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Dr. Qingyan Meng
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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Research Keywords & Expertise

0 Urban heat
0 Urban environment remote sensing
0 Urban livablity
0 Urban green space remote sensing
0 Remote sensing information processing

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Journal article
Published: 21 July 2021 in Remote Sensing
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Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.

ACS Style

Wenxiu Liu; Qingyan Meng; Mona Allam; Linlin Zhang; Die Hu; Massimo Menenti. Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China. Remote Sensing 2021, 13, 2858 .

AMA Style

Wenxiu Liu, Qingyan Meng, Mona Allam, Linlin Zhang, Die Hu, Massimo Menenti. Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China. Remote Sensing. 2021; 13 (15):2858.

Chicago/Turabian Style

Wenxiu Liu; Qingyan Meng; Mona Allam; Linlin Zhang; Die Hu; Massimo Menenti. 2021. "Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China." Remote Sensing 13, no. 15: 2858.

Journal article
Published: 24 June 2021 in Sensors
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A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.

ACS Style

Yupeng Kang; Qingyan Meng; Miao Liu; Youfeng Zou; Xuemiao Wang. Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data. Sensors 2021, 21, 4328 .

AMA Style

Yupeng Kang, Qingyan Meng, Miao Liu, Youfeng Zou, Xuemiao Wang. Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data. Sensors. 2021; 21 (13):4328.

Chicago/Turabian Style

Yupeng Kang; Qingyan Meng; Miao Liu; Youfeng Zou; Xuemiao Wang. 2021. "Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data." Sensors 21, no. 13: 4328.

Journal article
Published: 01 June 2021 in Ecological Indicators
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Nowadays urban climate is a global problem and many studies focused on understanding the relation between urban climate the built-up space using radiometric observations of the land surface temperature to estimate and monitor the surface urban heat island intensity (SUHIs). In this study MODIS land surface temperature (LST) data were used. The Yangtze River Delta Urban Agglomeration (YRDUA), eastern China, was selected as an example to study SUHI and multiple influencing factors in 16 big cities. Anthropogenic factors are considered the most important ones in determining SUHI, while natural factors remain influential. By using stratified random sampling (SRS), 78,085 random points were selected within the 16 cities. Nine influencing factors were selected in this study: distance from building (BD), distance from the main roads (RD), distance from water (WD), digital elevation model product (DEM), gross domestic product (GDP), normalized difference vegetation index product (NDVI), nighttime lighting intensity (NTI), population (POP) and impervious surface area data (%ISA). The SUHI intensity was extracted at each random point as well as the values of the influencing factors, NDVI, DEM, ISA, POP, NTI and GDP. For BD, WD and RD, random points were selected from the water, building and main roads using the near tool in ArcGIS to measure these distances. Boosted regression tree (BRT) model was applied to capture the contributions of the above factors to SUHI. We also applied a different procedure to evaluate the relative influence of Land Use and Land Cover (LULC). The relative influence refers to the contribution of each factor to determine SUHI. The influencing factors were ranked on the basis of the relative influence on SUHI. The results showed that (1) higher SUHI intensity was recorded in Shanghai, Jiaxing and Nanjing cities respectively, while Hangzhou recorded the lowest SUHI. (2) Anthropogenic drivers have slightly higher relative influence on SUHI than natural drivers, i.e. 51.29% and 48.71% respectively. The influence of all drivers on SUHI from high to low is NTI (27.62%), ISA (24.38%), NDVI (12.11%), GDP (7.95%), DEM (7.29%), POP (6.37%), BD (5.33%), WD (4.93%), RD (4.02%). (3) The variation in the socioeconomic level lead to different spatial patterns of different influence factors, further indicating that the overall mean SUHI intensity is affected by the development of the city.

ACS Style

Zian Wang; Qingyan Meng; Mona Allam; Die Hu; Linlin Zhang; Massimo Menenti. Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China. Ecological Indicators 2021, 128, 107845 .

AMA Style

Zian Wang, Qingyan Meng, Mona Allam, Die Hu, Linlin Zhang, Massimo Menenti. Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China. Ecological Indicators. 2021; 128 ():107845.

Chicago/Turabian Style

Zian Wang; Qingyan Meng; Mona Allam; Die Hu; Linlin Zhang; Massimo Menenti. 2021. "Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China." Ecological Indicators 128, no. : 107845.

