Depth In China Research Articles

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

Cross-calibration between MWRI and AMSR2 to improve consistency of snow depth products

The Microwave Radiation Imager (MWRI), boarded on the FY-3 series satellites: FY-3B, FY-3C, and FY-3D, is the first satellite-based microwave radiometer in China, commencing passive microwave brightness temperature data acquisition since 2010. The Advanced Microwave Scanning Radiometer 2 (AMSR2) boarded on the Global Change Observation Mission 1st-Water (GCOM-W1), has been operational since 2012. Despite the FY-3 series satellites are equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2, disparities in observation times and satellite platforms result in inconsistencies in the data obtained by different satellites, which further impacting the consistency of retrieved geophysical parameters. To improve the consistency of brightness temperatures from FY-3B, FY-3C, FY-3D/MWRI, and GCOM-W1/AMSR2, cross-calibrations were conducted among brightness temperatures at ten-channel from above four platforms. The consistency of derived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed. The results show that the correlation coefficients of brightness temperatures at all channels between sensors exceed 0.98. After cross-calibration, the RMSEs and biases of brightness temperatures at all frequencies and snow depth in China derived from them reduce to varying degrees. The consistencies in both brightness temperatures and snow depth of FY-3B/MWRI, FY-3D/MWRI, and AMSR2 are higher than those of FY-3C and others. These findings advocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI, FY-3D/MWRI, and AMSR2, which share similar satellite overpass time, to derived a long-term snow depth dataset.

Research on cooperative control method of comprehensive pressure relief and anchor grouting reinforcement in deep mining roadways

To address the surrounding rock control problems in high-stressed deep mining roadways, we used Suncun Coal Mine with maximum mining depth in China as the engineering background to analyze failure laws of surrounding rock of gob-side entry driving. Combined with field tests of anchor grouting parameters, we carried out numerical simulation with multi factors. The evolution of surrounding rock stress and displacement under different pressure relief parameters were clarified. On the basis, we proposed an integrated control method for the comprehensive pressure relief for coal and rock mass and anchor grouting reinforcement in deep mining roadway. We used geomechanical model test to compare gob-side entry driving method and this new method. The results showed that this new method can reduce average stress at the working face by 15.8% and peak stress of roadway shoulder by 12.5%. We applied this method in the field, which reduced roadway deformation by 535 mm.

The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Investigation of Spatiotemporal Variation and Drivers of Aerosol Optical Depth in China from 2010 to 2020

China has experienced rapid economic growth and serious control of aerosol emissions in the past decade. Thus, the spatiotemporal variations and driving factors of aerosol optical depth (AOD) are urgently needed to evaluate the effectiveness of aerosol control activities. The innovation of this study is a detailed spatial and temporal analysis of aerosol pollution in eight major regions of China from 2010 to 2020 using the MERRA-2 AOD reanalysis product and the driving mechanism based on the Granger causality test, sensitivity, and contribution analysis. The results show that the spatial distribution of AOD varied across the areas. Divided by the Hu Line, the AOD values of the Eastern areas were significantly higher than those of the Western areas. The temporal trend in the last eleven years was dominated by a continuous decline and moderate fluctuations at both annual and seasonal scales. The relationship between socioeconomic factors and AOD drivers was more significant in economically developed regions, suggesting that China pays more attention to haze control while developing its economy. The driving relationship between AOD and temperature was weak, while wind speed and relative humidity were more influential. For vegetation factors, Granger effects were mainly observed in the Northeast, Beijing-Tianjin-Hebei, Guangdong, Central China, and Southwest regions. In the Guangdong and Southwest regions, vegetation and economic factors were the more influential drivers. This study provides a scientific basis for the detection of aerosol changes, driving mechanisms and pollution management in China.

Spatiotemporal changes in snow depth and the influence factors in China from 1979 to 2019.

