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data

2013 ◽  
Vol 17 (2) ◽  
pp. 783-793 ◽  
Author(s):  
T. Y. Lakhankar ◽  
J. Muñoz ◽  
P. Romanov ◽  
A. M. Powell ◽  
N. Y. Krakauer ◽  
...  
Keyword(s):  
Grain Size ◽  
Field Experiment ◽  
Brightness Temperature ◽  
Snow Depth ◽  
Winter Season ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Substantial Impact ◽  
Brightness Temperatures ◽  
Snowpack Properties

Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during January–March 2011 at the research site of the National Weather Service office, Caribou, ME, USA. In this experiment dual-polarized microwave (37 and 89 GHz) observations were accompanied by detailed synchronous observations of meteorology and snowpack physical properties. The objective of this long-term field experiment was to improve understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations. In this paper we present an overview of the field experiment and comparative preliminary analysis of the continuous microwave and snowpack observations and simulations. The observations revealed a large difference between the brightness temperature of fresh and aged snowpack even when the snow depth was the same. This is indicative of a substantial impact of evolution of snowpack properties such as snow grain size, density and wetness on microwave observations. In the early spring we frequently observed a large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization corresponding to daytime snowmelt and nighttime refreeze events. SNTHERM (SNow THERmal Model) and the HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures, respectively. Simulated snow depth and snowpack temperature using SNTHERM were compared to in situ observations. Similarly, simulated microwave brightness temperatures using the HUT model were compared with the observed brightness temperatures under different snow conditions to identify different states of the snowpack that developed during the winter season.

scholarly journals Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

2021 ◽  
Vol 13 (4) ◽  
pp. 657
Author(s):  
Pengtao Wei ◽  
Tingbin Zhang ◽  
Xiaobing Zhou ◽  
Guihua Yi ◽  
Jingji Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
Environmental Factors ◽  
Spatial Resolution ◽  
Snow Depth ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Passive Microwave Remote Sensing ◽  
Depth Data

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.

scholarly journals Evaluation of snow cover and snow depth on the Qinghai-Tibetan Plateau derived from passive microwave remote sensing

2016 ◽  
Author(s):  
Liyun Dai ◽  
Tao Che ◽  
Yongjian Ding ◽  
Xiaohua Hao
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Ground Temperature ◽  
Omission Error ◽  
Sensing Data

Abstract. Snow cover on the Qinghai-Tibetan plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high elevation region of Asia. At present, passive microwave (PM) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, in situ observations, and airborne observation data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PM remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. Comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PM grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PM remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

scholarly journals Spatiotemporal variation of snow depth in the Northern Hemisphere from 1992 to 2016

2019 ◽  
Author(s):  
Xiongxin Xiao ◽  
Tingjun Zhang ◽  
Xinyue Zhong ◽  
Xiaodong Li ◽  
Yuxing Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Absolute Error ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Snow Mass

Abstract. Snow cover is an effective indicator of climate change due to its impact on regional and global surface energy and water balance, and thus also weather and climate, hydrological processes and water resources, and the ecosystem as a whole. The overall objective of this study is to investigate changes and variations of snow depth and snow mass over the Northern Hemisphere from 1992 to 2016. We developed a long term Northern Hemisphere daily snow depth and snow water equivalent product (NHSnow) by applying the support vector regression snow depth retrieval algorithm, using passive microwave remote sensing data from the period. NHSnow product was evaluated along with the other two snow cover products (GlobSnow and ERA-Interim/Land) for its accuracy across the Northern Hemisphere. The evaluation results show that NHSnow performs comparably well with relatively high accuracy (bias: −0.59 cm, mean absolute error: 15.12 cm, and root mean square error: 20.11 cm) when benchmarked against the station snow depth measurements. Further analyses were conducted across the Northern Hemisphere using snow depth, snow mass, and snow cover days as indices. Analysis results show that annual average snow mass have a significant declining trend, with a rate of about 19.72 km3 yr.−1 or a 13 % reduction in snow mass. Although spatial variation pattern of snow depth and snow cover days exhibited slight regional differences, they generally reveal the decreasing trend over the most area of the Northern Hemisphere. Our work provides evidence that rapid changes in snow depth and snow mass are occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

scholarly journals Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016

2020 ◽  
Vol 12 (17) ◽  
pp. 2728
Author(s):  
Xiongxin Xiao ◽  
Tingjun Zhang ◽  
Xinyue Zhong ◽  
Xiaodong Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Annual Average ◽  
Depth Analysis ◽  
Variation Trends

A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

scholarly journals Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

2019 ◽  
Vol 11 (24) ◽  
pp. 3037
Author(s):  
Lingjia Gu ◽  
Xintong Fan ◽  
Xiaofeng Li ◽  
Yanlin Wei
Keyword(s):  
Brightness Temperature ◽  
Snow Depth ◽  
Northeast China ◽  
Mean Squared Error ◽  
Microwave Remote Sensing ◽  
Snow Accumulation ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Lookup Table ◽  
Snow Season

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

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