Journal article
Published: 25 May 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Soil moisture is a key parameter affecting crop growth. Gaofen-3 (GF-3) satellite is the first C-band synthetic aperture radar (SAR) produced by China, which provides full-polarization data sources for soil moisture estimation. This paper evaluated the potential of estimating soil moisture via GF-3 SAR over agricultural area using different polarimetric decomposition models, namely, the Modified Freeman-Durden Model (MFDM), the An Model (AM) and the Freeman-Durden Model (FDM). Among them, the MFDM is the first attempt to be used for soil moisture retrieval. After removing the volume scattering, the surface and dihedral scattering component were used complementarily to estimate soil moisture. The results show the performance of each polarimetric decomposition models for soil moisture estimation depends on the crop type, crop growth stages and soil moisture conditions. Soil moisture retrievals exhibit an overall underestimation with a root mean square error of 8-11vol. %. This is mainly because of the random orientation assumption in the volume scattering model, which cannot accurately describe the variability of the crop structure. Due to the application of de-orientation process and power constraint, the MFDM shows the best performance both for corn and wheat, with inversion rates of 39%-45%.

ACS Style

Linlin Zhang; Qingyan Meng; Jiangyuan Zeng; Xiangqin Wei; Hongtao Shi. Evaluation of Gaofen-3 C-Band SAR for Soil Moisture Retrieval Using Different Polarimetric Decomposition Models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 5707 -5719.

AMA Style

Linlin Zhang, Qingyan Meng, Jiangyuan Zeng, Xiangqin Wei, Hongtao Shi. Evaluation of Gaofen-3 C-Band SAR for Soil Moisture Retrieval Using Different Polarimetric Decomposition Models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):5707-5719.

Chicago/Turabian Style

Linlin Zhang; Qingyan Meng; Jiangyuan Zeng; Xiangqin Wei; Hongtao Shi. 2021. "Evaluation of Gaofen-3 C-Band SAR for Soil Moisture Retrieval Using Different Polarimetric Decomposition Models." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 5707-5719.

Journal article
Published: 26 February 2021 in Remote Sensing
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On 3 August 2014, an Mw 6.2 earthquake occurred in Ludian, Yunnan Province, China (27.245° N 103.427° E). This damaging earthquake caused approximately 400 fatalities, 1800 injuries, and the destruction of at least 12,000 houses. Using air temperature data of the National Center for Environmental Prediction (NCEP) and the tidal force fluctuant analysis (TFFA) method, we derive the temperature variations in multiple air layers between before and after the Ludian earthquake. In the spatial range of 30° × 30° (12°–42° N, 88°–118° E) of China, a thermal anomaly appeared only on or near the epicenter before earthquake, and air was heated from the land, then uplifted by a heat flux, and then cooled and dissipated upon rising. With the approaching earthquake, the duration and range of the thermal anomaly during each tidal cycle was found to increase, and the amplitude of the thermal anomaly varied with the tidal force potential: air temperature was found to rise during the negative phase of the tidal force potential, to reach peak at its trough, and to attenuate when the tidal force potential was rising again. A significance test supports the hypothesis that the thermal anomalies are physically related to Ludian earthquakes rather than being coincidences. Based on these results, we argue that the change of air temperature could reflect the stress changes modulated under the tidal force. Moreover, unlike the thermal infrared remote sensing data, the air temperature data provided by NCEP are not affected by clouds, so it has a clear advantage for monitoring the pre-earthquake temperature variation in cloudy areas.

ACS Style

Ying Zhang; Qingyan Meng; Zian Wang; Xian Lu; Die Hu. Temperature Variations in Multiple Air Layers before the Mw 6.2 2014 Ludian Earthquake, Yunnan, China. Remote Sensing 2021, 13, 884 .

AMA Style

Ying Zhang, Qingyan Meng, Zian Wang, Xian Lu, Die Hu. Temperature Variations in Multiple Air Layers before the Mw 6.2 2014 Ludian Earthquake, Yunnan, China. Remote Sensing. 2021; 13 (5):884.

Chicago/Turabian Style

Ying Zhang; Qingyan Meng; Zian Wang; Xian Lu; Die Hu. 2021. "Temperature Variations in Multiple Air Layers before the Mw 6.2 2014 Ludian Earthquake, Yunnan, China." Remote Sensing 13, no. 5: 884.