Snow depth is an important parameter to characterize the characteristics of snow cover, and it is also one of the most sensitive response factors to regional climate change. However, the extent of snow depth variability and its driving mechanisms are still unknown in China. Therefore, in this study, we used the regression analysis, root-mean-square error analysis, anomalous year analysis, and correlation analysis methods to explore the spatiotemporal variation characteristics of snow depth in China from 1979 to 2019 based on the reanalysis snow depth dataset. The results show that (1) the snow distribution in China is obviously spatially heterogeneous, and the southeastern, western, and southern regions of the Qinghai-Tibet Plateau, northern Xinjiang, and northeastern China have high values of snow depth; (2) the high-value regions are also the sensitive regions for anomalous variations in snow depth in China; (3) in the past 41years, the interannual variability of snow depth in China has shown a significantly decreasing trend, and the linear tendency of snow depth is - 0.093cm/10 a (p < 0.01) and the snow depth in four seasons showed a decreasing trend (p < 0.05); and (4) the driving factors of snow heterogeneity are dissimilar in different regions and seasons. In temperate zones, average air temperature is the main factor affecting snow depth in cold temperature, mid temperature, and warm temperature zones; the maximum air temperature is the main factor affecting snow depth in mid temperate and warm temperate zones. Both the minimum air temperature and the average land-surface temperature are important factors affecting the snow depth in the cold temperate, mid temperate and warm temperate zones, and all passed the significance test of 0.01.

Predicting spatiotemporal changes of surface soil organic carbon in China

AbstractEvaluating the spatiotemporal dynamics of soil organic carbon (SOC) is essential for better understanding the role of SOC in global C cycling. In this study, we report on the application of a two‐compartment model for mapping and examining SOC changes in China. We develop a SOC change prediction model combined with an environmental stratification approach and map SOC changes for the top 20‐cm soil depth in China at a grid resolution of 1 km. We perform a model comparison to quantify the effects of environmental stratification on model performance. A mean SOC increase of 0.63 kg m−2 was observed over the past 40 yr. Compared to the model without environmental stratification (the concordance correlation coefficient [CCC]: 0.34–0.40; RMSE: 2.16–2.33 kg m−2), we found that the models incorporating spatial or temporal stratification provide better performance. Notably, the model with spatiotemporal stratification achieved the better performance compared with results without stratification, with the prediction accuracy improved up to 57% in terms of CCC. Our results suggest that both the spatial‐ and temporal‐scale effects of environmental heterogeneity on SOC processes need to be accounted for in the prediction of SOC changes. Furthermore, the results indicate that the developed approach can be used to map SOC changes over large regions. This study also emphasizes that to achieve accurate predictions, it is necessary to choose the proper environmental stratification method to refine the study unit.

Thermal properties and microstructural evolution of coal spontaneous combustion

With the increase of coal energy mining depth in China, coal spontaneous combustion phenomenon becomes more frequent, which makes the safe mining of coal energy in China under serious threat. In order to prevent coal spontaneous combustion, it is significant to investigate the process of coal oxidation and spontaneous combustion by combining thermal and microstructural properties, which is a necessary study to figure out the mechanism of coal spontaneous combustion. A series of coal oxidation experiments were conducted with the aid of high precision Calorimeter and X-ray diffractometer. The results show that the thermal properties of coal spontaneous combustion have obvious segmentation characteristics which is not affected by the degree of metamorphism and heating rates, and the initial exothermic temperature is positively correlated with heating rates. The effect of temperature on microscopic characteristics is not uniform, but the degree of metamorphism has a regular change for microscopic characteristics. Although temperature does not play a decisive role, the spatial structure of coal oxidation process has some reversibility with the change of temperature. Besides, thermal dynamic balance is an important cause of microstructure changes. It is expected that this work will contribute to the refinement of the theory of coal spontaneous combustion.

On the construction of The Theoretical system of English linguistics from a multi-dimensional perspective

With the rapid social and economic development, the connection between countries is becoming more and more close, and the breadth and depth of China's opening to the outside world has been significantly improved. Therefore, English language education has been paid attention to by educational scholars, and the construction of the theoretical system of English linguistics is more and more in-depth. At present, The study of English linguistics in China has begun to integrate with other disciplines and put forward a multi-dimensional research system of English linguistics. On the basis of understanding the importance of multi-dimensional study of English linguistics, this paper mainly analyzes how to construct a linguistic theoretical system from three perspectives, so as to provide basis for relevant academic research.

