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Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 20624

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Special Issue Editors

Dr. Simone Pettinato

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Guest Editor

IFAC-CNR, Via Madonna del Piano 10, 50019 Firenze, Italy
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR and microwave radiometers; GNSS-R, retrieval algorithms development
Special Issues, Collections and Topics in MDPI journals

Dr. Emanuele Santi

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Guest Editor

IFAC-CNR, Via Madonna del Piano 10, 50019 Firenze, Italy
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR and microwave radiometers; GNSS-R, retrieval algorithms development; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Snow represents a seasonal storage from where water is rapidly released during the melting period, and it has a large influence on surface energy and moisture fluxes: the spatial and temporal behavior of snow coverage can heavily influence floods, agriculture, hydropower and climate. The accurate characterization of snow is therefore a need for environmental, scientific and economic purposes.

In this respect, the possibility of observing snow from space with high spatial and temporal resolution is undoubtedly appealing. Snow cover area (SCA) monitoring using optical and microwave sensors has been reported for decades, and microwave sensors (both active and passive) were also demonstrated able to provide information on other snow parameter as Snow Depth (SD) and its Water Equivalent (SWE).

This Special Issue “Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes” aims at exploring the current potentialities of active and passive microwave joint with optical remote sensing in order to quantify, how much the hydrological cycle and climate change are influenced by snow.

Contributions on emerging methods and technologies will have special relevance within this Special Issue, as well as those ones where the physical mechanisms that regulate the interaction of microwave/visible and snow are welcome.

Dr. Simone Pettinato
Dr. Emanuele Santi
Guest Editors

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Keywords

SAR
Radiometers
Scatterometers
Satellite optical and infrared sensors
Optical and microwave satellite data fusion
Hydrological Cycle
Snow Depth and Snow Water Equivalent

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Published Papers (5 papers)

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Research

29 pages, 12755 KiB

Open AccessArticle

Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China

by Tao Chen, Jinmei Pan, Shunli Chang, Chuan Xiong, Jiancheng Shi, Mingyu Liu, Tao Che, Lifu Wang and Hongrui Liu

Remote Sens. 2020, 12(3), 507; https://doi.org/10.3390/rs12030507 - 5 Feb 2020

Cited by 15 | Viewed by 3732

Abstract

Validation of the snow process model is an important preliminary work for the snow parameter estimation. The snow grain growth is a continuous and accumulative process, which cannot be evaluated without comparing with the observations in snow season scale. In order to understand the snow properties in the Asian Water Tower region (including Xinjiang province and the Tibetan Plateau) and enhance the use of modeling tools, an extended snow experiment at the foot of the Altay Mountain was designed to validate and improve the coupled physical Snow Thermal Model (SNTHERM) and the Microwave Emission Model of Layered Snowpacks (MEMLS). By matching simultaneously the observed snow depth, geometric grain size, and observed brightness temperature (T_B), with an RMSE of 1.91 cm, 0.47 mm, and 4.43 K (at 36.5 GHz, vertical polarization), respectively, we finalized the important model coefficients, which are the grain growth coefficient and the grain size to exponential correlation length conversion coefficients. When extended to 102 meteorological stations in the 2008–2009 winter, the SNTHERM predicted the daily snow depth with an accuracy of 2–4 cm RMSE, and the coupled SNTHERM-MEMLS model predicted the satellite-observed T_B with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered. Full article

(This article belongs to the Special Issue Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes)

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31 pages, 13070 KiB

Open AccessArticle

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

by Surendar Manickam and Ana Barros

Remote Sens. 2020, 12(3), 483; https://doi.org/10.3390/rs12030483 - 3 Feb 2020

Cited by 20 | Viewed by 4411

Abstract

This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10 dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150–250 m, respectively, in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100–1000 m range with a break at (180–360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications. Full article

(This article belongs to the Special Issue Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes)

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20 pages, 8467 KiB

Open AccessArticle

High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

by Mingyu Liu, Chuan Xiong, Jinmei Pan, Tianxing Wang, Jiancheng Shi and Ninglian Wang

Remote Sens. 2020, 12(3), 460; https://doi.org/10.3390/rs12030460 - 1 Feb 2020

Cited by 6 | Viewed by 3580

Abstract

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research. Full article

(This article belongs to the Special Issue Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes)

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23 pages, 10631 KiB

Open AccessArticle

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

by Lingjia Gu, Xintong Fan, Xiaofeng Li and Yanlin Wei

Remote Sens. 2019, 11(24), 3037; https://doi.org/10.3390/rs11243037 - 16 Dec 2019

Cited by 9 | Viewed by 3133

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes)

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22 pages, 3682 KiB

Open AccessArticle

Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model

by Anxin Ding, Ziti Jiao, Yadong Dong, Xiaoning Zhang, Jouni I. Peltoniemi, Linlu Mei, Jing Guo, Siyang Yin, Lei Cui, Yaxuan Chang and Rui Xie

Remote Sens. 2019, 11(13), 1611; https://doi.org/10.3390/rs11131611 - 6 Jul 2019

Cited by 15 | Viewed by 5126

Abstract

The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and used in the kernel-driven BRDF model framework. However, there is a need to further evaluate the influence of using this snow kernel to improve the original kernel-driven models in snow albedo retrieval applications. The aim of this study is to perform such an evaluation using a variety of snow BRDF data. The RossThick-Roujean (RTR) model is used as a framework for taking in the new snow kernel (hereafter named the RTS model) since the Roujean geometric-optical (GO) kernel captures a neglectable hotspot effect and represents a more prominent dome-shaped BRDF, especially at a small solar zenith angle (SZA). We obtained the following results: (1) The RTR model has difficulties in reconstructing the snow BRDF shape, especially at large SZAs, which tends to underestimate the reflectance in the forward direction and overestimate reflectance in the backward direction for various data sources. In comparison, the RTS model performs very well in fitting snow BRDF data and shows high accuracy for all data. (2) The RTR model retrieved snow albedos at SZAs = 30°–70° are underestimated by 0.71% and 0.69% in the red and near-infrared (NIR) bands, respectively, compared with the simulation results of the bicontinuous photon tracking (bic-PT) model, which serve as “real” values. However, the albedo retrieved by the RTS model is significantly improved and generally agrees well with the simulation results of the bic-PT model, although the improved model still somewhat underestimates the albedo by 0.01% in the red band and overestimates the albedo by 0.05% in the NIR band, respectively, at SZAs = 30°–70°, which may be negligible. (3) The albedo derived by these two models shows a high correlation (R² > 0.9) between the field-measured and Polarization and Directionality of the Earth’s Reflectances (POLDER) data, especially for the black-sky albedo. However, the albedo derived using the RTR model is significantly underestimated compared with the RTS model. The RTR model underestimates the black-sky albedo (white-sky albedo) retrievals by 0.62% (1.51%) and 0.93% (2.08%) in the red and NIR bands, respectively, for the field-measured data. The shortwave black-sky and white-sky albedos derived using the RTR model for the POLDER data are underestimated by 1.43% and 1.54%, respectively, compared with the RTS model. These results indicate that the snow kernel in the kernel-driven BRDF model frame is more accurate in snow albedo retrievals and has the potential for application in the field of the regional and global energy budget. Full article

(This article belongs to the Special Issue Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes)

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