Research article
Published: 08 October 2020 in Journal of the Indian Society of Remote Sensing
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A number of studies have shown that urban green space (UGS) can improve the health and well-being of urban residents. However, the commonly used UGS measurement method has its obvious limitations, such as abrupt changes of measurement results in adjacent sites and complex operation process. This paper proposed a new approach to measure surrounding greenness based on moving window method, and identified its characteristics and applicability by comparison with the grid method and the buffer method in the core area of Székesfehérvár, Hungary. Spatial distribution, average, standard deviation and correlation were compared between the three kinds of measurement results. In general, all above three methods could describe the variety of surrounding greenness in areas with different building density, and the result of moving window method was more similar to the result of grid method. The descending order of the probability of contacting surrounding greenness is residential area, research area and commercial area. Statistically significant associations were found between the measurement results of the moving window method and the grid method as well as the moving window method and the buffer method (both R2 > 0.87). Moreover, moving window method was well suited for measuring surrounding greenness because of its ability to describe spatial distribution in more details.

ACS Style

Qingyan Meng; Yunxiao Sun; Xu Chen; Juan Li; Xuemiao Wang; Jun Wu. Moving Window Method: An Effective Approach to Measure Surrounding Greenness. Journal of the Indian Society of Remote Sensing 2020, 48, 1729 -1738.

AMA Style

Qingyan Meng, Yunxiao Sun, Xu Chen, Juan Li, Xuemiao Wang, Jun Wu. Moving Window Method: An Effective Approach to Measure Surrounding Greenness. Journal of the Indian Society of Remote Sensing. 2020; 48 (12):1729-1738.

Chicago/Turabian Style

Qingyan Meng; Yunxiao Sun; Xu Chen; Juan Li; Xuemiao Wang; Jun Wu. 2020. "Moving Window Method: An Effective Approach to Measure Surrounding Greenness." Journal of the Indian Society of Remote Sensing 48, no. 12: 1729-1738.

Journal article
Published: 15 September 2020 in Remote Sensing
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The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

ACS Style

Maofan Zhao; Qingyan Meng; Linlin Zhang; Die Hu; Ying Zhang; Mona Allam. A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images. Remote Sensing 2020, 12, 3005 .

AMA Style

Maofan Zhao, Qingyan Meng, Linlin Zhang, Die Hu, Ying Zhang, Mona Allam. A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images. Remote Sensing. 2020; 12 (18):3005.

Chicago/Turabian Style

Maofan Zhao; Qingyan Meng; Linlin Zhang; Die Hu; Ying Zhang; Mona Allam. 2020. "A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images." Remote Sensing 12, no. 18: 3005.

Journal article
Published: 06 August 2020 in Agricultural Water Management
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Actual and potential evapotranspiration (ET) are important variables for regional and global environmental modelling. These elements provide better understanding and management of the hydrological cycle, particularly in relation to several environmental stresses affecting ecosystems. With the advancement of airborne techniques, remote sensing approaches have enabled an accurate estimation of crop actual ET with limited field visits. In this study, an enhanced version (i.e. m-SEBALI) of the improved surface energy balance model for land, surnamed SEBALI, will be presented. Main improvements concern the retrieval of 10-m ET values using Sentinel-2 images and MODIS Terra LST 1-km datasets. The other enhancement considers the usage of monthly-only climatic datasets, instead of the required hourly, daily and monthly data in SEBALI. Calibrations and validations were made in China, Belgium, Germany, Lebanon and Italy between 2013 and 2017, yielding adequate RSME (i.e. 20.06 mm/month) and AME (i.e. 15.69 mm/month) values. These countries represent four different climatic regions (i.e. continental semi-arid, Monsoon-influenced warm-summer humid continental, Mediterranean hot summer and Oceanic climates). Thus, Landast-8 thermal bands will be unnecessary to implement SEBALI. Also, the conversion between hourly and daily climatic data and monthly climatic datasets in SEBALI showed a Pearson value of 93.3 %. Thus, seven of the required inputs in SEBALI were eliminated in m-SEBALI, saving on time and resources. Furthermore, the wheat seasonal trend is produced in the Bekaa valley, Lebanon, between November 2018 and July 2019, showing an average ET value of 620 mm in a region with 510 mm of precipitations during the same period. Two peaks were visible in January and May signaling the different wheat phenological stages. The importance of m-SEBALI lies in providing an automated retrieval of 10-m ET, biomass production and water productivity (WP), among other variables, in diverse climatic regions. Such improvements shall improve the assessment of ET and WP towards better management of water resources, particularly in regions lacking some required inputs.

ACS Style

Mona Allam; Mario Mhawej; Qingyan Meng; Ghaleb Faour; Yaser Abunnasr; Ali Fadel; Hu Xinli. Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data. Agricultural Water Management 2020, 243, 106432 .