The decline in the groundwater table depth over the past four decades in China simulated by the Noah-MP land model

Faced with intensified climate change and ever-increasing water demand, the groundwater table depth is vital to sustaining terrestrial environments in a country such as China; however, on a regional scale, insufficient research has been conducted on its variability thus far. Using a land model forced with reanalysis climate data, this paper investigates groundwater table depth changes across China for the 1979–2018 period. The results show a significant overall reduction in the groundwater table depth in China during the past 40 years without explicit consideration of direct human influence. There is an approximately 0.2 m decrease on average, but it is approximately 0.5 m in southern China and 0.8 m to the north of 35°N despite a 1.2 m rise in part of this region due to an increasingly wet climate and a topographic water convergence. The findings provide a first-order pattern of historical groundwater table responses to climate change, highlighting inherent links among subsurface-surface-atmosphere water cycles and worsened terrestrial water availability in China. A model evaluation compared with in situ observations has demonstrated the model’s ability to realistically portray terrestrial water and energy balances on regional scales, as well as the enhancement of their seasonality with incorporated lateral groundwater flow processes. This ability gives us reasonable confidence in the simulated trends and spatial variability. Nonetheless, the mean groundwater table still shows large biases relative to observations, and the problems of human influence, two-way interactions between groundwater and surface water, and local topographical complexity have yet to be properly addressed in the present study and deserve further research.

Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model

The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.

Investigating the ability of multiple reanalysis datasets to simulate snow depth variability over mainland China from 1981 to 2018

AbstractThough the use of reanalysis datasets to analyze snow changes is increasingly popular, the snow depth variability in China simulated by multiple reanalysis datasets has not been well evaluated. Also, the extent of regional snow depth variability and its driving mechanisms are still unknown. In this study, monthly snow depth observations from 325 stations during the period of 1981–2018 were taken to evaluate the ability of five reanalysis datasets (JRA55, MERRA2, GLDAS2, ERA5, and ERA5L) to simulate the spatial and temporal variability of snow depth in China. The evaluation results indicate that MERRA2 has the lowest root-mean-square deviation of snow depth and a high spatial correlation coefficient with observations. This may be partly related to the high accuracy of precipitation and temperature in MERRA2. Also, the 31 combinations of the five reanalysis datasets do not yield better accuracy in snow depth than MERRA2 alone. This is because the other four datasets have larger uncertainty. Based on MERRA2, four hotspot regions with significant snow depth changes from 1981–2018 were identified, including the central Xinjiang (XJ-C), the southern part of the Northeastern Plain and Mountain (NPM-S), and the southwestern (TP-SW) and southeastern (TP-SE) of the Tibetan Plateau. Snow depth changes mostly occurred in spring in TP-SW and winter in XJ-C, NPM-S, and TP-SE. The snow depth increase in XJ-C, NPM-S, and TP-SW is mainly caused by increased seasonal precipitation, while the snow depth decrease in TP-SE is attributed to the combined effects of decreased precipitation and warming temperature in winter.

Budget Structure Discontinuity: Unveiling Mechanism and Connecting Logic in China’s Context

While budget structure discontinuity signals decisional shifts, it has not been studied in depth in China’s context. We identify and explain budget structure discontinuity in 31 Chinese provinces between 1996 and 2018. We identify that fiscal expenditure on economic development and on science, education, culture, and healthcare (SECH) are more discontinuous, while expenditure on public safety is more incremental. We find that because Chinese provincial leaders have strong incentives to outperform predecessors and leaders of other provinces for better promotional prospects and transfer payments. New chief leaders gather political authority as their tenure increases and utilize their authority to overcome institutional inertia to achieve political missions, causing budget discontinuity. We empirically verify our mechanism and find that leaders strive for the upper reaches in economic development and approach the national average in SECH. With our mechanism, we form a coherent logic chain among institutional arrangements, budget discontinuity, and fiscal performance.

Estimation of Soil Organic Carbon Storage in Palustrine Wetlands, China.

