EVALUATION OF VEGETATION EFFECT ON THE MICROWAVE REMOTE SENSING BASED ON THE FIELD EXPERIMENT, AND ITS APPLICATION TO A SNOW DEPTH ESTIMATION

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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

10.5194/tc-2016-262-ac2 ◽

2017 ◽

Author(s):

Tao Che

Keyword(s):

Remote Sensing ◽

Tibetan Plateau ◽

Snow Cover ◽

Snow Depth ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Passive Microwave Remote Sensing

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

Hydrology and Earth System Sciences ◽

10.5194/hess-17-783-2013 ◽

2013 ◽

Vol 17 (2) ◽

pp. 783-793 ◽

Cited By ~ 9

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.

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Snow depth retrieval algorithm of saline-alkali land in the western Jilin Province of China using passive microwave remote sensing data

10.1117/12.2186292 ◽

2015 ◽

Author(s):

Mingbo Sun ◽

Lingjia Gu ◽

Ruizhi Ren ◽

Haoyang Fu

Keyword(s):

Remote Sensing ◽

Snow Depth ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Passive Microwave Remote Sensing ◽

Sensing Data ◽

Western Jilin Province ◽

Western Jilin

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Evaluating snow depth in Western China based on passive microwave remote sensing

2009 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2009.5418162 ◽

2009 ◽

Cited By ~ 1

Author(s):

Xiaojun Yin ◽

J. Shi ◽

Jinyang Du ◽

Jiang Lingmei

Keyword(s):

Remote Sensing ◽

Snow Depth ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Western China ◽

Passive Microwave Remote Sensing

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Microwave remote sensing of soil moisture with vegetation effect

10.1117/12.373132 ◽

1999 ◽

Author(s):

Teferi D. Tsegaye ◽

Ramarao Inguva ◽

Roger H. Lang ◽

Peggy E. O'Neill ◽

Ahmed Fahsi ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Microwave Remote Sensing ◽

Vegetation Effect

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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

Remote Sensing ◽

10.3390/rs13040657 ◽

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.

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

10.5194/tc-2016-262 ◽

2016 ◽

Cited By ~ 1

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.

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