AMA Style

Mona Allam, Mario Mhawej, Qingyan Meng, Ghaleb Faour, Yaser Abunnasr, Ali Fadel, Hu Xinli. Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data. Agricultural Water Management. 2020; 243 ():106432.

Chicago/Turabian Style

Mona Allam; Mario Mhawej; Qingyan Meng; Ghaleb Faour; Yaser Abunnasr; Ali Fadel; Hu Xinli. 2020. "Monthly 10-m evapotranspiration rates retrieved by SEBALI with Sentinel-2 and MODIS LST data." Agricultural Water Management 243, no. : 106432.

Research article
Published: 30 June 2020 in International Journal of Remote Sensing
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The Bayesian Maximum Entropy (BME) algorithm that can model larger-scale spatial heterogeneity and integrate multiple types of data is a better spatial estimation algorithm in avoiding the circular problem and improving the estimation accuracy of parameters than original empirical geostatistic and spatial statistic methods. To obtain higher-resolution and higher-accuracy soil moisture (SM) data, this study used the BME algorithm to integrate the multiple environmental factor variables related to SM as auxiliary data (11 data sets in total) such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Albedo products with 1 kilometre (km) grid resolution from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) satellite data, and the Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, Relief amplitude products generated from ASTER GDEM data. First, the FengYun 3-B satellite (FY3-B) SM product with 25 km grid resolution was downscaled to 1 km grid resolution using the visible, infrared (IR), and microwave fusion method. The downscaled FY3-B SM data and auxiliary data were used to generate the weighted probability soft data (SD) under four cases, case 1: 500 sample points of SD, case 2: 450 sample points of SD, case 3: 400 sample points of SD, and case 4: 350 sample points of SD. Particularly, we used two methods, the multivariable correlation analysis method and principal component analysis (PCA) method to get the weight values of environmental factor variables, i.e. NDVI, LST, Albedo, the Digital Elevation Model (DEM), Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, and Relief amplitude with SM. Then, the in-situ SM measurements as hard data (HD) were used to calibrate the weighted probability SD in the procedure of BME SM estimation. The SM under four cases were estimated from the weighted probability SD and HD using BME algorithm. Finally, comparisons of the estimated SM using BME algorithm with in-situ SM measurements at maize study area were carried out in this study. Our results indicated that the accuracy of the estimated SM using BME algorithm, the root-mean-square error (RMSE) = 0.049 cm3 cm−3, the correlation coefficient (r) = 0.639, the unbiased RMSE (RMSEu) = 0.047 cm3 cm−3, and Bias = 0.002 cm3 cm−3, under case 1 based on PCA method was obviously better than the downscaled FY3-B SM (RMSE = 0.079 cm3 cm−3, r = 0.096, RMSEu = 0.069 cm3 cm−3, and Bias = 0.039 cm3 cm−3) that was generated by the visible, IR, and microwave fusion method. We concluded that integrating auxiliary data into SM estimation using BME algorithm could further improve the downscaled FY3-B SM that was generated by visible, IR, and microwave fusion method.

ACS Style

Chunmei Wang; Qiuxia Xie; Xingfa Gu; Tao Yu; Qingyan Meng; Xiang Zhou; Leran Han; Yulin Zhan. Soil moisture estimation using Bayesian Maximum Entropy algorithm from FY3-B, MODIS and ASTER GDEM remote-sensing data in a maize region of HeBei province, China. International Journal of Remote Sensing 2020, 41, 7018 -7041.

AMA Style

Chunmei Wang, Qiuxia Xie, Xingfa Gu, Tao Yu, Qingyan Meng, Xiang Zhou, Leran Han, Yulin Zhan. Soil moisture estimation using Bayesian Maximum Entropy algorithm from FY3-B, MODIS and ASTER GDEM remote-sensing data in a maize region of HeBei province, China. International Journal of Remote Sensing. 2020; 41 (18):7018-7041.

Chicago/Turabian Style

Chunmei Wang; Qiuxia Xie; Xingfa Gu; Tao Yu; Qingyan Meng; Xiang Zhou; Leran Han; Yulin Zhan. 2020. "Soil moisture estimation using Bayesian Maximum Entropy algorithm from FY3-B, MODIS and ASTER GDEM remote-sensing data in a maize region of HeBei province, China." International Journal of Remote Sensing 41, no. 18: 7018-7041.