Wetlands regulate the balance of global organic carbon. Small changes in the carbon stocks of wetland ecosystem play a crucial role in the regional soil carbon cycle. However, an accurate estimation of carbon stocks is still be debated for China’s wetlands ecosystem due to the limitation of data collection and methodology. Here, we investigate the soil organic carbon (SOC) storage in a 1-m depth in China’s palustrine wetlands. A total of 1383 sample data were collected from palustrine wetlands in China. The data sources are divided into three parts, respectively, data collection from published literature, data from books, and actual measurement data of sample points. The results demonstrate that there is considerable SOC storage in China’s palustrine wetlands (9.945 Pg C), primarily abundant in the northeast, northwest arid and semi-arid as well as Qinghai-Tibet Plateau regions. The SOC density in per unit area soil was higher in the wetland area of northeast, southwest and Qinghai-Tibet plateau. Within China terrestrial scale, the temperature and precipitation differences caused by latitude were the main environmental factors affecting the organic carbon content. Furthermore, except for the southeast and south wetland region, SOC content decreased with depth.

Analysis of the Spatial and Temporal Differences of China’s Power Carbon Footprint

Thermal power generation has increased CO2 emissions, which in turn has put tremendous pressure on the environment. Therefore, analyzing the regional ecological environmental carrying capacity of thermal power carbon emissions is of great significance for countries with high thermal power capacity to guide the development of low-carbon power and industrial layout. To accurately measure the pressure of regional power production for the environment, this paper proposes the concept of renewable energy virtual carbon emission based on the carbon footprint theory. The regional power carbon footprint (PCF) depth model and the regional power carbon deficit model are constructed. Moreover, through the statistics of China’s power production, this paper calculates the depth of China’s PCF and analyzes the temporal and spatial differences of China’s PCF depth from the three aspects of regional power installation structure, power deficit, and PCF transfer. The results obtained illustrate, firstly, that China’s overall PCF continues to increase, with the depth of PCF moving eastward. Secondly, due to the imbalance of China’s PCF, there are significant temporal and spatial differences in the depth of regional PCF. Thirdly, with the rapid development of renewable energy in China, the effect of controlling the depth of regional PCF by renewable energy is rising. Besides, the transfer of PCF among different China’s regions has significant impact on the change of PCF depth of power output provinces.

Slotting effect on pressure relief during gas drainage of low permeability coal

As the increase of the mining depth in China, the underground temperature, in situ stress and CH4 gas pressure also increase. Such factors have great influences on the gas drainage engineering. In this paper, the discontinuous deformation analysis is used to model the stress relief effect of drilling and slot. The effects of slot width, height, and ground pressure was analyzed. The rheology property of the coal mass under different temperature and gas pressure are also studied. the results of this paper can be used as a reference for gas drainage engineering in coal mine.

Evaluating VIIRS EPS Aerosol Optical Depth in China: An intercomparison against ground-based measurements and MODIS

The Visible Infrared Imaging Radiometer Suite (VIIRS) has been providing routine retrievals of aerosol properties since 2011. As a new generation advanced sensor, VIIRS has certain advantages in terms of spectral and accuracy and spatial resolution, especially with updated algorithm for the NOAA Enterprise Processing System (EPS) since July 2017. However, the VIIRS EPS aerosol products have not been extensively validated. In this study, we evaluate the VIIRS EPS AOD product over mainland China, which has been suffering from heavy aerosol pollution in the recent decade. By comparing collocated VIIRS and ground observations over 12 sites for the Aerosol Robotic Network (AERONET) and 5 sites from the Sun-sky radiometer Observation NETwork (SONET), we find that VIIRS AOD achieves overall reasonable agreement, with 50.61% falling within the expected accuracy interval and a correlation of 0.91 with ground observation. A positive bias of 0.13 is noticed with relatively larger mean bias found in the spring and summer. VIIRS AOD also has a high bias compared with MODIS. The bias is found to be slightly increasing with AOD level. The seasonal variability of VIIRS AOD are also mostly consistent with surface observation and MODIS. VIIRS aerosol model still needs improvement, especially for dust and urban aerosols. A Combined Principal Component Analysis (CPCA) to extract dominant spatial and temporal variability between VIIRS and MODIS data also shows very good agreement, an indication that VIIRS is capable of capturing the major aerosol variability over China.

