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Due to the unique feature of the three-dimensional convolution neural network, it is used in image classification. For There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional CNN-based methods mainly use the 2D-CNN for feature extraction, which makes the interband correlations of HSIs underutilized. The 3D-CNN extracts the joint spectralspatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results.
Saeed Ghaderizadeh; Dariush Abbasi-Moghadam; Alireza Sharifi; Na Zhao; Aqil Tariq. Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleSaeed Ghaderizadeh, Dariush Abbasi-Moghadam, Alireza Sharifi, Na Zhao, Aqil Tariq. Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleSaeed Ghaderizadeh; Dariush Abbasi-Moghadam; Alireza Sharifi; Na Zhao; Aqil Tariq. 2021. "Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
Digital soil mapping approaches related to soil organic matter (SOM) are crucial to quantify the process of the carbon cycle in terrestrial ecosystems and thus, can better manage soil fertility. Recently, many studies have compared machine learning (ML) models with traditional statistical models in digital soil mapping. However, few studies focused on the application of hybrid models that combine ML with statistical models to map SOM content, especially in loess areas, which have a complicated geomorphologic landscape. In this study, the trend prediction used two ML models, i.e., gradient boosting modeling and random forest (RF), and a traditional stepwise multiple linear regression plus interpolated residuals generated from two classic geostatistical models, i.e., ordinary kriging and inverse distance weighting, and a high accuracy surface modeling (HASM) were implemented to map SOM content in the Dongzhi Loess Tableland area of China. A total of 145 topsoil samples and heterogeneous environmental variables were collected to develop the hybrid models. Results showed that 18 variables related to soil properties, climate variables, terrain attributes, vegetation indices, and location attributes played an important role in SOM mapping. The models that incorporate ML algorithms and interpolated residuals to predict SOM variation were found to have a better ability to handle complex environment relationships. The HASM model outperformed traditional geostatistical models in interpolating the residuals. In contrast, RF combined with HASM residuals (RF_HASM) gave the best performance, with the lowest mean absolute error (1.69 g/kg), root mean square error (2.30 g/kg), and the highest coefficient of determination (0.57) and concordance correlation coefficient (0.69) values. Moreover, the spatial distribution pattern obtained with RF_HASM yielded a spatial distribution of SOM that better fit the actual distribution pattern of the study area. In conclusion, these results suggest that RF_HASM is particularly capable of improving the mapping accuracy of SOM content at the regional scale.
Zong Wang; ZhengPing Du; Xiaoyan Li; Zhengyi Bao; Na Zhao; Tianxiang Yue. Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping. Ecological Indicators 2021, 129, 107975 .
AMA StyleZong Wang, ZhengPing Du, Xiaoyan Li, Zhengyi Bao, Na Zhao, Tianxiang Yue. Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping. Ecological Indicators. 2021; 129 ():107975.
Chicago/Turabian StyleZong Wang; ZhengPing Du; Xiaoyan Li; Zhengyi Bao; Na Zhao; Tianxiang Yue. 2021. "Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping." Ecological Indicators 129, no. : 107975.
Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.
Na Zhao; Yimeng Jiao. A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates. Remote Sensing 2021, 13, 2693 .
AMA StyleNa Zhao, Yimeng Jiao. A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates. Remote Sensing. 2021; 13 (14):2693.
Chicago/Turabian StyleNa Zhao; Yimeng Jiao. 2021. "A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates." Remote Sensing 13, no. 14: 2693.
Dissolved oxygen (DO) is a direct indicator of water pollution and an important water quality parameter that affects aquatic life. Based on the fundamental theorem of surfaces in differential geometry, the present study proposes a new modeling approach to estimate DO concentrations with high accuracy by assessing the spatial correlation and heterogeneity of DO with respect to explanatory variables. Specifically, a regularization penalty term is integrated into the high-accuracy surface modeling (HASM) method by applying geographically weighted regression (GWR) with some covariates. A modified version of HASM, namely HASM_MOD, is illustrated through a case study of Poyang Lake, China, by comparing the results of HASM, a support vector machine (SVM), and cokriging. The results indicate that HASM_MOD yields the best performance, with a mean absolute error (MAE) that is 38%, 45%, and 42% lower than those of HASM, the SVM, and cokriging, respectively, by using the cross-validation method. The introduction of a regularization penalty term by using GWR with respect to covariates can effectively improve the quality of the DO estimates. The results also suggest that HASM_MOD is able to effectively estimate nonlinear and nonstationary time series and outperforms three other methods using cross-validation, with a root-mean-square error (RMSE) of 0.20 mg/L and R2 of 0.93 for the two study sites (Sanshan and Outlet_A stations). The proposed method, HASM_MOD, provides a new way to estimate the DO concentration with high accuracy.