Journal article
Published: 27 May 2020 in Applied Sciences
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Nowadays, space-borne imaging spectro-radiometers are exploited for many environmental applications, including water quality monitoring. Turbidity is a standout amongst the essential parameters of water quality that affect productivity. The current study aims to utilize Landsat 8 surface reflectance (L8SR) to retrieve turbidity in the Ramganga River, a tributary of the Ganges River. Samples of river water were collected from 16 different locations on 13 March and 27 November 2014. L8SR images from6 March and 17 November 2014 were downloaded from the United States Geological Survey (USGS) website. The algorithm to retrieve turbidity is based on the correlation between L8SRreflectance (single and ratio bands) and insitu data. The b2/b4 and b2/b3 bands ratio are proven to be the best predictors of turbidity, with R2 = 0.560 (p < 0.05) and R2 = 0.726 (p < 0.05) for March and November, respectively. Selected models are validated by comparing the concentrations of predicted and measured turbidity. The results showed that L8SR is a promising tool for monitoring surface water from space, even in relatively narrow river channels, such as the Ramganga River.

ACS Style

Mona Allam; Mohd Yawar Ali Khan; Qingyan Meng. Retrieval of Turbidity on a Spatio-Temporal Scale Using Landsat 8 SR: A Case Study of the Ramganga River in the Ganges Basin, India. Applied Sciences 2020, 10, 3702 .

AMA Style

Mona Allam, Mohd Yawar Ali Khan, Qingyan Meng. Retrieval of Turbidity on a Spatio-Temporal Scale Using Landsat 8 SR: A Case Study of the Ramganga River in the Ganges Basin, India. Applied Sciences. 2020; 10 (11):3702.

Chicago/Turabian Style

Mona Allam; Mohd Yawar Ali Khan; Qingyan Meng. 2020. "Retrieval of Turbidity on a Spatio-Temporal Scale Using Landsat 8 SR: A Case Study of the Ramganga River in the Ganges Basin, India." Applied Sciences 10, no. 11: 3702.

Journal article
Published: 01 May 2020 in Land
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Monitoring of improper soil erosion empowered by water is constantly adding more risk to the natural resource mitigation scenarios, especially in developing countries. The demographical pattern and the rate of growth, in addition to the impairments of the rainfall pattern, are consequently disposed to adverse environmental disturbances. The current research goal is to evaluate soil erosion triggered by water in the coastal area of Kenya on the district level, and also in protected areas. The Revised Universal Soil Loss Equation (RUSLE) model was exercised to estimate the soil loss in the designated study area. RUSLE input parameters were functionally realized in terms of rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and gradient factor (LS), land cover management factor (C) and slope factor (P). The realization of RUSLE input parameters was carried out using different dataset sources, including meteorological data, soil/geology maps, the Digital Elevation Model (DEM) and processing of satellite imagery. Out of 26 districts in coastal area, eight districts were projected to have mean annual soil loss rates of >10 t·ha−1·y−1: Kololenli (19.709 t·ha−1·y−1), Kubo (14.36 t·ha−1·y−1), Matuga (19.32 t·ha−1·y−1), Changamwe (26.7 t·ha−1·y−1), Kisauni (16.23 t·ha−1·y−1), Likoni (27.9 t·ha−1·y−1), Mwatate (15.9 t·ha−1·y−1) and Wundanyi (26.51 t·ha−1·y−1). Out of 34 protected areas at the coastal areas, only four were projected to have high soil loss estimation rates >10 t·ha−1·y−1: Taita Hills (11.12 t·ha−1·y−1), Gonja (18.52 t·ha−1·y−1), Mailuganji (13.75.74 t·ha−1·y−1), and Shimba Hills (15.06 t·ha−1·y−1). In order to mitigate soil erosion in Kenya’s coastal areas, it is crucial to regulate the anthropogenic disturbances embedded mainly in deforestation of the timberlands, in addition to the natural deforestation process caused by the wildfires.

ACS Style

Yves Hategekimana; Mona Allam; Qingyan Meng; Yueping Nie; Elhag Mohamed. Quantification of Soil Losses along the Coastal Protected Areas in Kenya. Land 2020, 9, 137 .

AMA Style

Yves Hategekimana, Mona Allam, Qingyan Meng, Yueping Nie, Elhag Mohamed. Quantification of Soil Losses along the Coastal Protected Areas in Kenya. Land. 2020; 9 (5):137.

Chicago/Turabian Style

Yves Hategekimana; Mona Allam; Qingyan Meng; Yueping Nie; Elhag Mohamed. 2020. "Quantification of Soil Losses along the Coastal Protected Areas in Kenya." Land 9, no. 5: 137.