Resources and geology of coalbed methane in China: a review

ABSTRACTChina produced 17.1 billion cubic meters (BCM) of methane sourced from coal seams in 2015, of which 4.43 BCM is from wells drilled from the surface. This level of production is a clear indicator that China has gone into early stage large-scale coalbed methane (CBM) development. CBM resources in China have been extensively investigated since the 1980s. Research has focused on the geological controls of reservoir character. There have been significant advances over the last 37 years that have aided China’s CBM industry. CBM resources less than 2000 m in depth in China are estimated to be 36.81 trillion cubic meters, of which more than 84% occur in nine large-scale basins, such as the Qinshui, Ordos, Junggar, Qianxi, Erenhot, and Hailar. CBM accumulation and coal reservoir characteristics are controlled by the deposition, structure, coal rank, hydrology as well as other geological factors. Each basin has its own unique geological controls that influence the character of CBM reservoirs in both subtle and obvious ways. Coal reservoir geology in some basins or regions in China are still not well understood because of the complexity of the geological settings. At present, large-scale CBM production in China only occurs within the Qinshui and Ordos basins, mostly sourced from middle-to-high rank coal reservoirs. The CBM geology in other basins needs further investigation in order to achieve large-scale commercial production. To this end, the geological research in the paper should address issues such as how to stimulate economic gas flow from deep low permeability reservoirs and how best to efficiently produce from multiple horizons simultaneously. This paper summarizes these and other key issues that are significant scientific and technical challenges for the CBM industry within China.

Geochemical characteristics and genetic types of natural gas in the Changxing-Feixianguan Formations from the Yuanba Gas Field in the Sichuan Basin, China

The Yuanba Gas Field is a large gas field in the marine strata with the largest burial depth in China up to date. The studies performed on the mentioned gas field have achieved significant progress made possible by determining the characteristics of reef flat reservoirs in the marine strata and the main controlling factors of hydrocarbon accumulation. However, there is no consensus on the origin of natural gases stored in the reservoir in the Changxing-Feixianguan Formations in the Yuanba and its adjacent area. The study on geochemical characteristics indicates that natural gases in the reservoir in the Changxing-Feixianguan Formations in the Yuanba Gas Field are mainly composed of alkane gases which are dominated by methane with dryness coefficients generally higher than 0.995. The CO2 and H2S display an average content of 8.55% and 6.47%, respectively. The δ13C1 and δ13C2 values are from −31.2‰ to −27.9‰, and from −29.9‰ to −25.0‰, respectively, displaying the positive carbon isotopic series. The δ13CCO2 values are generally higher than −8‰, and the δD1 values are from −156‰ to −107‰. The identification of gas origin and gas–source correlation indicate that the natural gases reservoired in the Changxing-Feixianguan Formations in the Yuanba Gas Field have been altered by thermochemical sulfate reduction, and they are mainly composed of oil cracking gases derived from the secondary cracking of oil generated by the sapropelic-prone source rocks in the Permian Longtan Formation. The CO2 in the gas pools are mainly inorganic and were derived from the interaction between the acidic fluid and carbonate reservoirs.

Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China

Long-term snow depth/snow water equivalent (SWE) products derived from passive microwave remote sensing data are fundamental for climatological and hydrological studies. However, the temporal continuity of the products is affected by the updating or replacement of passive microwave sensors or satellite platforms. In this study, we inter-calibrated brightness temperature (Tb) data obtained from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMI/S). Then, we evaluated the consistency of the snow cover area (SCA) and snow depth derived from the Scanning Multichannel Microwave Radiometer (SMMR), SSM/I and SSMI/S. The results indicated that (1) the spatial pattern of the SCA derived from the SMMR and SSM/I data was more consistent after calibration than before; (2) the relative biases in the SCA and snow depth in China between the SSM/I and SSMI/S data decreased from 42.42% to 1.65% and from 66.18% to −1.5%, respectively; and (3) the SCA and snow depth derived from the SSM/I data carried on F08, F11 and F13 were highly consistent. To obtain consistent snow depth and SCA products, inter-sensor calibrations between SMMR, SSM/I and SSMI/S are important. In consideration of the snow data product continuation, we suggest that the brightness temperature data from all sensors be calibrated based on SSMI/S.