Na Zhao; ZeMeng Fan; Miaomiao Zhao. A New Approach for Estimating Dissolved Oxygen Based on a High-Accuracy Surface Modeling Method. Sensors 2021, 21, 3954 .
AMA StyleNa Zhao, ZeMeng Fan, Miaomiao Zhao. A New Approach for Estimating Dissolved Oxygen Based on a High-Accuracy Surface Modeling Method. Sensors. 2021; 21 (12):3954.
Chicago/Turabian StyleNa Zhao; ZeMeng Fan; Miaomiao Zhao. 2021. "A New Approach for Estimating Dissolved Oxygen Based on a High-Accuracy Surface Modeling Method." Sensors 21, no. 12: 3954.
Understanding the changing patterns of extreme temperatures is important for taking measures to reduce their associated negative impacts. Based on daily temperature data derived from 2272 meteorological stations in China, the spatiotemporal variations in temperature extremes were examined with respect to covariates by means of the Mann–Kendall test and a spatiotemporal model during 1960–2018. The results indicated that the temporal changes in cold extremes showed decreasing trends and warm extremes experienced increasing trends across almost all of China, with mean change rates of −3.9 days, −1.8 days, 3.7 days and 2.3 days per decade for TN10p, TX10p, TN90p and TX90p, respectively. Nighttime warming/cooling was higher than daytime warming/cooling, which indicated that trends in minimum temperature extremes are more rapid than trends in maximum temperature extremes. In addition, the temporal effect on the temperature extremes varied throughout the year, with significant increasing trends in the temporal heterogeneity of warm extremes occurring during 1992–2018. The areas with strong spatial heterogeneity of cool nights mainly included northeastern and central China, and the spatial variation on cool days was more prominent in northern China. For warm nights, the areas showing high spatial heterogeneity were mainly located in the northwestern part of China, while areas for warm days were distributed in northern China. Our results provide meaningful information for a deeper understanding of the spatiotemporal variations in temperature extremes across mainland China.
Na Zhao; Mingxing Chen. A Comprehensive Study of Spatiotemporal Variations in Temperature Extremes across China during 1960–2018. Sustainability 2021, 13, 3807 .
AMA StyleNa Zhao, Mingxing Chen. A Comprehensive Study of Spatiotemporal Variations in Temperature Extremes across China during 1960–2018. Sustainability. 2021; 13 (7):3807.
Chicago/Turabian StyleNa Zhao; Mingxing Chen. 2021. "A Comprehensive Study of Spatiotemporal Variations in Temperature Extremes across China during 1960–2018." Sustainability 13, no. 7: 3807.
Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.
Na Zhao. An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed. Remote Sensing 2021, 13, 234 .
AMA StyleNa Zhao. An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed. Remote Sensing. 2021; 13 (2):234.
Chicago/Turabian StyleNa Zhao. 2021. "An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed." Remote Sensing 13, no. 2: 234.