Articles
Published: 24 December 2019 in International Journal of Remote Sensing
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Soil dielectric model is an essential part of the microwave soil moisture retrieval process. This study compared the performance of four widely used dielectric models, the Wang-Schmugge model (WS), Dobson model, generalized refractive mixing dielectric model (GRMDM), and multi-relaxation generalized refractive mixing dielectric model (MRGRMDM), and investigated the effects of the uncertainties of each model on soil moisture retrievals. Furthermore, the simulated soil dielectric constants were evaluated by measured dielectric data at the P/L/C/X bands. The results showed that the uncertainties induced in soil moisture retrievals by an alternative dielectric model exceeded 0.09 m3 m−3 in the worst case. The Dobson model is sensitive to the sand content. WS, GRMDM, and MRGRMDM model are sensitive to the clay content. The measured dielectric data further verified that the applicability of each dielectric model depends on the soil texture type and soil moisture condition. Compared with Dobson model, WS showed better performance at dry soil. GRMDM and MRGRMDM provided better results under lower clay content soil. Especially, MRGRMDM has better simulation accuracy than GRMDM in the low-frequency range (< 1 GHz).

ACS Style

Linlin Zhang; Qingyan Meng; Die Hu; Ying Zhang; Shun Yao; Xu Chen. Comparison of different soil dielectric models for microwave soil moisture retrievals. International Journal of Remote Sensing 2019, 41, 3054 -3069.

AMA Style

Linlin Zhang, Qingyan Meng, Die Hu, Ying Zhang, Shun Yao, Xu Chen. Comparison of different soil dielectric models for microwave soil moisture retrievals. International Journal of Remote Sensing. 2019; 41 (8):3054-3069.

Chicago/Turabian Style

Linlin Zhang; Qingyan Meng; Die Hu; Ying Zhang; Shun Yao; Xu Chen. 2019. "Comparison of different soil dielectric models for microwave soil moisture retrievals." International Journal of Remote Sensing 41, no. 8: 3054-3069.

Journal article
Published: 04 December 2019 in Forests
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Urban street-side greenery, as an indispensable element of urban green spaces, is beneficial to residents’ physical and mental health. As readily available internet data, street view images have been widely used in urban green spaces research. While the relevant research using multiple images from different directions at a sampling point, researchers need to calculate the index of visible vegetation cover for many times. However, one Baidu panoramic street view image can cover the 360° view similar to that of a pedestrian. In this study, we selected 9644 points at 50-m intervals along the street lines in the central district of Sanya city, China, and acquired panoramic images via the Baidu application programming interface (API). The sky pixels were detected within the Baidu panoramic street view images using a proposed reflectance indicator. The green vegetation was extracted according to the Back Propagation (BP) neural-network method. Our proposed method was validated by comparing the results of the manual recognition and PSPNet method, and the accuracy met the requirements of the study. The Panoramic Green View Index (PGVI) was proposed to quantitatively evaluate greenery around streets. The authors found that the highest frequency value in the distribution was 0.075, which accounted for 32% of the total sample points, and the average PGVI value in this study area was low; the PGVI values between different roads varied greatly, and primary roads tended to have higher PGVI values than other roads. This case study proved that the PGVI is well suited for evaluating greenery around streets. We suggest that the PGVI derived from Baidu panoramic street view images may be a useful tool for city managers to support urban green spaces planning and management.

ACS Style

Xu Chen; Qingyan Meng; Die Hu; Linlin Zhang; Jian Yang. Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index. Forests 2019, 10, 1109 .

AMA Style

Xu Chen, Qingyan Meng, Die Hu, Linlin Zhang, Jian Yang. Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index. Forests. 2019; 10 (12):1109.

Chicago/Turabian Style

Xu Chen; Qingyan Meng; Die Hu; Linlin Zhang; Jian Yang. 2019. "Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index." Forests 10, no. 12: 1109.

