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.

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>

scholarly journals On direct passive microwave remote sensing of sea spray aerosol production

2014 ◽  
Vol 14 (21) ◽  
pp. 11611-11631 ◽  
Author(s):  
I. B. Savelyev ◽  
M. D. Anguelova ◽  
G. M. Frick ◽  
D. J. Dowgiallo ◽  
P. A. Hwang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Brightness Temperature ◽  
Microwave Remote Sensing ◽  
Ocean Surface ◽  
Breaking Waves ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Sea Spray ◽  
Sea Spray Aerosol ◽  
Microwave Emissions

Abstract. This study addresses and attempts to mitigate persistent uncertainty and scatter among existing approaches for determining the rate of sea spray aerosol production by breaking waves in the open ocean. The new approach proposed here utilizes passive microwave emissions from the ocean surface, which are known to be sensitive to surface roughness and foam. Direct, simultaneous, and collocated measurements of the aerosol production and microwave emissions were collected aboard the FLoating Instrument Platform (FLIP) in deep water ~ 150 km off the coast of California over a period of ~ 4 days. Vertical profiles of coarse-mode aerosol (0.25–23.5 μm) concentrations were measured with a forward-scattering spectrometer and converted to surface flux using dry deposition and vertical gradient methods. Back-trajectory analysis of eastern North Pacific meteorology verified the clean marine origin of the sampled air mass over at least 5 days prior to measurements. Vertical and horizontal polarization surface brightness temperature were measured with a microwave radiometer at 10.7 GHz frequency. Data analysis revealed a strong sensitivity of the brightness temperature polarization difference to the rate of aerosol production. An existing model of microwave emission from the ocean surface was used to determine the empirical relationship and to attribute its underlying physical basis to microwave emissions from surface roughness and foam within active and passive phases of breaking waves. A possibility of and initial steps towards satellite retrievals of the sea spray aerosol production are briefly discussed in concluding remarks.

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 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.

Experimental Study of C Band Passive Microwave Remote Sensing of Soil Moisture

2013 ◽  
Vol 477-478 ◽  
pp. 624-627
Author(s):  
Xiao Liu Gao ◽  
Hui Hui Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Emission Rate ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Passive Microwave Remote Sensing ◽  
Soil Surface Roughness ◽  
Relationship Of

Passive microwave remote sensing is one of the most effective methods for inversing soil moisture. Under the condition of laboratory, firstly, C band microwave radiation was used to achieve the trial of ground-based remote sensing soil moisture, and then regression analysis was carried out according to the data measured, finally, got the C band experience regression model of soil moisture inversion. The results showed that: in the level-off state of soil surface, soil humidity and soil microwave emission rate is linear negative correlation, in the other words, soil microwave emission rate decreased while the soil moisture increased. Besides, with the increasing of soil surface roughness, both the value of microwave polarization index (MPDI) and microwave emission rate polarization difference Δe have the same trend of quick drop, stabilization and slow raise, and it presented the relationship of quadratic curve with the change of roughness.

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