Quantifying the impact of urbanization on extreme climate events is significant for ecosystem responses, flood control, and urban planners. This study aimed to examine the urbanization effects on a suite of 36 extreme temperature and precipitation indices for the Beijing-Tianjin-Hebei (BTH) region by classifying the climate observations into three different urbanization levels. A total of 176 meteorological stations were used to identify large cities, small and medium-size cities and rural environments by applying K-means cluster analysis combined with spatial land use, nighttime light remote sensing, socio-economic data and Google Earth. The change trends of the extreme events during 1980–2015 were detected by using Mann-Kendall (MK) statistical test and Sen's slope estimator. Urbanization effects on those extreme events were calculated as well. Results indicated that the cool indices generally showed decreasing trends over the time period 1980–2015, while the warm indices tended to increase. Larger and more significant changes occurred with indices related to the daily minimum temperature. The different change rates of temperature extremes in urban, suburban and rural environments were mainly about the cool and warm night indices. Urbanization in medium-size cities tended to have a negative effect on cool indices, while the urbanization in large cities had a positive effect on warm indices. The significant difference of urbanization effect between large and medium-size cities lay in the daily maximum temperature. Results also demonstrated the scale effect of the urbanization on the extreme temperature events. However, the results showed little evidence of the urban effect on extreme precipitation events in the BTH region. This paper explored the changes in temperature and precipitation extremes and qualified the urbanization effects on those extreme events in the BTH region. The findings of this research can provide new insights into the future urban agglomeration development projects.
Na Zhao; Yimeng Jiao; Ting Ma; Miaomiao Zhao; ZeMeng Fan; XiaoZhe Yin; Yu Liu; Tianxiang Yue. Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China. Science of The Total Environment 2019, 688, 1005 -1015.
AMA StyleNa Zhao, Yimeng Jiao, Ting Ma, Miaomiao Zhao, ZeMeng Fan, XiaoZhe Yin, Yu Liu, Tianxiang Yue. Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China. Science of The Total Environment. 2019; 688 ():1005-1015.
Chicago/Turabian StyleNa Zhao; Yimeng Jiao; Ting Ma; Miaomiao Zhao; ZeMeng Fan; XiaoZhe Yin; Yu Liu; Tianxiang Yue. 2019. "Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China." Science of The Total Environment 688, no. : 1005-1015.
As an important cause of global warming, CO2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO2 data as the accuracy control conditions to obtain high accuracy XCO2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist.
Yu Liu; Tianxiang Yue; Lili Zhang; Na Zhao; Miaomiao Zhao; Yi Liu. Simulation and analysis of XCO2 in North China based on high accuracy surface modeling. Environmental Science and Pollution Research 2018, 25, 27378 -27392.
AMA StyleYu Liu, Tianxiang Yue, Lili Zhang, Na Zhao, Miaomiao Zhao, Yi Liu. Simulation and analysis of XCO2 in North China based on high accuracy surface modeling. Environmental Science and Pollution Research. 2018; 25 (27):27378-27392.
Chicago/Turabian StyleYu Liu; Tianxiang Yue; Lili Zhang; Na Zhao; Miaomiao Zhao; Yi Liu. 2018. "Simulation and analysis of XCO2 in North China based on high accuracy surface modeling." Environmental Science and Pollution Research 25, no. 27: 27378-27392.
Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.
Na Zhao; Tianxiang Yue; Chuanfa Chen; Miaomiao Zhao; ZhengPing Du. An improved HASM method for dealing with large spatial data sets. Science China Earth Sciences 2018, 61, 1078 -1087.
AMA StyleNa Zhao, Tianxiang Yue, Chuanfa Chen, Miaomiao Zhao, ZhengPing Du. An improved HASM method for dealing with large spatial data sets. Science China Earth Sciences. 2018; 61 (8):1078-1087.
Chicago/Turabian StyleNa Zhao; Tianxiang Yue; Chuanfa Chen; Miaomiao Zhao; ZhengPing Du. 2018. "An improved HASM method for dealing with large spatial data sets." Science China Earth Sciences 61, no. 8: 1078-1087.