Journal article
Published: 29 November 2019 in Science of The Total Environment
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Revealing the dominant driving factors of land surface temperature (LST) plays an important role in mitigating the urban heat island (UHI) effect. Numerous international metropolises are developing polycentric forms under the process of suburbanization in conjunction with rapid urbanization, generating new UHI spatial patterns in internal urban areas. To comprehensively understand the effects of multi-factors on the thermal environment, our study examined a typical polycentric city, Tianjin. According to the concept of polycentrism, this study focused on three types of city “centers”: major city core, new district core and industrial park. Eleven potential driving factors of LST were explored from four layers, and the geo-detector model was applied to rank the explanatory degree of these factors on LST. Three different city centers of the polycentric city showed varied UHI spatial pattern characteristics, and their response to the effect of natural factors and social factors on LST were quite diverse. Heat island areas were distributed homogeneously in the major city core; the UHI pattern on the east-west axis was unbalanced in the new district core due to the unsaturated urban space and dynamic planning policies; in industrial park, production areas were segregated by green belts with clear boundaries. For the whole city and the major city core, the imperviousness factor had the highest explanatory rate for LST, followed by the greenness factor. In contrast to the results of previous studies, the wetness factors had a greater impact on LST in the new district core and industrial park, second only to the greenness factor. Furthermore, selected factors exhibited bilinear or nonlinear enhanced relationships in their interactions. The driving laws of LST in different city centers were summarized with an explorative case study, aimed at providing theoretical basis and practical guidance for optimizing urban thermal environment planning, especially for highly urbanized polycentric cities.

ACS Style

Die Hu; Qingyan Meng; Linlin Zhang; Ying Zhang. Spatial quantitative analysis of the potential driving factors of land surface temperature in different “Centers” of polycentric cities: A case study in Tianjin, China. Science of The Total Environment 2019, 706, 135244 .

AMA Style

Die Hu, Qingyan Meng, Linlin Zhang, Ying Zhang. Spatial quantitative analysis of the potential driving factors of land surface temperature in different “Centers” of polycentric cities: A case study in Tianjin, China. Science of The Total Environment. 2019; 706 ():135244.

Chicago/Turabian Style

Die Hu; Qingyan Meng; Linlin Zhang; Ying Zhang. 2019. "Spatial quantitative analysis of the potential driving factors of land surface temperature in different “Centers” of polycentric cities: A case study in Tianjin, China." Science of The Total Environment 706, no. : 135244.

Research article
Published: 15 March 2019 in Natural Hazards and Earth System Sciences
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Research in the field of earthquake prediction has a long history, but the inadequacies of traditional approaches to the study of seismic threats have become increasingly evident. Remote sensing and Earth observation technology, an emerging method that can rapidly capture information concerning anomalies associated with seismic activity across a wide geographic area, has for some time been believed to be the key to overcoming the bottleneck in earthquake prediction studies. However, a multi-parametric method appears to be the most promising approach for increasing the reliability and precision of short-term seismic hazard forecasting, and thermal infrared (TIR) anomalies are important earthquake precursors. While several studies have investigated the correlation among TIR anomalies identified by the robust satellite techniques (RSTs) methodology and single earthquakes, few studies have extracted TIR anomalies over a long period within a large study area. Moreover, statistical analyses are required to determine whether TIR anomalies are precursors to earthquakes. In this paper, RST data analysis and the Robust Estimator of TIR Anomalies (RETIRA) index were used to extract the TIR anomalies from 2002 to 2018 in the Sichuan region using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, while the earthquake catalog was used to ascertain the correlation between TIR anomalies and earthquake occurrences. Most TIR anomalies corresponded to earthquakes, and statistical methods were used to verify the correlation between the extracted TIR anomalies and earthquakes. This is the first time that the ability to predict earthquakes has been evaluated based on the positive predictive value (PPV), false discovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR). The statistical results indicate that the prediction potential of RSTs with use of MODIS is limited with regard to the Sichuan region.

ACS Style

Ying Zhang; Qingyan Meng. A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data. Natural Hazards and Earth System Sciences 2019, 19, 535 -549.

AMA Style

Ying Zhang, Qingyan Meng. A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data. Natural Hazards and Earth System Sciences. 2019; 19 (3):535-549.

Chicago/Turabian Style

Ying Zhang; Qingyan Meng. 2019. "A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data." Natural Hazards and Earth System Sciences 19, no. 3: 535-549.

Articles
Published: 07 February 2019 in Geocarto International
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The WorldView-2 high spatial resolution satellite with eight multispectral imaging bands is ideally suited for extracting BUs from remote sensing images. In this study, an object-based automatic multi-index BUs extraction method was developed. First, several indices, including built-up areas extraction index (NBEIr-c), vegetation extraction index(NDVInir2-r) and water extraction index (NDWI b-nir1), were developed to obtain the BUs, vegetation and water maps, and then the fractional-order Darwinian particle swarm optimization (FODPSO) algorithm was employed to automatically segment the multi-index images and obtained BUs, water, vegetation and BS information. Finally, the extracted BUs results were optimized via an object-based analysis method and the results were compared with those of two other relevant indices, which confirmed the proposed indices had a higher accuracy and exhibited higher performance when separating the BS from the BUs.

ACS Style

Zhenhui Sun; Qingyan Meng. Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery. Geocarto International 2019, 35, 801 -817.

AMA Style

Zhenhui Sun, Qingyan Meng. Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery. Geocarto International. 2019; 35 (8):801-817.

Chicago/Turabian Style

Zhenhui Sun; Qingyan Meng. 2019. "Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery." Geocarto International 35, no. 8: 801-817.

Journal article
Published: 22 November 2018 in Remote Sensing
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Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy.

ACS Style

Zhenhui Sun; Qingyan Meng; Weifeng Zhai. An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images. Remote Sensing 2018, 10, 1863 .

AMA Style

Zhenhui Sun, Qingyan Meng, Weifeng Zhai. An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images. Remote Sensing. 2018; 10 (12):1863.

Chicago/Turabian Style

Zhenhui Sun; Qingyan Meng; Weifeng Zhai. 2018. "An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images." Remote Sensing 10, no. 12: 1863.

Research article
Published: 19 August 2018 in Advances in Meteorology
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Soil moisture (SM) plays important roles in surface energy conversion, crop growth, environmental protection, and drought monitoring. As crops grow, the associated vegetation seriously affects the ability of satellites to retrieve SM data. Here, we collected such data at different growth stages of maize using Bragg and X-Bragg scattering models based on the Freeman–Durden polarization decomposition method. We used the H/A/Alpha polarization decomposition approach to extract accurate threshold values of decomposed scattering components. The results showed that the H and Alpha values of bare soil areas were lower and those of vegetated areas were higher. The threshold values of the three scattering components were 0.2–0.4 H and 7–24° Alpha for the surface scattering component, 0.6–0.9 H and 22–50° Alpha for the volume scattering component, and other values for the dihedral scattering component. The SM data retrieved (using the X-Bragg model) on June 27, 2014, were better than those retrieved at other maize growth stages and were thus associated with the minimum root-mean-square error value (0.028). The satellite-evaluated SM contents were in broad agreement with data measured in situ. Our algorithm thus improves the accuracy of SM data retrieval from synthetic-aperture radar (SAR) images.

ACS Style

Qiuxia Xie; Qingyan Meng; Linlin Zhang; Chunmei Wang; Qiao Wang; Shaohua Zhao. Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data. Advances in Meteorology 2018, 2018, 1 -17.

AMA Style

Qiuxia Xie, Qingyan Meng, Linlin Zhang, Chunmei Wang, Qiao Wang, Shaohua Zhao. Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data. Advances in Meteorology. 2018; 2018 ():1-17.

Chicago/Turabian Style

Qiuxia Xie; Qingyan Meng; Linlin Zhang; Chunmei Wang; Qiao Wang; Shaohua Zhao. 2018. "Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data." Advances in Meteorology 2018, no. : 1-17.

Research article
Published: 15 August 2018 in Advances in Meteorology
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Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

ACS Style

Qingyan Meng; Linlin Zhang; Qiuxia Xie; Shun Yao; Xu Chen; Ying Zhang. Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology 2018, 2018, 1 -11.

AMA Style

Qingyan Meng, Linlin Zhang, Qiuxia Xie, Shun Yao, Xu Chen, Ying Zhang. Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology. 2018; 2018 ():1-11.

Chicago/Turabian Style

Qingyan Meng; Linlin Zhang; Qiuxia Xie; Shun Yao; Xu Chen; Ying Zhang. 2018. "Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network." Advances in Meteorology 2018, no. : 1-11.

Journal article
Published: 14 August 2018 in Sensors
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Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.

ACS Style

Linlin Zhang; Qingyan Meng; Shun Yao; Qiao Wang; Jiangyuan Zeng; Shaohua Zhao; Jianwei Ma. Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields. Sensors 2018, 18, 2675 .

AMA Style

Linlin Zhang, Qingyan Meng, Shun Yao, Qiao Wang, Jiangyuan Zeng, Shaohua Zhao, Jianwei Ma. Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields. Sensors. 2018; 18 (8):2675.

Chicago/Turabian Style

Linlin Zhang; Qingyan Meng; Shun Yao; Qiao Wang; Jiangyuan Zeng; Shaohua Zhao; Jianwei Ma. 2018. "Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields." Sensors 18, no. 8: 2675.