To overcome the huge volume problem of light detection and ranging (LiDAR) data for the derivation of digital terrain models (DTMs), a least squares compactly supported radial basis function (CSRBF) interpolation method is proposed in this paper. The proposed method has a limited support radius and fewer RBF centers than the sample points, selected by a newly developed surface variation-based algorithm. Those make the linear system of the proposed method not only much sparser but also efficiently solvable. Tests on a synthetic dataset demonstrate that the proposed method is comparable to the smoothing RBF, and far superior to the exact RBF. Moreover, the first is much faster than the others. The proposed method with the RBF centers selected by the surface variation-based algorithm obviously outperforms that with the random selection of equal number. Real-world examples on one private and ten public datasets show that the surfaces of simple interpolation methods including inverse distance weighting, natural neighbor, linear and bicubic suffer from the problems of roughness, peak-cutting, discontinuity and subtle terrain feature loss, respectively. By contrast, the proposed method produces visually appealing results, keeping a good tradeoff between noise removal and terrain feature preservation. Additionally, the new method compares favorably with ordinary kriging (OK) for the generation of high-resolution DTMs in terms of interpolation accuracy, yet the former is much more robust to spatial resolution variation and terrain characteristics than the latter. More importantly, our method is about 4 times faster than OK. In conclusion, the proposed method has high potential for the interpolation of a large LiDAR dataset, especially when both interpolation accuracy and computational cost are taken into account.
Chuanfa Chen; Yanyan Li; Na Zhao; Bin Guo; Naixia Mou. Least Squares Compactly Supported Radial Basis Function for Digital Terrain Model Interpolation from Airborne Lidar Point Clouds. Remote Sensing 2018, 10, 587 .
AMA StyleChuanfa Chen, Yanyan Li, Na Zhao, Bin Guo, Naixia Mou. Least Squares Compactly Supported Radial Basis Function for Digital Terrain Model Interpolation from Airborne Lidar Point Clouds. Remote Sensing. 2018; 10 (4):587.
Chicago/Turabian StyleChuanfa Chen; Yanyan Li; Na Zhao; Bin Guo; Naixia Mou. 2018. "Least Squares Compactly Supported Radial Basis Function for Digital Terrain Model Interpolation from Airborne Lidar Point Clouds." Remote Sensing 10, no. 4: 587.
Estimating an accurate spatial distribution of precipitation with high resolution is necessary for hydrological and ecological applications, especially in data‐scarce and terrain‐complicated river basins. Satellite‐based precipitation data have been widely used to measure the spatial patterns of precipitation, but an improvement in accuracy and resolution is needed. In this article, a new statistical downscaling method is proposed to generate improved monthly precipitation fields at a higher spatial resolution of 1 km in Heihe River basin (HRB), China. The presented methods employed the geographical weighted regression (GWR) method to explore the non‐stationarity between precipitation and its factors, and used the high‐accuracy surface modelling method (HASM) to compensate for the errors produced in the GWR downscaling process. The GWR model was first established under five different spatial scales, and the optimal relation between precipitation derived from the Tropical Rainfall Measuring Mission (TRMM) and its influencing factors was found for each month. The errors caused during the scale change were modified by performing HASM as a data merging framework, which considered both the local climate characteristics and meteorological observations. Results showed that the GWR downscaling method could not generate spatial patterns of precipitation similar to those of the original TRMM products. Although the performance of the GWR method after residual interpolations using Kriging, IDW, and tension Spline was improved, there existed significant variations in some regions, and the accuracy of those methods was still not satisfactory. In comparison with the other four models, GWR‐HASM showed better performance in reproducing the precipitation field at a high spatial resolution. Results indicate that the proposed downscaling method appears feasible for precipitation estimation in data‐scarce river basins.
Na Zhao; Tianxiang Yue; Chuanfa Chen; Mingwei Zhao; ZeMeng Fan. An improved statistical downscaling scheme of Tropical Rainfall Measuring Mission precipitation in the Heihe River basin, China. International Journal of Climatology 2018, 38, 3309 -3322.
AMA StyleNa Zhao, Tianxiang Yue, Chuanfa Chen, Mingwei Zhao, ZeMeng Fan. An improved statistical downscaling scheme of Tropical Rainfall Measuring Mission precipitation in the Heihe River basin, China. International Journal of Climatology. 2018; 38 (8):3309-3322.
Chicago/Turabian StyleNa Zhao; Tianxiang Yue; Chuanfa Chen; Mingwei Zhao; ZeMeng Fan. 2018. "An improved statistical downscaling scheme of Tropical Rainfall Measuring Mission precipitation in the Heihe River basin, China." International Journal of Climatology 38, no. 8: 3309-3322.
Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution.
Ziyue Chen; Danlu Chen; Yan Zhuang; Jun Cai; Na Zhao; Bin He; Bingbo Gao; Bing Xu. Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data. Remote Sensing 2017, 9, 971 .
AMA StyleZiyue Chen, Danlu Chen, Yan Zhuang, Jun Cai, Na Zhao, Bin He, Bingbo Gao, Bing Xu. Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data. Remote Sensing. 2017; 9 (10):971.
Chicago/Turabian StyleZiyue Chen; Danlu Chen; Yan Zhuang; Jun Cai; Na Zhao; Bin He; Bingbo Gao; Bing Xu. 2017. "Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data." Remote Sensing 9, no. 10: 971.
A more accurate Bayesian statistical technique together with a high accuracy surface modelling method (HASM) was used to improve the accuracy of temperature fields in the Beijing–Tianjin–Hebei (BTH) region, China. Bayesian statistical inference theory was first applied to fill missing daily values for meteorological stations on an annual scale. A mixed interpolator was then employed to simulate the annual mean temperature and was compared with other methods. The annual mean temperature was produced and the inter-decade change was investigated. The results show that the values filled by Bayesian statistical inference theory agree well with actual observations. A comparison with other interpolators shows that combining the Bayesian estimation method with the HASM gives better results than those of the other methods tested in this study. The construction of the annual mean temperature for 10 years shows the change interval and the spatial distribution of temperature in the BTH region. The annual temperature over 10 years changed from less than −1 °C in the northwest to more than 14 °C in the southeast. From 1981–1990 (T1) to 1991–2000 (T2) and T2 to 2001–2010 (T3) the temperature change was spatially stationary and varied mainly from −0.05 °C to 0.05 °C, except for some areas such as the southwest of Tangshan city and the south of Tianjin city where land cover and land use has exhibited large decadal variation during the past 30 years.
Na Zhao; Ning Lu; Chuanfa Chen; Han Li; Tianxiang Yue; Lili Zhang; Yu Liu. Mapping temperature using a Bayesian statistical method and a high accuracy surface modelling method in the Beijing-Tianjin-Hebei region, China. Meteorological Applications 2017, 24, 571 -579.
AMA StyleNa Zhao, Ning Lu, Chuanfa Chen, Han Li, Tianxiang Yue, Lili Zhang, Yu Liu. Mapping temperature using a Bayesian statistical method and a high accuracy surface modelling method in the Beijing-Tianjin-Hebei region, China. Meteorological Applications. 2017; 24 (4):571-579.
Chicago/Turabian StyleNa Zhao; Ning Lu; Chuanfa Chen; Han Li; Tianxiang Yue; Lili Zhang; Yu Liu. 2017. "Mapping temperature using a Bayesian statistical method and a high accuracy surface modelling method in the Beijing-Tianjin-Hebei region, China." Meteorological Applications 24, no. 4: 571-579.
The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km2 and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight’s observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2’s XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.
Li Li Zhang; Tian Xiang Yue; John P. Wilson; Ding Yi Wang; Na Zhao; Yu Liu; Dong Dong Liu; Zheng Ping Du; Yi Fu Wang; Chao Lin; Yu Quan Zheng; Jian Hong Guo. Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments. Sensors 2016, 16, 1818 .
AMA StyleLi Li Zhang, Tian Xiang Yue, John P. Wilson, Ding Yi Wang, Na Zhao, Yu Liu, Dong Dong Liu, Zheng Ping Du, Yi Fu Wang, Chao Lin, Yu Quan Zheng, Jian Hong Guo. Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments. Sensors. 2016; 16 (11):1818.
Chicago/Turabian StyleLi Li Zhang; Tian Xiang Yue; John P. Wilson; Ding Yi Wang; Na Zhao; Yu Liu; Dong Dong Liu; Zheng Ping Du; Yi Fu Wang; Chao Lin; Yu Quan Zheng; Jian Hong Guo. 2016. "Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments." Sensors 16, no. 11: 1818.
Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976–2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976–2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011–2040) to T2 (2041–2070) and T2 to T3 (2071–2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.
Na Zhao; Tianxiang Yue; Xun Zhou; Mingwei Zhao; Yu Liu; ZhengPing Du; Lili Zhang. Statistical downscaling of precipitation using local regression and high accuracy surface modeling method. Theoretical and Applied Climatology 2016, 129, 281 -292.
AMA StyleNa Zhao, Tianxiang Yue, Xun Zhou, Mingwei Zhao, Yu Liu, ZhengPing Du, Lili Zhang. Statistical downscaling of precipitation using local regression and high accuracy surface modeling method. Theoretical and Applied Climatology. 2016; 129 (1-2):281-292.
Chicago/Turabian StyleNa Zhao; Tianxiang Yue; Xun Zhou; Mingwei Zhao; Yu Liu; ZhengPing Du; Lili Zhang. 2016. "Statistical downscaling of precipitation using local regression and high accuracy surface modeling method." Theoretical and Applied Climatology 129, no. 1-2: 281-292.
Tianxiang Yue; Bing Xu; Na Zhao; Cui Chen; Olaf Kolditz. Thematic Issue: Environment and Health in China—I. Environmental Earth Sciences 2015, 74, 6361 -6365.
AMA StyleTianxiang Yue, Bing Xu, Na Zhao, Cui Chen, Olaf Kolditz. Thematic Issue: Environment and Health in China—I. Environmental Earth Sciences. 2015; 74 (8):6361-6365.
Chicago/Turabian StyleTianxiang Yue; Bing Xu; Na Zhao; Cui Chen; Olaf Kolditz. 2015. "Thematic Issue: Environment and Health in China—I." Environmental Earth Sciences 74, no. 8: 6361-6365.
Ground observation is able to obtain highly accurate data with high temporal resolution at observation points, but these observation points are too sparsely to satisfy the application requirements at regional scale. Satellite remote sensing can frequently supply spatially continuous information on earth surface, which is impossible from ground-based investigations, but remote sensing description is not able to directly obtain process parameters. In fact, in terms of fundamental theorem of surfaces, a surface is uniquely defined by the first fundamental coefficients, about the details of the surface observed when we stay on the surface, and the second fundamental coefficients, the change of the surface observed from outside the surface. A method for high accuracy surface modeling (HASM) has been developed initiatively to find solutions for error problem and slow-speed problem of earth surface modeling since 1986. HASM takes global approximate information (e.g., remote sensing images or model simulation results) as its driving field and local accurate information (e.g., ground observation data and/or sampling data) as its optimum control constraints. Its output satisfies the iteration stopping criterion which is determined by application requirement for accuracy. This paper reviews problems to be solved in every development stage and applications of HASM.
Tian-Xiang Yue; Li-Li Zhang; Na Zhao; Ming-Wei Zhao; Chuan-Fa Chen; Zheng-Ping Du; Dun-Jiang Song; Ze-Meng Fan; Wen-Jiao Shi; Shi-Hai Wang; Chang-Qing Yan; Qi-Quan Li; Xiao-Fang Sun; Hai Yang; John Wilson; Bing Xu. A review of recent developments in HASM. Environmental Earth Sciences 2015, 74, 6541 -6549.
AMA StyleTian-Xiang Yue, Li-Li Zhang, Na Zhao, Ming-Wei Zhao, Chuan-Fa Chen, Zheng-Ping Du, Dun-Jiang Song, Ze-Meng Fan, Wen-Jiao Shi, Shi-Hai Wang, Chang-Qing Yan, Qi-Quan Li, Xiao-Fang Sun, Hai Yang, John Wilson, Bing Xu. A review of recent developments in HASM. Environmental Earth Sciences. 2015; 74 (8):6541-6549.
Chicago/Turabian StyleTian-Xiang Yue; Li-Li Zhang; Na Zhao; Ming-Wei Zhao; Chuan-Fa Chen; Zheng-Ping Du; Dun-Jiang Song; Ze-Meng Fan; Wen-Jiao Shi; Shi-Hai Wang; Chang-Qing Yan; Qi-Quan Li; Xiao-Fang Sun; Hai Yang; John Wilson; Bing Xu. 2015. "A review of recent developments in HASM." Environmental Earth Sciences 74, no. 8: 6541-6549.
In this paper, a combination of a novel interpolation method and a local regression method was employed to improve the estimation accuracy of monthly precipitation over China. After the normalized processing and Box-Cox transformation of the data, we used the geographically weighted regression (GWR) method to describe the spatial precipitation trend, and then interpolated the residual by using a modified high accuracy surface modeling method (HASM-PRE). A high quality database of monthly precipitation with a resolution of 1 km2 was constructed based on the meteorological stations. Results showed that wet years and dry years appear alternatively, and trend analysis of precipitation data series from 1981 to 2010 showed that the probability of years with extreme precipitation has increased in recent years. Precipitation in winter is rather uncertain and more dynamic from year to year compared to precipitation in summer.
Chen-Liang Wang; Na Zhao; Tian-Xiang Yue; Ming-Wei Zhao; Cui Chen. Change trend of monthly precipitation in China with an improved surface modeling method. Environmental Earth Sciences 2015, 74, 6459 -6469.
AMA StyleChen-Liang Wang, Na Zhao, Tian-Xiang Yue, Ming-Wei Zhao, Cui Chen. Change trend of monthly precipitation in China with an improved surface modeling method. Environmental Earth Sciences. 2015; 74 (8):6459-6469.
Chicago/Turabian StyleChen-Liang Wang; Na Zhao; Tian-Xiang Yue; Ming-Wei Zhao; Cui Chen. 2015. "Change trend of monthly precipitation in China with an improved surface modeling method." Environmental Earth Sciences 74, no. 8: 6459-6469.
A method of surface modeling, high accuracy surface modeling (HASM), which is based on the fundamental theorem of surface theory, is modified. The earlier version of HASM is theoretically incomplete and almost performs similar or slightly better than other methods being compared in the practical applications which definitely limit its promotion. According to the fundamental theorem of surface theory, we modify HASM by adding another important nonlinear equation to solve the low accuracy in some cases and make HASM have a complete and solid theory foundation. A numerical test and a real-world example are employed to comparatively validate the effectiveness of this modification. It is found that the accuracy of the simulation result has a great improvement. Another feature of the modified version of HASM is that it is theoretically perfect since it considers the third equation of the surface theory. The modified HASM will be useful with a wide range of spatial interpolation, particularly if the focus on simulation accuracy.
Na Zhao; Tianxiang Yue; Mingwei Zhao. An improved version of a high accuracy surface modeling method. GEM - International Journal on Geomathematics 2013, 4, 185 -200.
AMA StyleNa Zhao, Tianxiang Yue, Mingwei Zhao. An improved version of a high accuracy surface modeling method. GEM - International Journal on Geomathematics. 2013; 4 (2):185-200.
Chicago/Turabian StyleNa Zhao; Tianxiang Yue; Mingwei Zhao. 2013. "An improved version of a high accuracy surface modeling method." GEM - International Journal on Geomathematics 4, no. 2: 185-200.
High accuracy surface modeling (HASM) is a novel surface modeling method. The well known preconditioned conjugate gradient (PCG) method is used to solve the equations produced by HASM. In this paper, in order to improve the convergence rate of PCG, we use two preconditioners: incomplete Cholesky decomposition conjugate gradient method (ICCG) and symmetric successive over relaxation-preconditioned conjugate gradient method (SSORCG), which have not previously been available for use with HASM. Furthermore, we give adequate storage scheme of the large sparse matrix and optimize the performance of sparse matrix-vector multiplication. We test the proposed method on a Dell OP990 machine. Numerical tests show that ICCG has the fastest convergence rate of HASM. We also find that both ICCG and SSORCG have much faster convergence rates than some available solvers.
Na Zhao; Tian Xiang Yue. Fast Methods for Solving High Accuracy Surface Modeling. Journal of Algorithms & Computational Technology 2013, 7, 187 -196.
AMA StyleNa Zhao, Tian Xiang Yue. Fast Methods for Solving High Accuracy Surface Modeling. Journal of Algorithms & Computational Technology. 2013; 7 (2):187-196.
Chicago/Turabian StyleNa Zhao; Tian Xiang Yue. 2013. "Fast Methods for Solving High Accuracy Surface Modeling." Journal of Algorithms & Computational Technology 7, no. 2: 187-196.